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Free Download Predictive Customer Analytics (2024) Published 10/2024 Created by Start-Tech Academy MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 29 Lectures ( 3h 23m ) | Size: 1.68 GB Build predictive machine learning and forecasting models in Excel to build customer decision and customer behavior What you'll learn Discover how to preprocess customer data for predictive modeling using Excel. Master the application of linear regression in Excel to predict customer behavior. Explore the use of logistic regression for customer churn prediction and retention strategies. Analyze customer data using clustering techniques to segment customer groups. Build sales forecasting models using Excel's Solver and time series analysis. Implement XLSTAT for advanced statistical analysis in customer predictions. Develop and run logistic regression models using Excel Macros for automation. Predict future customer behavior with additive and multiplicative time series models. Interpret the results of regression and clustering models to make actionable business decisions. Evaluate the effectiveness of your predictive models in improving customer retention and business strategies. Requirements A PC/ laptop with good internet connection and MS Excel installed on it Description Are you an aspiring data analyst or business professional looking to make data-driven decisions that impact customer behavior and retention? Do you want to leverage Excel to build predictive models without the complexity of advanced coding? If yes, this course is for you.In today's competitive market, understanding customer behavior is key to business success. Predictive Customer Analytics helps you stay ahead by forecasting customer decisions, improving retention, and driving targeted marketing strategies. This course will empower you to use Excel as a powerful tool for building predictive machine learning models and forecasting techniques, even if you're not an expert in data science.In this course, you will:Develop a solid understanding of linear and logistic regression techniques in Excel to predict customer behavior.Master clustering techniques for customer segmentation, identifying key groups within your customer base.Build sales forecasting models using Excel's Solver and time series methods.Implement real-world solutions with case studies, such as predicting customer churn and segmenting customers for better marketing strategies.Why is Predictive Customer Analytics so important? By using Excel, a tool most professionals are already familiar with, you can unlock deeper insights into customer data, enabling better decision-making without needing advanced technical skills. From forecasting sales trends to retaining key customers, predictive analytics is a game-changer for businesses looking to grow and scale.Throughout the course, you will complete hands-on exercises in Excel, including:Preprocessing customer data for linear and logistic regressionBuilding predictive models using XLSTAT and Excel MacrosClustering customer data for segmentation analysisImplementing time series forecasting to predict salesWhat sets this course apart is its focus on practical, easy-to-implement techniques that don't require programming knowledge. You'll learn how to utilize Excel's advanced features to get accurate, actionable results quickly.Ready to transform your customer insights? Enroll today and start building your own predictive models in Excel! Who this course is for Marketing professionals who want to use data to predict customer behavior and enhance targeted campaigns. Sales managers looking to forecast sales trends and improve customer retention strategies. Data analysts who want to build predictive models in Excel without needing complex coding skills. Small business owners aiming to make data-driven decisions to optimize customer acquisition and retention. Homepage https://www.udemy.com/course/predictive-customer-analytics/ Screenshot Rapidgator https://rg.to/file/22f5ee0eb74995ff51792cdfb7a2082e/hmaze.Predictive.Customer.Analytics.2024.part1.rar.html https://rg.to/file/34e18dce1ed2acff4d9463783b285b44/hmaze.Predictive.Customer.Analytics.2024.part2.rar.html Fikper Free Download https://fikper.com/7QwAECQ5io/hmaze.Predictive.Customer.Analytics.2024.part2.rar.html https://fikper.com/Q16PHN9U1L/hmaze.Predictive.Customer.Analytics.2024.part1.rar.html No Password - Links are Interchangeable

Free Download Marketing Analytics in Action Drive Growth and Communication with Data Insights Released: 10/2024 Duration: 28m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 64 MB Level: Intermediate | Genre: eLearning | Language: English In this course, Chris DallaVilla, a key opinion leader in the space, shows you how to harness the power of marketing data analytics and AI to uncover valuable insights and fuel business growth. Learn how to leverage the latest AI tools and techniques to streamline your marketing analytics processes, so that you can make faster, more informed decisions. Gain the skills and confidence to turn data into compelling narratives that inspire action, communication, and growth. Whether you're a seasoned marketer or new to the field, this course will empower you to get the most from your data, connect and communicate with others, unlock creativity, and take your marketing efforts to the next level. Homepage https://www.linkedin.com/learning/marketing-analytics-in-action-drive-growth-and-communication-with-data-insights Rapidgator https://rg.to/file/f3b2260e19283b1716b8c8a210f23ac2/aakvn.Marketing.Analytics.in.Action.Drive.Growth.and.Communication.with.Data.Insights.rar.html Fikper Free Download https://fikper.com/fOx6f3RFoG/aakvn.Marketing.Analytics.in.Action.Drive.Growth.and.Communication.with.Data.Insights.rar.html No Password - Links are Interchangeable

Free Download Linkedin - Adobe Customer Journey Analytics Released 10/2024 With Eric Matisoff MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 30m 58s | Size: 372 MB Learn how to use Adobe Customer Journey Analytics to access, analyze, and refine omnichannel data. Course details This course delivers a detailed review of the Adobe Customer Journey Analytics platform. Eric Matisoff explains the unique aspects of Customer Journey Analytics compared to Adobe Analytics, as well as how to apply features for your omnichannel and digital analytics needs. Plus, Eric guides you on how to analyze data using Adobe Product Analytics. Finally, learn several administrative capabilities that are key differentiators of Customer Journey Analytics. Homepage https://www.linkedin.com/learning/adobe-customer-journey-analytics Screenshot Rapidgator https://rg.to/file/1b04abd35ae602314d0251017240e852/iqywc.Adobe.Customer.Journey.Analytics.rar.html Fikper Free Download https://fikper.com/hHzvPcJYtE/iqywc.Adobe.Customer.Journey.Analytics.rar.html No Password - Links are Interchangeable

Free Download Introduction To Business Analytics (2024) Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 797.98 MB | Duration: 0h 53m Gain Insight Into Workplace Efficiency, Employee Performance, Advertising ROI And Future Trends For Business Advantage What you'll learn Understand the current roles and applications of business analytics. Recall concepts of business analytics and its wider importance for professional success. Spot different types and sources of data, and how to collect them properly. Visualise data to unlock insightful and meaningful information. Understand when different visualisation tools are appropriate given the source, type and context of the data provided. Implement predictive analytics correctly, from method through to validation. Apply time-series data in a meaningful way to spot trends and patterns. Gain a stronger appreciation of the future of business analytics, and what each trend may mean for your company. Requirements No specific requirements. Description Are you looking to increase your business' profits and ROI using cutting-edge analytics tools? Do you want to better understand the basics of data science, and how to implement those techniques in the real world? Would you like to maximise workplace performance, marketing strategy and overall efficiency, but aren't sure where to start? If you answered yes to any of these questions, then this course is for you! Data is at the heart of every company's success, so it is essential you know the basic analytical concepts and how to apply them correctly. From predicting forecasting to heat mapping, there are endless ways to make use of data for business performance. Analytics can help drive decision-making, quantify risk and even improve task efficiency. First, however, we need to focus on the fundamentals before we can utilise data's endless possibilities. Considering that 63% of companies have implemented either artificial intelligence or machine learning to enhance their analytical capabilities, it is key you stay up to date! Throughout a series of six short video-based lectures, you will learn to navigate the dynamic landscape of business analytics. We will uncover the current roles and applications driving this field, empowering you to grasp its significance for professional triumph. You'll recall vital business analytics concepts through interactive lessons and explore their broader implications. By delving into data visualisation techniques, you'll unlock insightful and meaningful information hidden within data sets. We will guide you to adeptly identify diverse data types and sources, ensuring proficient and ethical data collection methods. You'll develop a keen sense of discernment in selecting appropriate visualisation tools based on data sources, types, and contextual cues. From predictive analytics methods to validation strategies, you'll gain the skills to effectively implement these tools. Moreover, you'll harness the power of time-series data, deciphering trends and patterns that hold valuable insights. As you progress, you'll cultivate a profound understanding of upcoming trends in business analytics, equipping you to anti[beeep]te their impact on your organisation's future. This course paves the way for a transformative journey toward becoming a proficient and forward-thinking business analytics practitioner. By the end of this course, we are confident you will emerge with a robust skill set in business analytics, equipped to discern, analyse and visualise data effectively, make informed decisions using predictive analytics and possess a clear vision of how to leverage emerging trends for your company's strategic advantage. So, are you ready to push your business into the future and revolutionise your processes? Enrol now to learn more! Overview Section 1: Introduction Lecture 1 Introduction To Business Analytics Section 2: Introduction To Business Analytics Lecture 2 Importance Of Analytics In Decision Making And Key Terminologies Lecture 3 Foundations Of Business Analytics Lecture 4 Predictive Analytics Lecture 5 Time Series Analysis Lecture 6 Future Trends In Business Analytics Section 3: Conclusion Lecture 7 Conclusion Managers who want to gain extra insight into workplace tasks and employee performance.,Business owners who want to take their company to the next level using novel and unique methods.,Professionals hoping to use future analytical trends to stay ahead of the curve.,Individuals in marketing or UX hoping to use customer-based data to improve advertising and product experience.,Business leaders seeking practical skills in business analytics, regardless of their initial understanding.,Professionals looking to take a more technical approach to business, using case studies and useful information to motivate important decisions within their company. 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Free Download Expanding Data Analytics And Ai In Internal Audit Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.16 GB | Duration: 1h 4m Staying on top of the new technology What you'll learn Understand the Role of Data Analytics in Internal Audit: Gain insights into how data analytics can enhance audit quality, efficiency, and effectiveness by ident Explore AI Applications in Auditing: Learn how AI technologies such as machine learning and natural language processing can be used to automate tasks, improve f Develop Strategies for Integrating Data Analytics and AI: Learn best practices for incorporating data analytics and AI into your existing audit framework, from Assess the Benefits and Challenges of AI-Driven Audits: Understand the opportunities AI provides in enhancing audit capabilities while identifying potential cha Implement Advanced Data Analytics Techniques: Acquire practical knowledge of key data analytics techniques, including predictive analytics and data visualizatio Understand Internal Audit's new role as it comes to implementing AI usage in the organization with emphasis on data privacy. This course is designed to discuss the concept/use of data analytics but it is not a guide on how to use that tool. Requirements There are no prerequisites for this course Description Join us for an insightful webinar focused on transforming internal audit practices through advanced data analytics and AI. As organizations face increasing complexities, internal auditors must evolve to stay ahead of emerging risks. This session will explore critical topics like improving data literacy within audit teams, and how educating senior management and the board can lead to more strategic decision-making.We will also address the growing need for real-time threat detection in a fast-paced digital environment and how AI can enhance risk identification. Learn practical steps to implement data-driven audits that provide deeper insights, increased efficiency, and proactive issue resolution. Whether you're looking to build a more data-centric audit function or improve collaboration with leadership on risk management, this webinar offers actionable guidance and industry best practices. This is not just your usual data analytics course. It looks conceptually, as the new cyber dependent world in which we operate.Don't miss this exciting opportunity to stay at the forefront of internal audit innovation, with expert advice on leveraging data and AI to drive impactful change. Perfect for internal audit professionals, data analysts, and business leaders aiming to elevate their approach to risk and performance monitoring. This session will show you why it so important to stay on top of the advancements in technology and how to use it to your advantage. Overview Section 1: Understanding Data Analytics and its evolution within Auditing Lecture 1 Introduction Section 2: Benefits of Data Analytics Lecture 2 Understanding the Benefits and Taking Inventory Section 3: Preparing for AI Lecture 3 Preparing for AI Section 4: AI and Data Analytics Merge Lecture 4 AI and Data Analytics Merge Section 5: Conclusion Lecture 5 Recap and Wrap Up Internal auditors,Audit Management,IT auditors,CIOs Screenshot Homepage https://www.udemy.com/course/expanding-data-analytics-and-ai-in-internal-audit/ Rapidgator https://rg.to/file/5ddbc7de975a01de748c6456bfd9eeee/tasyw.Expanding.Data.Analytics.And.Ai.In.Internal.Audit.part1.rar.html https://rg.to/file/ceaf762fd59160c9724ea1e901c8eafd/tasyw.Expanding.Data.Analytics.And.Ai.In.Internal.Audit.part2.rar.html Fikper Free Download https://fikper.com/kHWszX5YnB/tasyw.Expanding.Data.Analytics.And.Ai.In.Internal.Audit.part1.rar.html https://fikper.com/oY3pY1MD0l/tasyw.Expanding.Data.Analytics.And.Ai.In.Internal.Audit.part2.rar.html No Password - Links are Interchangeable

Free Download Excel Predictive Analytics, Automation, and AI - No-Code Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 57m | Size: 1.82 GB Microsoft Excel Predictive Analytics, Automation, and AI Tools: The Complete No-Code Guide for Every Professional What you'll learn Master Predictive Analytics Using Excel Students will learn how to use Excel's built-in tools and functions, such as LINEST and TREND Automate Repetitive Tasks with Macros and VBA Learners will gain the skills to create and implement Excel macros and basic VBA to automate repetitive tasks Apply Advanced Forecasting Techniques Students will explore and apply advanced forecasting methods, such as exponential smoothing to predict trends Learners will develop dynamic, interactive dashboards using PowerPivot, Power BI, and Excel's advanced charting tools to present insights effectively. Requirements Basic Understanding of Excel Students should have a basic familiarity with Excel, including knowledge of standard functions like SUM, AVERAGE, and simple cell formatting. Access to Excel Software Learners will need access to Microsoft Excel (preferably version 2021 or Excel 365) to follow along with the exercises and practical applications. No Coding Experience Required No prior programming or coding knowledge is needed, as this course focuses on no-code solutions for predictive analytics and automation using Excel. A Desire to Learn Predictive Analytics While no advanced math or analytics background is required, students should be motivated to explore how data-driven decisions can be made using Excel's tools. Description Here's a course description for Excel Predictive Analytics, Automation, and AI : No-Code.Unlock the full potential of Excel with this comprehensive course designed to take you from the basics of predictive analytics to advanced forecasting, automation, and data visualization-all without writing a single line of code.In this course, you'll learn how to use Excel's powerful tools to analyze data, make accurate predictions, and automate repetitive tasks, allowing you to save time and drive smarter business decisions. From forecasting sales trends to building dynamic dashboards, this course is packed with practical, real-world applications that you can start using immediately.What You'll Learn:Master predictive analytics and forecasting using Excel's built-in functions like LINEST, TREND, and Forecast.ETS.Automate repetitive tasks using Excel macros and VBA to boost efficiency and save time.Create dynamic, interactive dashboards with PowerPivot and Power BI, visualizing key business insights with advanced charts and slicers.Leverage AI tools like ChatGPT to enhance your Excel workflows and improve decision-making.This course is perfect for business professionals, analysts, small business owners, and Excel users looking to take their skills to the next level by integrating automation and data-driven decision-making into their work.No coding required-just a desire to improve your Excel skills and become an expert in predictive analytics and automation! Who this course is for Business Professionals Anyone working in business roles such as finance, marketing, sales, or operations who needs to make data-driven decisions and improve their forecasting capabilities without relying on complex coding. Analysts and Data Enthusiasts People in data analysis roles or those interested in learning practical, no-code methods for performing predictive analytics and creating powerful data visualizations using Excel. Small Business Owners and Entrepreneurs Entrepreneurs looking to optimize their business performance by forecasting sales, automating tasks, and gaining insights from their data using easy-to-understand tools in Excel. Excel Users Seeking Advanced Skills Intermediate Excel users who want to elevate their skills by learning advanced Excel features such as macros, Solver, PowerPivot, and data visualization techniques to boost their productivity and analysis capabilities. Homepage https://www.udemy.com/course/excel-predictive-analytics-automation-and-ai-no-code/ Rapidgator https://rg.to/file/c7dee0a270578da0362ff9186c3fabfe/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part2.rar.html https://rg.to/file/f8a89bb8fb3e8dfd7f976bb03bf97676/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part1.rar.html Fikper Free Download https://fikper.com/7qgOFaU5Es/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part1.rar.html https://fikper.com/Tmxd1AsG1a/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part2.rar.html No Password - Links are Interchangeable

Free Download Excel Analytics - Data Analysis with Pivot-Tables and Charts Last updated 11/2022 Duration: 2h 51m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48kHz, 2ch | Size: 1.63 GB Genre: eLearning | Language: English Learn Data Analysis from A to First Project with Pivot-Tables, Pivot-Charts and Advanced Formulas All In One Course. What you'll learn Write advanced formulas including lookup functions and Dynamic Arrays. Get used to the course content by practicing through the different assignments included in the course. A Full Project guidance step-by-step on a real-life databaset How to analyze any data step by step Understand How spreadsheets can be used as a data analysis tool. Gain an immersive understanding of Data (Defination, Categories, Types and Qualities). Understand the differences between Big Data and Traditional Data. Easy Guidance on how to find free datasets for practice. Obtain Knowledge on Data Clearning (Importance and different methods of Data Cleaning). Obtain Knowledge on Data Manipulation (How to extract information out of a dataset). How to use Pivot-Tables and gain an undestanding of its different features. Learn the different types of charts and how to use them properly. Requirements Microsoft Excel 2014 and Above For Mac Users, You might find a different interface when it comes to Pivot-Table Panel Description Why should you enroll in this course? This course covers all aspects of Excel Analytics starting from downloading a dataset to analyzing and creating a full project. It covers the fundamentals of Data and Quality data to have a good basis before getting into data analysis. It's suitable for beginner and intermediate levels to up their skills to advanced. It covers all business categories therefore, No matter what industry you're working in/want to work in, This course is for you. As a result of this course, you will be able to work on Excel Analytics projects on your own and make money freelancing. On-Video and Off-Video full project, Step-by-Step, in addition to practice tests. All your queries and questions will be answered by the instructor directly. Finally, This course is updated regularly to match the latest updates in regard to data analysis and Excel in particular. What will you learn in this course? 1- Data Data Analysis surely starts with Data, Hence, During Section 1, You will learn What's Data? Starting with its definition up to categories and qualities What are the Data Types in Excel? How to find a dataset? How to Navigate through a dataset? How to Autofil Data in Excel? 2- Data Cleansing Data Cleansing is the process of clearing a dataset of any duplicates, blanks, or inconsistencies and making sure your dataset contains quality data What are the traits of quality data? How to identify duplicates and blanks? Essential excel formulas (TRIM, UPPER, lower, Proper, Substitute, Concatenation) Filter and Sort Features How to change Data Types Text to Column How to Format data as a table? 3- Data Manipulation Data Manipulation is the process of manipulating data to extract relevant and key information from your dataset Statistical Formulas or Functions (Mean, Median, Mode, Min, Max, Average) Data Manipulation formulas (SUM, VLOOKUP, HLOOKUP) Logical Functions (IF, IFS, SUMIF, COUNT, COUNTIF, COUNTIFS, COUNTBLANKS) 4- Analyze Data Stage 4 is analyzing data using Pivot-Tables to gain more insights by creating a pivot table based on different rows/columns/filters and calculations Introduction to Pivot-tables to understand what it is and why it is important How to create a Pivot-Table? How to Format Data in a Pivot-Table? How to apply calculations on data in a Pivot-Table? How to Sort Data in a Pivot-Table? What are Slicers? How to use them and Why they are important? How to create automated recommended Pivot-Tables? 5- Data Visualization Finally Data Visualization, This is the final stage where you get to present your insights and analysis using Charts and Graphs, Charts are important as each Chart Type could identify hidden patterns and trends Introduction to important Chart Types such as (Bar Charts, Column Charts, Line Charts, Combo Charts, Pie Charts, and Doughnut Charts) How to create a Pivot-Chart? How to Apply a Slicer to a Pivot-Chart? How to Manually Style your Pivot-Chart? 6- Final Project (Step-by-Step) In the final section of the course, we will run through a hands-on data analysis project where you will make use of the knowledge you have gained throughout the course. What makes this course different from the other courses? This course is complete and precise, Hence, You won't have to take another course in Excel Analytics. This course teaches you everything about data analysis and guides you through your first data analysis project. Less than 8 hours avg response time (By the instructor) Assessment (First Project) evaluation in less than 24 hours by the instructor It offers auto-generated subtitles in more than 3 languages including (Arabic, Hindi, and Spanish) Regularly updated downloadable resources. What our students say about this course I would say this course is just straight to the point and exactly as described, i would definitely recommend it to any one who looks to start learning data analysis using Excel, Thank you Moustafa! I like it so far very to the point and basic so anyone can catch on. I am doing work to up my career skills and finding this course was exactly what I needed! awesome course. The course materials are easy to follow even for novices and the instructor teaches very well explained. I will recommend this class to whom want to learn data analytics with excel. This course is complete as you will learn all that you need to get your hands dirty in the field of Data Analysis, You will obtain the important intellectual and technical skills to start your first project. Enroll with us, We're looking forward to becoming a part of your success. Who this course is for Anyone using Excel and wants to dive into the world of Data Analysis Homepage https://www.udemy.com/course/microsoft-excel-analytics Screenshot Rapidgator https://rg.to/file/21846201eebbabe3f26d4a7d60fe2d31/lnkio.Excel.Analytics..Data.Analysis.with.PivotTables.and.Charts.part2.rar.html https://rg.to/file/ab5197996b69acdcae73df036181b03a/lnkio.Excel.Analytics..Data.Analysis.with.PivotTables.and.Charts.part1.rar.html Fikper Free Download https://fikper.com/e74GG1MWgo/lnkio.Excel.Analytics..Data.Analysis.with.PivotTables.and.Charts.part2.rar.html https://fikper.com/j7biZOOygG/lnkio.Excel.Analytics..Data.Analysis.with.PivotTables.and.Charts.part1.rar.html No Password - Links are Interchangeable

Free Download Data Engineering using AWS Data Analytics Last updated 2/2023 Created by Durga Viswanatha Raju Gadiraju,Pratik Kumar,Sathvika Dandu,Madhuri Gadiraju,Sai Varma,Phani Bhushan Bozzam MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 434 Lectures ( 26h 15m ) | Size: 7.77 GB Build Data Engineering Pipelines on AWS using Data Analytics Services - Glue, EMR, Athena, Kinesis, Lambda, Redshift What you'll learn Data Engineering leveraging Services under AWS Data Analytics AWS Essentials such as s3, IAM, EC2, etc Understanding AWS s3 for cloud based storage Understanding details related to virtual machines on AWS known as EC2 Managing AWS IAM users, groups, roles and policies for RBAC (Role Based Access Control) Managing Tables using AWS Glue Catalog Engineering Batch Data Pipelines using AWS Glue Jobs Orchestrating Batch Data Pipelines using AWS Glue Workflows Running Queries using AWS Athena - Server less query engine service Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards Data Ingestion using AWS Lambda Functions Scheduling using AWS Events Bridge Engineering Streaming Pipelines using AWS Kinesis Streaming Web Server logs using AWS Kinesis Firehose Overview of data processing using AWS Athena Running AWS Athena queries or commands using CLI Running AWS Athena queries using Python boto3 Creating AWS Redshift Cluster, Create tables and perform CRUD Operations Copy data from s3 to AWS Redshift Tables Understanding Distribution Styles and creating tables using Distkeys Running queries on external RDBMS Tables using AWS Redshift Federated Queries Running queries on Glue or Athena Catalog tables using AWS Redshift Spectrum Requirements A Computer with at least 8 GB RAM Programming Experience using Python is highly desired as some of the topics are demonstrated using Python SQL Experience is highly desired as some of the topics are demonstrated using SQL Nice to have Data Engineering Experience using Pandas or Pyspark This course is ideal for experienced data engineers to add AWS Analytics Services as key skills to their profile Description Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lakes or Data Warehouses and then from Data Lakes or Data Warehouses to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using AWS Data Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, EMR, Kinesis, and many more.Here are the high-level steps which you will follow as part of the course.Setup Development EnvironmentGetting Started with AWSStorage - All about AWS s3 (Simple Storage Service)User Level Security - Managing Users, Roles, and Policies using IAMInfrastructure - AWS EC2 (Elastic Cloud Compute)Data Ingestion using AWS Lambda FunctionsOverview of AWS Glue ComponentsSetup Spark History Server for AWS Glue JobsDeep Dive into AWS Glue CatalogExploring AWS Glue Job APIsAWS Glue Job BookmarksDevelopment Life Cycle of PysparkGetting Started with AWS EMRDeploying Spark Applications using AWS EMRStreaming Pipeline using AWS KinesisConsuming Data from AWS s3 using boto3 ingested using AWS KinesisPopulating GitHub Data to AWS DynamodbOverview of Amazon AWS AthenaAmazon AWS Athena using AWS CLIAmazon AWS Athena using Python boto3Getting Started with Amazon AWS RedshiftCopy Data from AWS s3 into AWS Redshift TablesDevelop Applications using AWS Redshift ClusterAWS Redshift Tables with Distkeys and SortkeysAWS Redshift Federated Queries and SpectrumHere are the details about what you will be learning as part of this course. We will cover most of the commonly used services with hands-on practice which are available under AWS Data Analytics.Getting Started with AWSAs part of this section, you will be going through the details related to getting started with AWS.Introduction - AWS Getting StartedCreate s3 BucketCreate AWS IAM Group and AWS IAM User to have required access on s3 Bucket and other servicesOverview of AWS IAM RolesCreate and Attach Custom AWS IAM Policy to both AWS IAM Groups as well as UsersConfigure and Validate AWS CLI to access AWS Services using AWS CLI CommandsStorage - All about AWS s3 (Simple Storage Service)AWS s3 is one of the most prominent fully managed AWS services. All IT professionals who would like to work on AWS should be familiar with it. We will get into quite a few common features related to AWS s3 in this section.Getting Started with AWS S3Setup Data Set locally to upload to AWS s3Adding AWS S3 Buckets and Managing Objects (files and folders) in AWS s3 bucketsVersion Control for AWS S3 BucketsCross-Region Replication for AWS S3 BucketsOverview of AWS S3 Storage ClassesOverview of AWS S3 GlacierManaging AWS S3 using AWS CLI CommandsManaging Objects in AWS S3 using CLI - LabUser Level Security - Managing Users, Roles, and Policies using IAMOnce you start working on AWS, you need to understand the permissions you have as a non-admin user. As part of this section, you will understand the details related to AWS IAM users, groups, roles as well as policies.Creating AWS IAM UsersLogging into AWS Management Console using AWS IAM UserValidate Programmatic Access to AWS IAM UserAWS IAM Identity-based PoliciesManaging AWS IAM GroupsManaging AWS IAM RolesOverview of Custom AWS IAM PoliciesManaging AWS IAM users, groups, roles as well as policies using AWS CLI CommandsInfrastructure - AWS EC2 (Elastic Cloud Compute) BasicsAWS EC2 Instances are nothing but virtual machines on AWS. As part of this section, we will go through some of the basics related to AWS EC2 Basics.Getting Started with AWS EC2Create AWS EC2 Key PairLaunch AWS EC2 InstanceConnecting to AWS EC2 InstanceAWS EC2 Security Groups BasicsAWS EC2 Public and Private IP AddressesAWS EC2 Life CycleAllocating and Assigning AWS Elastic IP AddressManaging AWS EC2 Using AWS CLIUpgrade or Downgrade AWS EC2 InstancesInfrastructure - AWS EC2 AdvancedIn this section, we will continue with AWS EC2 to understand how we can manage EC2 instances using AWS Commands and also how to install additional OS modules leveraging bootstrap scripts.Getting Started with AWS EC2Understanding AWS EC2 MetadataQuerying on AWS EC2 MetadataFitering on AWS EC2 MetadataUsing Bootstrapping Scripts with AWS EC2 Instances to install additional softwares on AWS EC2 instancesCreate an AWS AMI using AWS EC2 InstancesValidate AWS AMI - LabData Ingestion using Lambda FunctionsAWS Lambda functions are nothing but serverless functions. In this section, we will understand how we can develop and deploy Lambda functions using Python as a programming language. We will also see how to maintain a bookmark or checkpoint using s3.Hello World using AWS LambdaSetup Project for local development of AWS Lambda FunctionsDeploy Project to AWS Lambda consoleDevelop download functionality using requests for AWS Lambda FunctionsUsing 3rd party libraries in AWS Lambda FunctionsValidating AWS s3 access for local development of AWS Lambda FunctionsDevelop upload functionality to s3 using AWS Lambda FunctionsValidating AWS Lambda Functions using AWS Lambda ConsoleRun AWS Lambda Functions using AWS Lambda ConsoleValidating files incrementally downloaded using AWS Lambda FunctionsReading and Writing Bookmark to s3 using AWS Lambda FunctionsMaintaining Bookmark on s3 using AWS Lambda FunctionsReview the incremental upload logic developed using AWS Lambda FunctionsDeploying AWS Lambda FunctionsSchedule AWS Lambda Functions using AWS Event BridgeOverview of AWS Glue ComponentsIn this section, we will get a broad overview of all important Glue Components such as Glue Crawler, Glue Databases, Glue Tables, etc. We will also understand how to validate Glue tables using AWS Athena. AWS Glue (especially Glue Catalog) is one of the key components in the realm of AWS Data Analytics Services.Introduction - Overview of AWS Glue ComponentsCreate AWS Glue Crawler and AWS Glue Catalog Database as well as TableAnalyze Data using AWS AthenaCreating AWS S3 Bucket and Role to create AWS Glue Catalog Tables using Crawler on the s3 locationCreate and Run the AWS Glue Job to process data in AWS Glue Catalog TablesValidate using AWS Glue Catalog Table and by running queries using AWS AthenaCreate and Run AWS Glue TriggerCreate AWS Glue WorkflowRun AWS Glue Workflow and ValidateSetup Spark History Server for AWS Glue JobsAWS Glue uses Apache Spark under the hood to process the data. It is important we setup Spark History Server for AWS Glue Jobs to troubleshoot any issues.Introduction - Spark History Server for AWS GlueSetup Spark History Server on AWSClone AWS Glue Samples repositoryBuild AWS Glue Spark UI ContainerUpdate AWS IAM Policy PermissionsStart AWS Glue Spark UI ContainerDeep Dive into AWS Glue CatalogAWS Glue has several components, but the most important ones are nothing but AWS Glue Crawlers, Databases as well as Catalog Tables. In this section, we will go through some of the most important and commonly used features of the AWS Glue Catalog.Prerequisites for AWS Glue Catalog TablesSteps for Creating AWS Glue Catalog TablesDownload Data Set to use to create AWS Glue Catalog TablesUpload data to s3 to crawl using AWS Glue Crawler to create required AWS Glue Catalog TablesCreate AWS Glue Catalog Database - itvghlandingdbCreate AWS Glue Catalog Table - ghactivityRunning Queries using AWS Athena - ghactivityCrawling Multiple Folders using AWS Glue CrawlersManaging AWS Glue Catalog using AWS CLIManaging AWS Glue Catalog using Python Boto3Exploring AWS Glue Job APIsOnce we deploy AWS Glue jobs, we can manage them using AWS Glue Job APIs. In this section we will get overview of AWS Glue Job APIs to run and manage the jobs.Update AWS IAM Role for AWS Glue JobGenerate baseline AWS Glue JobRunning baseline AWS Glue JobAWS Glue Script for Partitioning DataValidating using AWS AthenaUnderstanding AWS Glue Job BookmarksAWS Glue Job Bookmarks can be leveraged to maintain the bookmarks or checkpoints for incremental loads. In this section, we will go through the details related to AWS Glue Job Bookmarks.Introduction to AWS Glue Job BookmarksCleaning up the data to run AWS Glue JobsOverview of AWS Glue CLI and CommandsRun AWS Glue Job using AWS Glue BookmarkValidate AWS Glue Bookmark using AWS CLIAdd new data to the landing zone to run AWS Glue Jobs using BookmarksRerun AWS Glue Job using BookmarkValidate AWS Glue Job Bookmark and Files for Incremental runRecrawl the AWS Glue Catalog Table using AWS CLI CommandsRun AWS Athena Queries for Data ValidationDevelopment Lifecycle for PysparkIn this section, we will focus on the development of Spark applications using Pyspark. We will use this application later while exploring EMR in detail.Setup Virtual Environment and Install PysparkGetting Started with PycharmPassing Run Time ArgumentsAccessing OS Environment VariablesGetting Started with SparkCreate Function for Spark SessionSetup Sample DataRead data from filesProcess data using Spark APIsWrite data to filesValidating Writing Data to FilesProductionizing the CodeGetting Started with AWS EMR (Elastic Map Reduce)As part of this section, we will understand how to get started with AWS EMR Cluster. We will primarily focus on AWS EMR Web Console. Elastic Map Reduce is one of the key service in AWS Data Analytics Services which provide capability to run applications which process large scale data leveraging distributed computing frameworks such as Spark.Planning for AWS EMR ClusterCreate AWS EC2 Key Pair for AWS EMR ClusterSetup AWS EMR Cluster with Apache SparkUnderstanding Summary of AWS EMR ClusterReview AWS EMR Cluster Application User InterfacesReview AWS EMR Cluster MonitoringReview AWS EMR Cluster Hardware and Cluster Scaling PolicyReview AWS EMR Cluster ConfigurationsReview AWS EMR Cluster EventsReview AWS EMR Cluster StepsReview AWS EMR Cluster Bootstrap ActionsConnecting to AWS EMR Master Node using SSHDisabling Termination Protection for AWS EMR Cluster and Terminating the AWS EMR ClusterClone and Create a New AWS EMR ClusterListing AWS S3 Buckets and Objects using AWS CLI on AWS EMR ClusterListing AWS S3 Buckets and Objects using HDFS CLI on AWS EMR ClusterManaging Files in AWS S3 using HDFS CLI on AWS EMR ClusterReview AWS Glue Catalog Databases and TablesAccessing AWS Glue Catalog Databases and Tables using AWS EMR ClusterAccessing spark-sql CLI of AWS EMR ClusterAccessing pyspark CLI of AWS EMR ClusterAccessing spark-shell CLI of AWS EMR ClusterCreate AWS EMR Cluster for NotebooksDeploying Spark Applications using AWS EMRAs part of this section, we will understand how we typically deploy Spark Applications using AWS EMR. We will be using the Spark Application we deployed earlier.Deploying Applications using AWS EMR - IntroductionSetup AWS EMR Cluster to deploy applicationsValidate SSH Connectivity to Master node of AWS EMR ClusterSetup Jupyter Notebook Environment on AWS EMR ClusterCreate required AWS s3 Bucket for AWS EMR ClusterUpload GHActivity Data to s3 so that we can process using Spark Application deployed on AWS EMR ClusterValidate Application using AWS EMR Compatible Versions of Python and SparkDeploy Spark Application to AWS EMR Master NodeCreate user space for ec2-user on AWS EMR ClusterRun Spark Application using spark-submit on AWS EMR Master NodeValidate Data using Jupyter Notebooks on AWS EMR ClusterClone and Start Auto Terminated AWS EMR ClusterDelete Data Populated by GHAcitivity Application using AWS EMR ClusterDifferences between Spark Client and Cluster Deployment Modes on AWS EMR ClusterRunning Spark Application using Cluster Mode on AWS EMR ClusterOverview of Adding Pyspark Application as Step to AWS EMR ClusterDeploy Spark Application to AWS S3 to run using AWS EMR StepsRunning Spark Applications as AWS EMR Steps in client modeRunning Spark Applications as AWS EMR Steps in cluster modeValidate AWS EMR Step Execution of Spark ApplicationStreaming Data Ingestion Pipeline using AWS KinesisAs part of this section, we will go through details related to the streaming data ingestion pipeline using AWS Kinesis which is a streaming service of AWS Data Analytics Services. We will use AWS Kinesis Firehose Agent and AWS Kinesis Delivery Stream to read the data from log files and ingest it into AWS s3.Building Streaming Pipeline using AWS Kinesis Firehose Agent and Delivery StreamRotating Logs so that the files are created frequently which will be eventually ingested using AWS Kinesis Firehose Agent and AWS Kinesis Firehose Delivery StreamSet up AWS Kinesis Firehose Agent to get data from logs into AWS Kinesis Delivery Stream.Create AWS Kinesis Firehose Delivery StreamPlanning the Pipeline to ingest data into s3 using AWS Kinesis Delivery StreamCreate AWS IAM Group and User for Streaming Pipelines using AWS Kinesis ComponentsGranting Permissions to AWS IAM User using Policy for Streaming Pipelines using AWS Kinesis ComponentsConfigure AWS Kinesis Firehose Agent to read the data from log files and ingest it into AWS Kinesis Firehose Delivery Stream.Start and Validate AWS Kinesis Firehose AgentConclusion - Building Simple Steaming Pipeline using AWS Kinesis FirehoseConsuming Data from AWS s3 using Python boto3 ingested using AWS KinesisAs data is ingested into AWS S3, we will understand how data can ingested in AWS s3 can be processed using boto3.Customizing AWS s3 folder using AWS Kinesis Delivery StreamCreate AWS IAM Policy to read from AWS s3 BucketValidate AWS s3 access using AWS CLISetup Python Virtual Environment to explore boto3Validating access to AWS s3 using Python boto3Read Content from AWS s3 objectRead multiple AWS s3 ObjectsGet the number of AWS s3 Objects using MarkerGet the size of AWS s3 Objects using MarkerPopulating GitHub Data to AWS DynamodbAs part of this section, we will understand how we can populate data to AWS Dynamodb tables using Python as a programming language.Install required libraries to get GitHub Data to AWS Dynamodb tables.Understanding GitHub APIsSetting up GitHub API TokenUnderstanding GitHub Rate LimitCreate New Repository for sinceExtracting Required Information using PythonProcessing Data using PythonGrant Permissions to create AWS dynamodb tables using boto3Create AWS Dynamodb TablesAWS Dynamodb CRUD OperationsPopulate AWS Dynamodb TableAWS Dynamodb Batch OperationsOverview of Amazon AWS AthenaAs part of this section, we will understand how to get started with AWS Athena using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Athena Query Editor.Getting Started with Amazon AWS AthenaQuick Recap of AWS Glue Catalog Databases and TablesAccess AWS Glue Catalog Databases and Tables using AWS Athena Query EditorCreate a Database and Table using AWS AthenaPopulate Data into Table using AWS AthenaUsing CTAS to create tables using AWS AthenaOverview of Amazon AWS Athena ArchitectureAmazon AWS Athena Resources and relationship with HiveCreate a Partitioned Table using AWS AthenaDevelop Query for Partitioned ColumnInsert into Partitioned Tables using AWS AthenaValidate Data Partitioning using AWS AthenaDrop AWS Athena Tables and Delete Data FilesDrop Partitioned Table using AWS AthenaData Partitioning in AWS Athena using CTASAmazon AWS Athena using AWS CLIAs part of this section, we will understand how to interact with AWS Athena using AWS CLI Commands.Amazon AWS Athena using AWS CLI - IntroductionGet help and list AWS Athena databases using AWS CLIManaging AWS Athena Workgroups using AWS CLIRun AWS Athena Queries using AWS CLIGet AWS Athena Table Metadata using AWS CLIRun AWS Athena Queries with a custom location using AWS CLIDrop AWS Athena table using AWS CLIRun CTAS under AWS Athena using AWS CLIAmazon AWS Athena using Python boto3As part of this section, we will understand how to interact with AWS Athena using Python boto3.Amazon AWS Athena using Python boto3 - IntroductionGetting Started with Managing AWS Athena using Python boto3List Amazon AWS Athena Databases using Python boto3List Amazon AWS Athena Tables using Python boto3Run Amazon AWS Athena Queries with boto3Review AWS Athena Query Results using boto3Persist Amazon AWS Athena Query Results in Custom Location using boto3Processing AWS Athena Query Results using PandasRun CTAS against Amazon AWS Athena using Python boto3Getting Started with Amazon AWS RedshiftAs part of this section, we will understand how to get started with AWS Redshift using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Redshift Query Editor.Getting Started with Amazon AWS Redshift - IntroductionCreate AWS Redshift Cluster using Free TrialConnecting to Database using AWS Redshift Query EditorGet a list of tables querying information schemaRun Queries against AWS Redshift Tables using Query EditorCreate AWS Redshift Table using Primary KeyInsert Data into AWS Redshift TablesUpdate Data in AWS Redshift TablesDelete data from AWS Redshift tablesRedshift Saved Queries using Query EditorDeleting AWS Redshift ClusterRestore AWS Redshift Cluster from SnapshotCopy Data from s3 into AWS Redshift TablesAs part of this section, we will go through the details about copying data from s3 into AWS Redshift tables using the AWS Redshift Copy command.Copy Data from s3 to AWS Redshift - IntroductionSetup Data in s3 for AWS Redshift CopyCopy Database and Table for AWS Redshift Copy CommandCreate IAM User with full access on s3 for AWS Redshift CopyRun Copy Command to copy data from s3 to AWS Redshift TableTroubleshoot Errors related to AWS Redshift Copy CommandRun Copy Command to copy from s3 to AWS Redshift tableValidate using queries against AWS Redshift TableOverview of AWS Redshift Copy CommandCreate IAM Role for AWS Redshift to access s3Copy Data from s3 to AWS Redshift table using IAM RoleSetup JSON Dataset in s3 for AWS Redshift Copy CommandCopy JSON Data from s3 to AWS Redshift table using IAM RoleDevelop Applications using AWS Redshift ClusterAs part of this section, we will understand how to develop applications against databases and tables created as part of AWS Redshift Cluster.Develop application using AWS Redshift Cluster - IntroductionAllocate Elastic Ip for AWS Redshift ClusterEnable Public Accessibility for AWS Redshift ClusterUpdate Inbound Rules in Security Group to access AWS Redshift ClusterCreate Database and User in AWS Redshift ClusterConnect to the database in AWS Redshift using psqlChange Owner on AWS Redshift TablesDownload AWS Redshift JDBC Jar fileConnect to AWS Redshift Databases using IDEs such as SQL WorkbenchSetup Python Virtual Environment for AWS RedshiftRun Simple Query against AWS Redshift Database Table using PythonTruncate AWS Redshift Table using PythonCreate IAM User to copy from s3 to AWS Redshift TablesValidate Access of IAM User using Boto3Run AWS Redshift Copy Command using PythonAWS Redshift Tables with Distkeys and SortkeysAs part of this section, we will go through AWS Redshift-specific features such as distribution keys and sort keys to create AWS Redshift tables.AWS Redshift Tables with Distkeys and Sortkeys - IntroductionQuick Review of AWS Redshift ArchitectureCreate multi-node AWS Redshift ClusterConnect to AWS Redshift Cluster using Query EditorCreate AWS Redshift DatabaseCreate AWS Redshift Database UserCreate AWS Redshift Database SchemaDefault Distribution Style of AWS Redshift TableGrant Select Permissions on Catalog to AWS Redshift Database UserUpdate Search Path to query AWS Redshift system tablesValidate AWS Redshift table with DISTSTYLE AUTOCreate AWS Redshift Cluster from Snapshot to the original stateOverview of Node Slices in AWS Redshift ClusterOverview of Distribution Styles related to AWS Redshift tablesDistribution Strategies for retail tables in AWS Redshift DatabasesCreate AWS Redshift tables with distribution style allTroubleshoot and Fix Load or Copy ErrorsCreate AWS Redshift Table with Distribution Style AutoCreate AWS Redshift Tables using Distribution Style KeyDelete AWS Redshift Cluster with a manual snapshotAWS Redshift Federated Queries and SpectrumAs part of this section, we will go through some of the advanced features of Redshift such as AWS Redshift Federated Queries and AWS Redshift Spectrum.AWS Redshift Federated Queries and Spectrum - IntroductionOverview of integrating AWS RDS and AWS Redshift for Federated QueriesCreate IAM Role for AWS Redshift ClusterSetup Postgres Database Server for AWS Redshift Federated QueriesCreate tables in Postgres Database for AWS Redshift Federated QueriesCreating Secret using Secrets Manager for Postgres DatabaseAccessing Secret Details using Python Boto3Reading Json Data to Dataframe using PandasWrite JSON Data to AWS Redshift Database Tables using PandasCreate AWS IAM Policy for Secret and associate with Redshift RoleCreate AWS Redshift Cluster using AWS IAM Role with permissions on secretCreate AWS Redshift External Schema to Postgres DatabaseUpdate AWS Redshift Cluster Network Settings for Federated QueriesPerforming ETL using AWS Redshift Federated QueriesClean up resources added for AWS Redshift Federated QueriesGrant Access on AWS Glue Data Catalog to AWS Redshift Cluster for SpectrumSetup AWS Redshift Clusters to run queries using SpectrumQuick Recap of AWS Glue Catalog Database and Tables for AWS Redshift SpectrumCreate External Schema using AWS Redshift SpectrumRun Queries using AWS Redshift SpectrumCleanup the AWS Redshift Cluster Who this course is for Beginner or Intermediate Data Engineers who want to learn AWS Analytics Services for Data Engineering Intermediate Application Engineers who want to explore Data Engineering using AWS Analytics Services Data and Analytics Engineers who want to learn Data Engineering using AWS Analytics Services Testers who want to learn key skills to test Data Engineering applications built using AWS Analytics Services Homepage https://www.udemy.com/course/data-engineering-using-aws-analytics-services/ Screenshot Rapidgator https://rg.to/file/2920699636897ec371102bdb027e8885/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part05.rar.html https://rg.to/file/2b9aa74769b487eaaedd3eec96345e51/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part04.rar.html https://rg.to/file/45d0ce3bc16db1d6999d3e6f18531e4c/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part06.rar.html https://rg.to/file/d29ebd836544f98050324f6aa8bef726/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part08.rar.html https://rg.to/file/d6f9f7479e24d0907c1621c594edb1fc/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part03.rar.html https://rg.to/file/e60b71f472370ab29b7e10f24d6cbe91/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part02.rar.html https://rg.to/file/e7a08371221bd4424901c60b4e7c8dd4/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part01.rar.html https://rg.to/file/ef32aa5500311afe27cc6fad7604a04d/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part07.rar.html Fikper Free Download https://fikper.com/1gyNST3T5B/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part02.rar.html https://fikper.com/BbGZKWTfP1/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part01.rar.html https://fikper.com/DJBmRaMzGu/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part03.rar.html https://fikper.com/SXxm2gI5gG/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part08.rar.html https://fikper.com/V3HWO9bEX3/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part05.rar.html https://fikper.com/m68l9B0z0U/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part06.rar.html https://fikper.com/yW1sDrNVjo/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part07.rar.html https://fikper.com/zvCGisvXzn/nwnrk.Data.Engineering.using.AWS.Data.Analytics.part04.rar.html No Password - 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Free Download Data Analytics Guide with Microsoft Excel and ChatGPT Published 10/2024 Created by Being Commerce,Being Commerce MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 48 Lectures ( 14h 32m ) | Size: 8.21 GB Master Data Analytics with Microsoft Excel and ChatGPT: Unleash the Power of Data! What you'll learn Understanding data analytics fundamentals. Navigating Microsoft Excel for data-related tasks. Learning basic and advanced Excel functions. Data cleaning, sorting, and filtering. How to use Excel's Power Query Editor for data transformation. Mastering Pivot Tables for visualization. Automating data processes using ChatGPT. Using logical operators and nested formulas. Creating professional data reports. Applying data aggregation and text functions. Real-world case studies for practical learning. Requirements A computer with Microsoft Excel installed. Basic computer literacy. No prior data analytics or Excel experience is required. Description Welcome to "Data Analytics Guide with Microsoft Excel and ChatGPT"! This course is designed to transform your understanding of data analytics, even if you have no prior experience. Data is the new gold, and knowing how to analyze it can elevate your professional and personal life. With this course, you will become a master of data analytics using Microsoft Excel, one of the most powerful and widely-used tools in the industry.Why this course stands out is its unique integration of Microsoft Excel with AI technology, specifically ChatGPT. Whether you're a beginner or someone looking to sharpen your skills, this course is structured in a way that ensures you're comfortable while learning. Imagine having the power of Excel, combined with AI-driven insights and formulas from ChatGPT, to analyze and manipulate data like never before!This course starts from the very basics, making it perfect for those with little to no experience in Excel. You will begin by understanding what data analytics is and why it's critical in today's business world. From there, you'll be guided step-by-step through mastering Excel's core functions, data cleaning, and formula application. Throughout the journey, ChatGPT will be introduced to show you how to automate and streamline your workflow, making your data analysis faster, more efficient, and precise.Data Analytics is not just a technical skill, but an essential one that opens doors to countless opportunities. Every business, organization, and entrepreneur relies on data-driven decisions to stay competitive, and you can be at the forefront of that decision-making. If you skip this course, you risk falling behind in a world where data literacy is no longer a choice, but a necessity. Don't let that happen. Enroll today and become a data expert, confident in your ability to deliver impactful insights and make smarter decisions. Who this course is for Beginners wanting to learn data analytics from scratch. Professionals looking to boost their career by adding data analytics to their skill set. Small business owners and entrepreneurs who want to make data-driven decisions. Students interested in learning how to work with Excel efficiently. Anyone who wants to automate data processes using AI tools like ChatGPT. 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Free Download Create and Share Analytics with Jupyter Notebooks Duration: 2h 12m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 312 MB Genre: eLearning | Language: English This course covers the important aspects of working with Jupyter notebooks, including installation and the role of kernels, magic functions, and running shell commands. In addition, the power of cloud-hosted Jupyter notebooks is explored on AWS, Microsoft Azure as well as the Google Cloud Platform. Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Jupyter is an execution environment rather than a fully-fledged IDE, but even so, notebooks have various important features that are worth understanding thoroughly. In this course, Create and Share Analytics with Jupyter Notebooks, you will learn how Jupyter notebooks are a key driver of Python's popularity, by providing an incredibly intuitive, interactive environment for executing Python programs. First, you will learn how to get up and running with Jupyter notebooks, and how best to leverage features such as markdown to enhance the readability of your code. Next, you will discover how more advanced features such as magic functions work, and how the next generation offering from Jupyter, named JupyterLab goes even further towards a fully-fledged development environment. Finally, you will round out your knowledge by working with cloud-hosted Jupyter notebooks on each of the major cloud platforms. When you're finished with this course, you will have the skills and knowledge to leverage the full power of Jupyter notebooks and Jupyterlab, particularly in the context of cloud-hosted notebooks for distributed and collaborative use-cases. Homepage https://www.pluralsight.com/courses/create-share-analytics-jupyter-notebooks Rapidgator https://rg.to/file/c3c88df5a6984262a474886bf7597e93/ofhik.Create.and.Share.Analytics.with.Jupyter.Notebooks.rar.html Fikper Free Download https://fikper.com/Nn8MzQFLcT/ofhik.Create.and.Share.Analytics.with.Jupyter.Notebooks.rar.html No Password - Links are Interchangeable

Free Download Building Your First Python Analytics Solution Duration: 2h 46m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 382 MB Genre: eLearning | Language: English This course covers the important aspects of choosing a development environment for Python, the differences between Conda and Pip for working with Python libraries, popular IDEs such as PyCharm, IDLE, Eclipse, and Spyder, as well as running Python on the cloud. Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Despite its great success as a prototyping tool, Python is still relatively unproven for large, enterprise-scale development. Homepage https://www.pluralsight.com/courses/building-first-python-analytics-solution Rapidgator https://rg.to/file/2e9cfdb917163d3a123626831e5bf881/uxzwp.Building.Your.First.Python.Analytics.Solution.rar.html Fikper Free Download https://fikper.com/MCJ0rhjXGs/uxzwp.Building.Your.First.Python.Analytics.Solution.rar.html No Password - Links are Interchangeable

Free Download Advanced L&D Analytics From Insights To Impact Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 250.09 MB | Duration: 0h 44m Transform Learning with Data-Driven Insights: Master L&D Analytics for Measurable Impact and Continuous Improvement What you'll learn Master the fundamentals of data analytics in L&D to make informed decisions. Discover how data analytics enhances L&D initiatives for better training outcomes. Identify key data sources for effective L&D analytics to focus on what matters. Generate training effectiveness reports to showcase the success of your programs. Produce reports on learner progress and performance to support individual journeys. Create reports on learner's engagement and parti[beeep]tion to enhance training involvement. Develop reports on content effectiveness to evaluate and improve your training materials. Summary Requirements L&D or HR Experience Description Here are the key benefits and top learnings from this course:Gain a solid understanding of data analytics in Learning & Development (L&D), empowering you to make smarter, data-driven decisions for improving learning strategies.Discover the advantages of using data to inform L&D strategies, ensuring alignment with business objectives and enhancing decision-making capabilities.Learn effective methods for collecting and analyzing training data, building a strong foundation for leveraging L&D analytics to boost program effectiveness.Master the creation of Training Effectiveness Reports, allowing you to assess the success of your programs, improve their design, and clearly demonstrate return on investment (ROI) to stakeholders.Track learner development with Learner Progress and Performance Reports, ensuring your workforce meets their learning and performance milestones.Measure engagement levels through Engagement and Parti[beeep]tion Reports, fostering higher levels of involvement in learning programs and ensuring employees stay motivated and engaged.Assess the success and relevance of learning content using Content Effectiveness and Utilization Reports, allowing you to optimize resource allocation and improve training outcomes.Leverage data insights to enhance learner experiences, boost retention, and continuously improve the quality and impact of your training programs.Understand how data can drive continuous improvement and innovation, ensuring your learning programs remain relevant and effective over time.Equip yourself with practical techniques to identify trends, spot skill gaps, and optimize your L&D efforts, using data to drive growth and development.Use analytics to demonstrate the tangible impact of learning initiatives on business outcomes and stakeholder satisfaction, showing the value of your L&D programs.This course is ideal for L&D professionals aiming to integrate data analytics into their strategy and maximize the effectiveness of their training programs. Overview Section 1: Introduction Lecture 1 Introduction (Advanced L&D Analytics: From Insights to Impact) Section 2: Advantages of Implementing Data Analytics in L&D Lecture 2 Advantages of Implementing Data Analytics in L&D Section 3: Collecting Data for L&D Analytics Lecture 3 Collecting Data for L&D Analytics Section 4: L&D Analytics Report Introduction Lecture 4 Generating L&D Analytics Report Section 5: Report Type 1: Training Effectiveness Report Lecture 5 Report 1: Training Feedback Lecture 6 Report 2: Pre-Post Assessment Result Lecture 7 Report 3: Self & Manager feedback Lecture 8 Report 4: Impact of training Lecture 9 Report 5: Return on Investment Section 6: Report 2: Engagement and Parti[beeep]tion Reports Lecture 10 Attendance rate in In-Person & virtual sessions Report Lecture 11 Interactive Activities Report Lecture 12 Click-Through Rates for E-Learning content Report Lecture 13 Time Spent on Modules Report Lecture 14 Login Frequency Report Lecture 15 Course Completion Rates Report Section 7: Report 3: Learner's Progress and Performance Reports Lecture 16 Training Completion Rate Report Lecture 17 Dropout Rates and Reasons Report Lecture 18 Performance in Quizzes and Exams Report Lecture 19 Learning Paths and Milestone Achievements Report Lecture 20 Time Taken to Complete Courses Report Section 8: Report 4: Content Effectiveness And Utilization Report Lecture 21 Most and Least Accessed Materials Reports Lecture 22 Completion Rates Reports Lecture 23 Time Spent on Content Report Section 9: Summary Lecture 24 Summary L&D Professionals, HR Heads Homepage https://www.udemy.com/course/advanced-lnd-analytics/ Rapidgator https://rg.to/file/58ac1700125d116c614f02b66b4eb1fb/cihck.Advanced.LD.Analytics.From.Insights.To.Impact.rar.html Fikper Free Download https://fikper.com/20eQsF0OhY/cihck.Advanced.LD.Analytics.From.Insights.To.Impact.rar.html No Password - Links are Interchangeable

Free Download Udemy - vPractical Text Analytics Using Spacy V3.0 Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.16 GB | Duration: 2h 0m How to extract information WITHOUT building custom Machine Learning models What you'll learn Understand the spaCy document object How spaCy pipelines work How to use Rule based Matching for Information Extraction A system for practical, iterative Text Analytics using the itables library Requirements Intermediate Knowledge of Python programming Basic knowledge of the pandas dataframe library Description What is text analytics?I like this definition: "Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components, and then evaluating each part's role and meaning using complex software rules and machine learning algorithms."[Source: Lexalytics website]In spaCy, you can use machine learning algorithms in two ways1) pretrained models provided by spaCy and other organizations - for example the en_core_web_md, which I use in this course, is a pretrained model provided by Explosion, the company which created spaCy2) custom machine learning models that you train on your data - which is often referred to in the documentation as "statistical models"Why not statistical models?This is what the makers of spaCy say in their documentation:"For complex tasks, it's usually better to train a statistical entity recognition model. However, statistical models require training data, so for many situations, rule-based approaches are more practical. This is especially true at the start of a project: you can use a rule-based approach as part of a data collection process, to help you "bootstrap" a statistical model.Training a model is useful if you have some examples and you want your system to be able to generalize based on those examples. It works especially well if there are clues in the local context. For instance, if you're trying to detect person or company names, your application may benefit from a statistical named entity recognition model.Rule-based systems are a good choice if there's a more or less finite number of examples that you want to find in the data, or if there's a very clear, structured pattern you can express with token rules or regular expressions. For instance, country names, IP addresses or URLs are things you might be able to handle well with a purely rule-based approach."Just to clarify, I am not against developing statistical models - but as the documentation states quite clearly, it is often more practical to start with rules based systems. One of my main aims in this course is to provide a solid understanding of what you can and cannot do using just a rules based system - in fact I use only one dataset in this entire course so it is a lot easier for the students to make this distinction.When you combine a rules based system with the data visualization technique I describe in this course, you will also gain a very good understanding of your dataset. You can then use this understanding to improve your statistical model if you choose to build one. In my view, most people barely scratch the surface when it comes to using spaCy rules for text analytics. I hope this course will provide them a lot of new insight into how to approach this task. Overview Section 1: About this course Lecture 1 How this course is different from other spaCy courses Lecture 2 The best dataset for learning text analytics Section 2: Exploring spaCy document objects Lecture 3 Import libraries Lecture 4 Splitting text into sentences Lecture 5 Splitting text into words Lecture 6 Part-of-speech tagging Lecture 7 Stop words and punctuation Lecture 8 Text spans Lecture 9 Dependency Parse Tree Lecture 10 Named Entity Recognition Lecture 11 Token is_ attributes Lecture 12 Token like_ attributes Lecture 13 More token attributes Lecture 14 Remaining token attributes Lecture 15 Visualizing the Subtree Lecture 16 Visualizing the token head Section 3: spaCy pipelines Lecture 17 Display pipeline Lecture 18 Tokenizer is unique Lecture 19 tagger Lecture 20 parser Lecture 21 attribute_ruler Lecture 22 lemmatizer Lecture 23 ner Section 4: Rule based matching Lecture 24 Token matcher Lecture 25 Dependency Matcher based on position Lecture 26 Dependency Matcher based on the parse tree Lecture 27 Phrase matcher Section 5: Download the Jupyter notebook Lecture 28 Download the Jupyter notebook used in this course Data Science practitioners who want to use spaCy and Natural Language Processing,Anyone who has a spreadsheet where one of the columns is a paragraph of text and wants to know how to extract useful information from that text to use with the filters you can apply on the OTHER columns (sort, less than, greater than etc) in spreadsheet tools like Excel and Airtable Homepage https://www.udemy.com/course/practical-text-analytics-using-spacy-v3/ Rapidgator https://rg.to/file/62de6a95c15c9caec322b44adc37b8d3/zefsq.Practical.Text.Analytics.Using.Spacy.V3.0.part1.rar.html https://rg.to/file/ebe4aa0823cc80bc955727b0d97e63a3/zefsq.Practical.Text.Analytics.Using.Spacy.V3.0.part2.rar.html Fikper Free Download https://fikper.com/mEBhv5CBte/zefsq.Practical.Text.Analytics.Using.Spacy.V3.0.part1.rar https://fikper.com/S8KuPYpfKq/zefsq.Practical.Text.Analytics.Using.Spacy.V3.0.part2.rar No Password - Links are Interchangeable

Free Download Master Python & Generative Ai For Advanced Analytics Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.33 GB | Duration: 3h 44m Master Python and Generative AI to enhance your skills in advanced analytics What you'll learn Understand the core concepts and applications of Generative AI. Master Python programming for building AI-driven analytical models. Implement Generative Adversarial Networks (GANs) in Python. Learn data augmentation techniques for advanced analytics. Explore text analysis and processing with Generative AI tools. Apply image processing techniques using Python and AI libraries. Optimize model performance through training and troubleshooting. Use Python libraries for data manipulation and visualization. Develop predictive models using AI-driven insights. Gain practical experience with real-world projects in analytics. Requirements Basic understanding of Python programming Familiarity with machine learning concepts is helpful but not required Access to a computer with Python installed Willingness to learn Description Unlock the power of Python and Generative AI in advanced analytics with this comprehensive course designed for data enthusiasts, analysts, and developers. This course will equip you with the skills to harness the latest in AI technology, allowing you to build and apply generative models for tasks like data augmentation, text analysis, image processing, and predictive modeling.Starting with the foundational concepts of Generative AI, you will explore various types of generative models and understand their applications in real-world analytics. As you move through the course, you will delve into Python programming concepts essential for working with AI, covering data manipulation, visualization, and machine learning libraries.The course also includes hands-on projects such as constructing Generative Adversarial Networks (GANs) and using them for stock market trend predictions. You'll gain in-depth knowledge of data preparation, model training, optimization techniques, and troubleshooting strategies for achieving high-performance models.By the end of this course, you will be equipped with the knowledge to apply generative AI techniques in various fields, enhancing your data analysis capabilities and leveraging AI for predictive insights and improved data-driven decisions.Whether you're a beginner or an experienced programmer, this course is tailored to help you master advanced Python and generative AI for your analytics needs! Overview Section 1: Foundational Concepts of Generative AI Lecture 1 Course Outline and Goal Lecture 2 Introduction to Generative AI Lecture 3 Applications in Advanced Analytics Lecture 4 Different types of Generative Models Lecture 5 Generative AI vs. Traditional ML Lecture 6 Course Structure and Learning Objectives Section 2: Python Programming for Generative AI Lecture 7 Python for Generative AI Workflows Lecture 8 Setting Up the Environment Section 3: Core Python Programming Concepts Lecture 9 Variables and Data types Lecture 10 Data Structures in Python. Lecture 11 Control flows in python Lecture 12 Functions in Python Lecture 13 Object Oriented Programming in Python Lecture 14 Regular Expressions in Python Lecture 15 Modules in Python Lecture 16 File Handling in Python Lecture 17 Error Handling in Python Section 4: Essential Python Libraries for Generative AI Lecture 18 Essential Python Libraries for Generative AI(Theory) Lecture 19 Data manipulation Lecture 20 Data visualization Lecture 21 Image Processing Lecture 22 Machine Learning tools Lecture 23 Model Building and Training Section 5: Model Building and Training Lecture 24 Data Wrangling for Python (Part 1) Lecture 25 Data Wrangling for Python (Part 2) Lecture 26 Advanced Python Concepts Lecture 27 Generative AI Libraries Section 6: Building Generative Models Lecture 28 Understanding Generative Adversarial Networks Lecture 29 Constructing Your First GAN with Python Lecture 30 Model Training and Optimization Techniques Lecture 31 Troubleshooting Training Challenges Lecture 32 Understanding Model Performance Section 7: Generative AI Applications for Advanced Analytics Lecture 33 Data Generation Lecture 34 Augmentation for Improved Analysis Lecture 35 Advanced Text Analysis with Generative AI Lecture 36 Generative AI for Images & Signals Lecture 37 7.5 Predictive Analytics with Generative AI Lecture 38 Analytics Insights with Generative AI Lecture 39 Applications of Generative AI in Advanced Analytics Section 8: Project Title: "Generative AI-powered Stock Market Trend Prediction" Lecture 40 8.1 Data Collection & Preprocessing Lecture 41 8.2 Model Building Lecture 42 8.3 Data Generation & Trend Analysis Lecture 43 Evaluation Section 9: Conclusion Lecture 44 Course Recap and Key Learnings Lecture 45 The Future of Generative AI and Impact on Advanced Analytics Lecture 46 Additional Resources and Learning Paths Data analysts looking to expand their AI skills,Python developers interested in advanced analytics,Machine learning enthusiasts seeking practical AI applications,Students of data science and AI,Professionals aiming to integrate AI into their workflows,Beginners with basic Python knowledge wanting to explore AI Homepage https://www.udemy.com/course/master-python-generative-ai-for-advanced-analytics/ Rapidgator https://rg.to/file/c0550d42e04f72b57575016d0788c2cf/rwbpk.Master.Python..Generative.Ai.For.Advanced.Analytics.part1.rar.html https://rg.to/file/41cad725242b9e12d6b1982b910430eb/rwbpk.Master.Python..Generative.Ai.For.Advanced.Analytics.part2.rar.html Fikper Free Download https://fikper.com/Ny5UyiZ7lj/rwbpk.Master.Python..Generative.Ai.For.Advanced.Analytics.part1.rar https://fikper.com/1FOXE5TXdI/rwbpk.Master.Python..Generative.Ai.For.Advanced.Analytics.part2.rar No Password - Links are Interchangeable

Free Download Microsoft Powerbi For Business Analytics And Intelligence Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 559.70 MB | Duration: 0h 47m Become proficient in Power BI Desktop for data analysis through practical assignments and projects. What you'll learn Construct high-quality business intelligence reports starting from the basics. Utilize tools employed by professional analysts and data scientists for design and implementation. Discover the capabilities of powerful artificial intelligence tools and advanced visualization techniques. Transform and integrate raw data into visually appealing interactive dashboards. Demonstrate your proficiency through two comprehensive course projects, complete with step-by-step solutions. Requirements Microsoft Power BI Desktop (free download) This course is designed for PC/Windows users (currently not available for Mac) Experience with Excel is an added advantage, but not a requirement Description Course Description: Microsoft Power BI - PizzaShop AnalysisUnlock the full potential of Power BI and master the top business intelligence tool with this course. Whether you're an experienced data professional or a newcomer to analytics, this course will equip you with the skills to turn raw data into meaningful insights.THE COURSE PROJECT:Step into the role of a Business Intelligence Analyst for PizzaShop, a fictional pizzeria business. Your task will be to transform data into professional-quality reports and dashboards, tracking key performance indicators (KPIs), analyzing trends, and identifying top customers. You'll follow real-world processes that professionals use daily in business intelligence roles.The course follows four key stages of the business intelligence workflow:STAGE 1: Connecting & Shaping DataLearn how to connect to various data sources and shape data using Power Query. You'll master the tools to clean, transform, and prepare data for analysis. Topics include:Data connectors and storage modesTable transformations and data profiling toolsPivoting, unpivoting, and appending queriesSTAGE 2: Building a Relational Data ModelDive into data modeling best practices, focusing on creating relationships between tables to organize your data efficiently. You'll cover:Database normalizationFact & dimension tablesStar schemas and relationship cardinalitySTAGE 3: DAX & Calculated FieldsDevelop your skills in Data Analysis Expressions (DAX) to create calculated fields, measures, and complex expressions for data analysis. Key concepts include:Row and filter contextTime intelligence patternsConditional and logical functionsSTAGE 4: Data Visualization & ReportingBring your data to life with powerful data visualizations. Learn to design interactive dashboards and reports that drive decision-making, using:Best practices for data visualizationInteractive reports with slicers, drill-downs, and bookmarksCustom visualizations and Power BI service integrationWith real-world assignments, you'll not only learn the theory but gain hands-on experience in creating stunning Power BI reports tailored for business impact.Ready to transform your data skills? Join the course and start building dynamic Power BI reports today! Overview Section 1: Introduction and your First intelligence project Lecture 1 Introduction to this PowerBI course Lecture 2 Instruction to Install PowerBI on Windows Lecture 3 Practice files and Instructions Lecture 4 Connecting our Data to PowerBI Desktop Lecture 5 Transforming and Preparing the Data in PowerBI Lecture 6 Analyze and Visualize data Section 2: All about PowerBI and Business Intelligence Tools Lecture 7 What is PowerBI Lecture 8 Why should we use Microsoft PowerBI Lecture 9 Excel vs PowerBI Section 3: Conclusion Lecture 10 Conclusion Individuals seeking a practical, project-driven introduction to Microsoft Power BI Desktop,Data analysts and Excel users aiming to enhance their skills in advanced data modeling, dashboard design, and business intelligence,Aspiring data professionals eager to excel in using the leading business intelligence tool in the market.,Students in search of a comprehensive, engaging, and highly interactive training approach,Those aspiring to build a career in data analysis or business intelligence Homepage https://www.udemy.com/course/microsoft-powerbi-for-business-analytics-and-intelligence/ Rapidgator https://rg.to/file/45d40d910b1449379a31dc0e73bcdf36/svzyg.Microsoft.Powerbi.For.Business.Analytics.And.Intelligence.rar.html Fikper Free Download https://fikper.com/LbbjM7pveu/svzyg.Microsoft.Powerbi.For.Business.Analytics.And.Intelligence.rar.html No Password - Links are Interchangeable

Free Download Data Analytics Toolkit From Excel to Python, R, and Tableau Released 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 11h 9m | Size: 3.23 GB Course Outline Data Analytics Toolkit: Introduction 6m 20s Author introduction by Eric Gaze 4m 49s Author introduction by McKenzie Lamb 2m 42s Topics 2m 34s 1.1 Excel: Bank Data 24m 48s 1.2 Tableau: Bank Data 15m 20s 1.3 R: Bank Data Part 1 15m 12s 1.4 R: Bank Data Part 2 5m 56s 1.5 R: Bank Data Part 3 9m 19s 1.6 R: Bank Data Part 4 16m 21s 1.7 R: Bank Data Part 5 12m 30s 1.8 Intro to Python: Bank Data 21m 49s 1.9 Python Plotly: Bank Data 23m 41s Topics 6m 8s 2.1 Excel: Countries Part 1 19m 28s 2.2 Excel: Countries Part 2 23m 43s 2.3 Tableau: Countries Part 1 25m 23s 2.4 Tableau: Countries Part 2 30m 47s 2.5 R: Countries Part 1 23m 57s 2.6 R: Countries Part 2 25m 30s 2.7 Python: Countries 26m 12s Topics 6m 9s 3.1 Excel: Wisconsin Elections 20m 3.2 Tableau: Wisconsin Elections Part 1 15m 55s 3.3 Tableau: Wisconsin Elections Part 2 16m 10s 3.4 Tableau: Wisconsin Elections Part 3 9m 15s 3.5 R: Wisconsin Elections Part 1 15m 34s 3.6 R: Wisconsin Elections Part 2 13m 14s 3.7 Python: Wisconsin Elections Part 1 10m 21s 3.8 Python: Wisconsin Elections Part 2 20m 46s Topics 3m 11s 4.1 Excel: Covid 11m 19s 4.2 Tableau: Covid 11m 59s 4.3 R: Covid 21m 11s 4.4 Python: Covid 21m 27s Topics 1m 56s 5.1 Excel: Nightingale's Rose 20m 11s 5.2 Tableau: Nightingale's Rose 26m 57s 5.3 R: Nightingale's Rose 26m 4s 5.4 Python: Nightingale's Rose Part 1 13m 52s 5.5 Python: Nightingale's Rose Part 2 20m 8s 5.6 Python: Nightingale's Rose Part 3 6m 45s 5.7 Python: Nightingale's Rose Part 4 14m 30s Data Analytics Toolkit: Summary 33s https://www.oreilly.com/videos/data-analytics-toolkit/9780135397732/ Rapidgator https://rg.to/file/1170371d0884f5a3259e4c09d38418bc/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part4.rar.html https://rg.to/file/1ad3e69cede1fc2ed9da10421fe93e58/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part1.rar.html https://rg.to/file/88b42107810b1930e07fb3ce60a54080/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part2.rar.html https://rg.to/file/92a02c2d4f4cd927ec60b5f279ee492c/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part3.rar.html Fikper Free Download https://fikper.com/BReAwaBegG/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part3.rar.html https://fikper.com/BWGkrObSkO/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part2.rar.html https://fikper.com/JoFqFfESk4/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part4.rar.html https://fikper.com/zFAnvSVuTL/kopbl.Data.Analytics.Toolkit.From.Excel.to.Python.R.and.Tableau.part1.rar.html No Password - Links are Interchangeable

Free Download Big Data Analytics with Hadoop and Apache Spark (2024) Released 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 51m | Size: 119 MB Apache Hadoop was a pioneer in the world of big data technologies, and it continues to lead in enterprise big data storage. Apache Spark is the top big data processing engine and provides an impressive array of features and capabilities. When used together, the Hadoop Distributed File System (HDFS) and Spark can provide a truly scalable setup for big data analytics. In this course, data analytics expert Kumaran Ponnambalam shows you how to leverage these two technologies to build scalable and optimized data analytics pipelines. Explore ways to optimize data modeling and storage on HDFS; discuss scalable data ingestion and extraction using Spark; and review actionable tips for optimizing data processing in Spark. Plus, complete a use case project that allows you to practice your new techniques. Homepage https://www.linkedin.com/learning/big-data-analytics-with-hadoop-and-apache-spark-24658440 TakeFile https://takefile.link/w0zy9iimazaw/rcymx.Big.Data.Analytics.with.Hadoop.and.Apache.Spark.2024.rar.html Rapidgator https://rg.to/file/c2aa6957b523d1208f0d282967f7e844/rcymx.Big.Data.Analytics.with.Hadoop.and.Apache.Spark.2024.rar.html Fikper Free Download https://fikper.com/zuaXVhI7bl/rcymx.Big.Data.Analytics.with.Hadoop.and.Apache.Spark.2024.rar.html No Password - Links are Interchangeable

Free Download Certified Analytics Professional (Cap) Exam Prep Course Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.21 GB | Duration: 7h 55m Master the skills and knowledge to ace the CAP exam and advance your career as a Certified Analytics Professional! What you'll learn Understanding the CAP certification process and its benefits. Mastering business problem framing and analytical problem-solving techniques. Gaining proficiency in data science, including data acquisition, preparation, analysis, and feature engineering. Applying the Five E's of the CAP exam and developing essential soft skills for the certification. Effectively using data visualization tools to communicate insights and create impactful data stories. Learning different analytics methodologies, validating models, and using predictive and simulation techniques. Familiarizing with CAP-specific terminology and concepts like regression, predictive, and prescriptive analytics. By the end of the course, students will be fully equipped to pass the CAP exam and advance their careers in the field of analytics. Requirements Basic Understanding of Analytics: A foundational knowledge of data analytics concepts and methodologies is recommended to facilitate comprehension of advanced topics. Familiarity with Data Science Tools: Prior experience with data analysis tools and software (e.g., Excel, R, Python, or SQL) will be beneficial. Statistical Knowledge: A basic understanding of statistical principles and methods is essential for grasping analytical problem framing and interpretation. Critical Thinking Skills: Students should possess strong analytical and critical thinking skills to effectively identify and solve business problems. Desire to Obtain CAP Certification: A motivation to pursue the Certified Analytics Professional (CAP) certification will enhance engagement and commitment to the course material. Description Introduction:The Certified Analytics Professional (CAP) certification is a globally recognized credential that validates your expertise in analytics. This course is designed to help you master the essential topics and skills needed to excel in the CAP exam. You will gain insights into business problem framing, analytical problem-solving, data science, and the importance of data visualization. Whether you are an aspiring data scientist, an analytics professional, or someone aiming to advance their career with a CAP certification, this course offers structured learning to help you succeed.Section 1: Introduction to CAP ExamsThe course begins with an introduction to the CAP certification, outlining the benefits of earning this credential for analytics professionals. You'll gain a deep understanding of the CAP certification process and how it can boost your career. Additionally, you'll learn the relevance of the certification across different industries and how it serves as a benchmark for analytic skills.Section 2: Understanding ObjectivesIn this section, you will dive into the key objectives of the CAP exam and their respective weightages. Lectures cover topics such as business problem framing, analytical problem framing, and the methodological approach to solving business challenges. The concept of "knowledge statements" and effective presentation techniques will also be explored, helping you understand what the exam evaluators are looking for.Section 3: Understanding Business Problem IdentificationThis section focuses on the critical task of identifying business problems and conducting stakeholder analysis. You'll learn how to refine problem statements and agree on initial business benefits with stakeholders. The goal is to ensure that you can clearly define problems before jumping into analytical solutions.Section 4: Further Reading on Business Problem FramingHere, you will be guided through the process of writing effective problem statements. This section emphasizes problem-solving techniques, the process of defining a problem, and the powerful impact of re-framing problems. You'll be equipped with questions to frame business problems more effectively, setting the stage for impactful analytical work.Section 5: Analytical ProblemThis section delves into the process of analytical problem framing and introduces you to frameworks such as Kano's Requirement Model. You'll explore key success metrics, how to propose drivers and relationships between inputs, and understand the core principles that guide successful analytics problem framing.Section 6: Certified Analyst Professional Training - Data ScienceData science plays a crucial role in CAP certification. This section covers data science fundamentals and explores the differences between business intelligence (BI) and data science. You'll learn the step-by-step process of acquiring and preparing data, analyzing it, and transforming data into actionable insights. Key concepts like feature engineering, dimensionality reduction, and model validation are also discussed.Section 7: Certified Analyst Professional Training - Five E's of CAP ExamThis section introduces the Five E's of the CAP exam, focusing on the key skills and soft skills required to pass the exam. You'll learn how to clarify the analytical process, understand CAP-specific terminology, and apply regression, predictive, and prescriptive analytics. Real-world examples will demonstrate the practical applications of these skills.Section 8: Data Visualization - CAP CertificationIn this section, you'll explore the importance of data visualization in presenting analytics results. Learn common data visualization techniques such as decision trees and heat maps, and how to effectively communicate data insights through data storytelling. Data quality, cleaning, and building a data mart are also discussed, providing you with the tools to create meaningful, accurate visual representations.Section 9: Analytics Methodology and Test Analytics ModelThe final section focuses on different analytics methodologies and how to validate analytics models. You'll learn about predictive methodologies, simulation techniques, and software tool selection. This section ensures you are prepared to test, refine, and implement analytics models in a real-world context.Conclusion:By the end of this course, you will have a solid understanding of the key components required to excel in the CAP exam. You will be proficient in framing business and analytical problems, applying data science techniques, utilizing data visualization tools, and validating analytics models. This comprehensive training will equip you with the skills necessary to become a Certified Analytics Professional. Overview Section 1: Introduction to CAP Exams Lecture 1 CAP certification and Benefits Section 2: Understanding Objectives Lecture 2 Different Objectives and their Weightage Lecture 3 Objective- Business Problem Framing Lecture 4 Objective- Analytical Problem Framing Lecture 5 Objective- Methodology Approach Lecture 6 What are Knowledge Statements Lecture 7 Knowledge Statements- Presentation techniques Section 3: Understanding Business Problem Identification Lecture 8 Business problem identification and stakeholders analysis Lecture 9 How to refine problem statement Lecture 10 Initial business benefits and stakeholders agreement Section 4: Further Reading Business Problem Lecture 11 How to Write a problem Statement Lecture 12 Problem Statement- Issue, Vision etc Lecture 13 Problem Solving Lecture 14 The Problem Definition Process Lecture 15 Power of Re-framing Problems Lecture 16 Power of Re-framing Problem continued Lecture 17 Business Problem Framing Questions Section 5: Analytical Problem Lecture 18 Analytical Problem Framing Lecture 19 Kano's Requirement Model Lecture 20 Proposed set of drivers and relationship to inputs Lecture 21 Key Metrics of Success Section 6: Certified Analyst Professional training- Data Science Lecture 22 Data Science Introduction and difference between BI and Data Science Lecture 23 Data Science Introduction and difference between BI and Data Science continued Lecture 24 How Data Science Work along with Acquire and Prepare Steps Lecture 25 How Data Science Work along with Acquire and Prepare Steps continued Lecture 26 How to Analyse and Act Data Lecture 27 Guiding Principles and Reasoning and Common Sense Lecture 28 Components of Data Science Lecture 29 Classes of Analytic Techniques Transforming Learning and Predictive Analytics Lecture 30 Learning Models , Execution Models Scheduling and Sequencing Lecture 31 Decomposing Analytical Problem Lecture 32 Data Science Maturity Lecture 33 Feature Engineering Dimensionality Reduction and Model Validation Part 1 Lecture 34 Feature Engineering Dimensionality Reduction and Model Validation Part 2 Lecture 35 DATA CAP Questions Section 7: Certified Analyst Professional training- FIVE E of CAP Exam Lecture 36 The Five E for CAP exam Lecture 37 The Five E for CAP exam continued Lecture 38 Soft Skills for CAP exam Lecture 39 Clarifying the Analytical Process Lecture 40 CAP Terminology Yield Vechile Routing Problem and TSP Lecture 41 CAP Terminology supply chain six sigma RFM Lecture 42 CAP Terminology supply chain six sigma RFM continued Lecture 43 Pattern Recognition Regression Predictive and Prescriptive Analytics Lecture 44 Pattern Recognition Regression Predictive and Prescriptive Analytics continued Section 8: Data Visualization- CAP Certification Lecture 45 Data Visualization Definition and Importance Lecture 46 Data Visualization Definition and Importance continued Lecture 47 Common Techniques for Data Visualization,Data Cardinality and Velocity Lecture 48 Common Techniques for Data Visualization,Data Cardinality and Velocity Continued Lecture 49 Decision Trees Heat Maps and other type of Data Visualization Techniques Lecture 50 How to write Data Story Lecture 51 Data Cleaning Lecture 52 Quality of Data and Datamart Lecture 53 Quality of Data and Datamart continued Lecture 54 CAP Terminology Optimization and Next Best offer Section 9: Analytics Methodology and Test Analytics Model Lecture 55 Analytics Methodology Introduction Lecture 56 Different type of Analytics Methodology Lecture 57 Software Tool Selection Lecture 58 Validating Analytics Model and Testing Results Lecture 59 Predictive Methodlogy and Different Kinds Lecture 60 Simulation and its Kind Aspiring Analysts: Individuals looking to start a career in analytics and seeking to obtain the Certified Analytics Professional (CAP) certification.,Data Science Professionals: Those currently working in data science or related fields who wish to deepen their understanding of analytics methodologies and improve their skills.,Business Professionals: Managers and decision-makers who want to enhance their analytical skills to make data-driven decisions within their organizations.,Students: University or college students pursuing degrees in business, data science, statistics, or related fields who are interested in gaining practical knowledge and certification in analytics.,Career Changers: Professionals from non-analytical backgrounds seeking to transition into data analytics roles and wanting a comprehensive understanding of the analytics process. 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Free Download CBTNuggets - DP-600 Implementing Analytics Solutions Using Microsoft Fabric Released 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 129 Lessons ( 13h 2m ) | Size: 4.1 GB This intermediate DP-600 training prepares learners to integrate diverse data sources, create and deploy data models, implement advanced analytics, and optimize performance with Microsoft Fabric. Microsoft Fabric comes equipped with some of the industry's most powerful tools and services for designing, creating and deploying enterprise-scale analytics solutions. But without deliberate and thorough training like this course, those tools are just complicated and expensive buttons that no one really understands. This course puts the enormous power of Microsoft Fabric into your hands. Learn how to design and deploy powerful data solutions that tackle complex challenges. And all the while prepare for the Fabric Analytics Engineer Associate certification. For IT managers, this Azure training can be used to onboard new data engineers, curated into individual or team training plans, or as a Azure reference resource. DP-600: What You Need to Know This DP-600: Implementing Analytics Solutions Using Microsoft Fabric training has videos that cover topics including Integrating diverse data sources for ingest Creating robust data modules in Power BI Implementing advanced analytics with Azure Improving data models with machine learning and AI Optimizing enterprise-scale data solutions Who Should Take DP-600 Training? This PCEP training is considered foundational-level Python training, which means it was designed for junior software developers. This Python skills course is valuable for new IT professionals with at least a year of experience with software development and experienced junior software developers looking to validate their Python skills. New or aspiring junior software developers. This course gives brand new software developers a perfect foundation for landing your first Python job. It covers essential Python skills and terminology while giving chances to practice writing clean and efficient code. The projects you'll code in this course will look great in a portfolio and the certification will be great on a resume. Experienced junior software developers.This is a good course for software developers who have a few years of experience but no formal training in coding. You'll be familiar with most of the concepts on this course, but learning them from the ground up will ensure your foundations are thoroughly covered and you're ready to advance to advanced Python courses or start learning other languages. Homepage https://www.cbtnuggets.com/it-training/microsoft-azure/fabric-analytics-engineer-associate TakeFile https://takefile.link/5swnkdq4xufj/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part1.rar.html https://takefile.link/phyjyi9nrrq2/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part2.rar.html https://takefile.link/g9m2zga2wmjf/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part3.rar.html https://takefile.link/jychxglhnkpk/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part4.rar.html https://takefile.link/ylyizadlxz0x/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part5.rar.html Rapidgator https://rg.to/file/14558312dde1e1b43ec4342c4fa7fa60/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part1.rar.html https://rg.to/file/0c06506c64ea8bf9b79f38a052d62428/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part2.rar.html https://rg.to/file/b5dc945849159302de3d29167de4906b/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part3.rar.html https://rg.to/file/4ab41cb5b078fd5d623cd7f4a63fff02/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part4.rar.html https://rg.to/file/ac248cb05444ca7079a240ae53e276ad/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part5.rar.html Fikper Free Download https://fikper.com/kLK4kdqDdv/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part1.rar.html https://fikper.com/NDaHmzSSCD/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part2.rar.html https://fikper.com/LkCdKH3vnI/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part3.rar.html https://fikper.com/vQKcbRdEIW/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part4.rar.html https://fikper.com/aOpapjc82Z/ohqsc.CBTNuggets..DP600.Implementing.Analytics.Solutions.Using.Microsoft.Fabric.part5.rar.html No Password - Links are Interchangeable

Free Download Getting Started with Augmented Analytics in Sisense Duration: 50m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 166 MB Genre: eLearning | Language: English This course will teach you how Sisense augmented analytics features - the analytics that leverages artificial intelligence - can help you extract relevant and actionable insights from your data. Artificial Intelligence has helped solve several problems and applied to the field of business intelligence. The term Augmented Analytics is often used to refer to BI tools and capabilities that leverage AI and ML. Homepage https://www.pluralsight.com/courses/getting-started-augmented-analytics-sisense TakeFile https://takefile.link/dn0jwhcysiya/blrvh.Getting.Started.with.Augmented.Analytics.in.Sisense.rar.html Rapidgator https://rg.to/file/e41a7e608e5b9cdeaaf6608be3d876d2/blrvh.Getting.Started.with.Augmented.Analytics.in.Sisense.rar.html Fikper Free Download https://fikper.com/dO92GIcP3a/blrvh.Getting.Started.with.Augmented.Analytics.in.Sisense.rar.html No Password - Links are Interchangeable

Free Download Certification In Key Business Analytics And Data Analytics Last updated 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.34 GB | Duration: 12h 9m Key Business Analytics 40 + concepts like AB testing, Visual, Correlation, Scenario, Forecasting, Data mining more What you'll learn You will learn the Introduction to the Key Business Analytics including the raw material - data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics. You will be able to learn Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations. Linear programming. Cohort analysis. Factor analysis. Neural network analysis. Meta analytics literature analysis. Learn about the details related to Qualitative surveys. Focus groups (. Interviews and ethnography. Learn Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. Cash flow analysis. Value driver analytics. Shareholder value analytics. Market analytics. Market size analytics. Discover how to get the knowledge of Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics. Requirements You should have an interest in Key Business Analytics and data driven management Basic understanding of business and different requirements to run an organization Basic communication skill and proficiency in office package Description DescriptionTake the next step in your career! Whether you're an up-and-coming professional, an experienced executive, aspiring manager, budding Professional. This course is an opportunity to sharpen your Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations., increase your efficiency for professional growth and make a positive and lasting impact in the business or organization.With this course as your guide, you learn how to:All the basic functions and skills required key business analytics.Transform the Key Business Analytics including the raw material - data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics.Get access to recommended templates and formats for the detail's information related to key business analytics. Learn to Qualitative surveys. Focus groups (. Interviews and ethnography. Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. are presented as with useful forms and frameworksInvest in yourself today and reap the benefits for years to comeThe Frameworks of the CourseEngaging video lectures, case studies, assessment, downloadable resources and interactive exercises. This course is created to learn the Introduction to the Key Business Analytics including the raw material - data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics. Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations. Linear programming. Cohort analysis. Factor analysis. Neural network analysis. Meta analytics literature analysis. Analytics inputs tools or data collection methodsThe details Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. Cash flow analysis. Value driver analytics. Shareholder value analytics. Market analytics. Market size analytics. Demand forecasting. Market trends analytics. Non- customer analytics.The course includes multiple Case studies, resources like formats-templates-worksheets-reading materials, quizzes, self-assessment, film study and assignments to nurture and upgrade your of Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics in details.In the first part of the course, you'll learn the details of Introduction to the Key Business Analytics including the raw material - data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics. Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations. Linear programming.In the middle part of the course, you'll learn how to develop a knowledge of The , Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. Cash flow analysis. Value driver analytics. Shareholder value analytics. Market analytics. Market size analytics. Demand forecasting. Market trends analytics. Non- customer analytics.In the final part of the course, you'll develop the Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics. Course Content:Part 1Introduction and Study Plan· Introduction and know your Instructor· Study Plan and Structure of the Course1. Introduction1.1 Details of Introduction1.2. The raw materials -Data1.3. Data types and format1.4. How to use this 1.5. Who is this for?2. Business experiments or experimental design or AB testing2.1. What is it?2.2. What business questions is it helping me to answer2.3. Create a hypothesis2.4. Design the experiment2.5. Tips and traps3. Visual analytics4. Correlation analysis5. Scenario analysis6. Forecasting or Time7. Data mining8. Regression analysis9. Text analytics10. Sentiment analysis11. .Image Analytics12. Video analytics13. .Voice analytics14. Monte Carlo simulations15. . Linear programming16. Cohort analysis17. Factor analysis18. Neural network analysis19. Meta analytics literature analysis20. Analytics inputs tools or data collection methods21. Qualitative surveysPart 222. Focus groups23. Interviews24. Ethnography25. Test capture26. . Image capture27. Sensor date28. Machine data capture29. Financial analytics30. Customer profitability analytics31. Product Profitability32. Cash flow analysis33. Value driver analytics34. Shareholder value analytics35. Market analytics36. Market size analytics37. Demand forecasting38. Market trends analytics39. Non- customer analytics40. Competitor analytics41. Pricing analytics42. Marketing channel43. Brand analytics44. Customer analytics45. Customer lifetime Overview Section 1: Introduction to Key Business Analytics Lecture 1 Introduction and Study Plan Lecture 2 1.1. Details of Introduction Lecture 3 1.2. The raw materials -Data Lecture 4 1.3. Data types and format Lecture 5 1.4. How to use this Lecture 6 1.5. Who is this for? Section 2: 2. Business experiments or experimental design or AB testing Lecture 7 2.1. What is it? Lecture 8 2.2. What business questions is it helping me to answer Lecture 9 2.3. Create a hypothesis Lecture 10 2.4. Design the experiment Lecture 11 2.5. Tips and traps Section 3: 3. Visual analytics Lecture 12 3.1. What is it Lecture 13 3.2. What business questions is it helping me to answer Section 4: 4. Correlation analysis Lecture 14 4.1 Correlation analysis Lecture 15 4.2. What business questions is it helping me to answer Section 5: 5. Scenario analysis Lecture 16 5.1 Scenario analysis Lecture 17 5.2. What business questions is it helping me to answer Section 6: 6. Forecasting or Time Lecture 18 6.1 Forecasting or Time Lecture 19 6.2. What business questions is it helping me to answer Section 7: 7. Data mining Lecture 20 7.1 Data mining Lecture 21 7.2. What business questions is it helping me to answer Section 8: 8. Regression analysis Lecture 22 8.1 Regression analysis Lecture 23 8.2. What business questions is it helping me to answer Section 9: 9. Text analytics Lecture 24 9.1 Text analytics Section 10: 10. Sentiment analysis Lecture 25 10.1 Sentiment analysis Section 11: 11. Image Analytics Lecture 26 11.1 Image Analytics Section 12: 12. Video analytics Lecture 27 12.1 Video analytics Lecture 28 12.2 How do I use it? Section 13: 13. Voice analytics Lecture 29 13.1 Voice analytics Section 14: 14. Monte Carlo simulations Lecture 30 14.1 Monte Carlo simulations Section 15: 15. Linear programming Lecture 31 15.1 Linear programming Lecture 32 15.2 How do I use it? Section 16: 16. Cohort analysis Lecture 33 16.1 Cohort analysis Section 17: 17. Factor analysis Lecture 34 17.1 Factor analysis Lecture 35 17.2 Tips and Traps? Section 18: 18. Neural network analysis Lecture 36 18.1 Neural network analysis Lecture 37 18.2 How do I use it? Section 19: 19. Meta analytics literature analysis Lecture 38 19.1 Meta analytics literature analysis Lecture 39 19.2 Tips and Traps Section 20: 20. Analytics inputs tools or data collection methods Lecture 40 20.1 Analytics inputs tools or data collection methods Section 21: 21. Qualitative surveys Lecture 41 21.1 Qualitative surveys Section 22: 22. Focus groups Lecture 42 22.1 Focus groups Section 23: 23. Interviews Lecture 43 23.1 Interviews Section 24: 24. Ethnography Lecture 44 24. Ethnography Section 25: 25. Test capture Lecture 45 25.1 Test capture Lecture 46 25.2 How can I use it? Section 26: 26. Image capture Lecture 47 26.1 Image capture Section 27: 27. Sensor date Lecture 48 27.1 Sensor date Lecture 49 27.2. Possible data Sources Section 28: 28. Machine data capture Lecture 50 28.1 Machine data capture Lecture 51 28.2. Why does it matter Lecture 52 28.3. How do I get started? Section 29: 29. Financial analytics Lecture 53 29.1 Financial analytics Section 30: 30. Customer profitability analytics Lecture 54 30.1 Customer profitability analytics Lecture 55 30.2. Why does it matter? Section 31: 31. Product Profitability Lecture 56 31.1 Product Profitability Lecture 57 31.2. Tips and traps Section 32: 32. Cash flow analysis Lecture 58 32.1 Cash flow analysis Lecture 59 32.2. How do I use it Section 33: 33. Value driver analytics Lecture 60 33. Value driver analytics Lecture 61 33.2. Why does it matter Section 34: 34. Shareholder value analytics Lecture 62 34.1 Shareholder value analytics Section 35: 35. Market analytics Lecture 63 35.1 Market analytics - Unmet need analytics Lecture 64 35.2. Why does it matter Lecture 65 35.3. Tips and traps Section 36: 36. Market size analytics Lecture 66 36.1 Market size analytics Section 37: 37. Demand forecasting Lecture 67 37.1 Demand forecasting Lecture 68 37.2. How do I use it Section 38: 38. Market trends analytics Lecture 69 38.1 Market trends analytics Lecture 70 38.2. Tips and traps Section 39: 39. Non- customer analytics Lecture 71 39.1 Non- customer analytics Lecture 72 39.2. Why does it matter? Section 40: 40. Competitor analytics Lecture 73 40.1 Competitor analytics Section 41: 41. Pricing analytics Lecture 74 41.1 Pricing analytics Section 42: 42. Marketing channel Lecture 75 42.1 Marketing channel Section 43: 43. Brand analytics Lecture 76 43.1 Brand analytics Section 44: 44. Customer analytics Lecture 77 44.1 Customer analytics Section 45: 45. Customer lifetime Lecture 78 45.1 Customer lifetime Lecture 79 45.2. Why does it matter? Section 46: Assignment Part Lecture 80 Assignment Part Existing executive board directors, managing directors who is looking to get more engagement and innovation from their teams and organizations,Any managers or aspiring managers wants to understand business with different data analysis,Any professional wants to be a Business Analyst or Data Analyst Homepage https://www.udemy.com/course/certification-in-key-business-analytics-and-data-analytics/ Rapidgator https://rg.to/file/f036528e871b19928316e531ed1a381f/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part1.rar.html https://rg.to/file/7b472d4eb477cdcd530870dd60b28fe6/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part2.rar.html https://rg.to/file/ae43283bde2c6f86a3c69be2931d6271/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part3.rar.html https://rg.to/file/1a00c631188d9fc0c6c9deec24fd17bc/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part4.rar.html Fikper Free Download https://fikper.com/ygV1IrHX1B/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part1.rar.html https://fikper.com/GjreTWRvZx/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part2.rar.html https://fikper.com/lSjpDPhMtZ/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part3.rar.html https://fikper.com/zioaNyXgAO/qcwpb.Certification.In.Key.Business.Analytics.And.Data.Analytics.part4.rar.html No Password - Links are Interchangeable

Free Download Business Analytics Bootcamp - Complete Mastery Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 913.41 MB | Duration: 1h 6m Learn 424 ways in which you can analyze metrics generated by the 7 critical business functions in any business What you'll learn 424 different ways to analyze metrics generated by different business functions 108 ways to analyze metrics generated by the Distribution Function in any business 76 ways to analyze metrics generated by the Marketing Function in any business 74 ways to analyze metrics generated by the Sales Function in any business 64 ways to analyze metrics generated by the Accounting Function in any business 45 ways to analyze metrics generated by the Customer Support Function in any business 31 ways to analyze metrics generated by the Human Resources Function in any business 23 ways to analyze metrics generated by the Sourcing & Procurement Function in any business 3 important statements to analyze metrics generated by the Finance Function in any business Become Cross-functional experts and professionals Requirements No prior Business academic experience required No prior Analytics experience required Description This is not just another Business Analytics course. This course focuses more on WHAT different metrics generated by a business day in and day out must be analyzed to comprehend business performance rather than HOW. If we don't know WHAT to measure, will there be anything to improve? In this course, I have handpicked 424 different ways to analyze metrics generated by different business functions. In this course, learners will comprehend -108 ways to analyze metrics generated by the Distribution Function in any business76 ways to analyze metrics generated by the Marketing Function in any business74 ways to analyze metrics generated by the Sales Function in any business64 ways to analyze metrics generated by the Accounting Function in any business45 ways to analyze metrics generated by the Customer Support Function in any business31 ways to analyze metrics generated by the Human Resources Function in any business23 ways to analyze metrics generated by the Sourcing & Procurement Function in any business3 important statements to analyze metrics generated by the Finance Function in any businessThis course will enable learners to become Cross-functional experts and professionalsNo prior business academic experience or prior analytics experience is required for this course.The audience for this course ranges from -Technology Professionals & Technology StudentsBusiness Professionals and Business studentsEntrepreneurs and Business OwnersTechnology CXOs, Leaders, and ManagersEngineering Professionals & Engineering StudentsBudding EntrepreneursStartup Founders, CXOs, Leaders and ManagersData Analysts, Business Analysts, and Data professionalsTo anyone who wants to learn about Business Analytics per se. Overview Section 1: Introduction to Business Analytics Mastery Lecture 1 Introduction Section 2: Marketing Analytics Lecture 2 Marketing Analytics Mastery Section 3: Sales Analytics Lecture 3 Sales Analytics Mastery Section 4: HR Analytics Lecture 4 HR Analytics Mastery Section 5: Financial Analytics Lecture 5 Financial Analytics Mastery Section 6: Accounting Analytics Lecture 6 Accounting Analytics Mastery Section 7: Customer Support Analytics Lecture 7 Customer Support Analytics Mastery Section 8: Distrbution Analytics Lecture 8 Distribution Analytics Mastery Section 9: Sourcing & Procurement Analytics Lecture 9 Sourcing & Procurement Analytics Mastery Section 10: Concluding Suggestions Lecture 10 Concluding Suggestions Technology Professionals & Technology Students,Business Professionals and Business students,Entrepreneurs and Business Owners,Technology CXOs, Leaders and Managers,Engineering Professionals & Engineering Students,Budding Entrepreneurs,Startup Founders, CXOs, Leaders and Managers,Data Analysts, Business Analysts and Data professionals Homepage https://www.udemy.com/course/businessanalyticsmastery/ Rapidgator https://rg.to/file/92afb3cff0d045f49e483d77986a45b7/mwqtd.Business.Analytics.Bootcamp.Complete.Mastery.rar.html Fikper Free Download https://fikper.com/mmFLc5WQ6A/mwqtd.Business.Analytics.Bootcamp.Complete.Mastery.rar.html No Password - Links are Interchangeable

pdf | 51.28 MB | English| Isbn:9781119446019 | Author: David Karlins, Eric Matisoff | Year: 2019 Description: Category:Computers, Business, Applications & Software, Enterprise Computing Systems, Marketing & Sales, Marketing, Business Software, Enterprise Computing - General & Miscellaneous, Marketing - General & Miscellaneous, Marketing - Professional & Reference https://ddownload.com/21oy84i01tvs https://rapidgator.net/file/998220d7c3b3dace9a77041244de56f5/ https://turbobit.net/x72euzpnyi8u.html

Free Download Data Analytics Mastery Published 9/2024 Created by Yodaafy Academy MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 114 Lectures ( 35h 38m ) | Size: 19.4 GB A Complete Data Analytics Course on Python, SQL, PowerBi, Tableau Advanced Excel and more.. What you'll learn: Gain a comprehensive understanding of data analytics concepts, including data collection, cleaning, and processing. Learn how to use industry-standard tools like Python, R, SQL, and Excel for data analysis. Master the art of visualizing data using tools like Tableau and Power BI to present insights effectively. Develop a strong foundation in statistical methods and how they apply to data analytics. Work on real-world projects that simulate industry challenges, giving you practical experience. Understand the concepts of big data and learn how to work with large datasets using tools like Hadoop and Spark. Learn various data mining techniques to discover patterns and relationships in large datasets. Understand predictive modeling techniques to forecast future trends based on historical data. Develop the ability to make informed business decisions using data-driven insights. Learn about the ethical issues related to data privacy and security. Enhance your Excel skills, including data manipulation, advanced formulas, and pivot tables. Learn how to create interactive dashboards to monitor and present data in real-time. Improve your ability to communicate data insights clearly to non-technical stakeholders. Prepare for a career in data analytics with guidance on resume building, interview preparation, and job search strategies. Requirements: No prior experience is required. We will start from the very basics Description: The Importance of Data Analytics in Today's WorldIn the modern era, data has become the lifeblood of decision-making, innovation, and competitive advantage. Data analytics, the practice of examining raw data to extract meaningful insights, is essential for businesses, governments, and organizations across the globe. By transforming vast amounts of data into actionable intelligence, data analytics enables informed decision-making that drives success. It empowers organizations to move beyond intuition, using factual analysis to identify opportunities, mitigate risks, and adapt to changing market conditions with greater precision.Data analytics also plays a crucial role in enhancing operational efficiency. By examining data from various sources-such as supply chains, customer interactions, and internal processes-businesses can identify inefficiencies and optimize their operations. This leads to cost savings, better resource management, and improved customer satisfaction, all of which contribute to a company's bottom line. Moreover, the predictive power of data analytics allows organizations to anti[beeep]te future trends and behaviors, positioning them to be proactive rather than reactive in their strategies.Beyond the business world, data analytics is increasingly being used to address social and ethical challenges. Governments and non-profits leverage data to tackle issues like public health, climate change, and social inequality. As technology continues to evolve, the importance of data analytics will only grow, making it a vital skill for navigating the complexities of the future. In essence, data analytics is not just a tool for understanding the present but a powerful means to shape the future.Why Choose YodaafyAt Yodaafy, we believe in learning by doing. Our course is designed with a hands-on approach that ensures you not only understand theoretical concepts but also know how to apply them in real-world situations. With expert instructors, a supportive learning environment, and a curriculum aligned with industry needs, Yodaafy is your gateway to a successful career in data analytics.Course Includes 30+ Hours of on-Demand video 200+ Coding Exercises Downloadable resourcesCertificate of CompletionObjectivesDevelop Foundational KnowledgeEnhance Technical SkillsApply Data-Driven Decision MakingFoster Critical Thinking and Problem-SolvingWhat We Will Cover in the Yodaafy Data Analytics CourseThe Yodaafy Data Analytics course is designed to provide a comprehensive understanding of data analytics through a hands-on approach. Below is an overview of the key topics covered in the course:Microsoft Excel for Data AnalyticsExcel Fundamentals: Learn the basics of Excel, including navigating the interface, customizing toolbars, and understanding workbook structures.Data Entry and Formulas: Gain proficiency in entering and editing text, working with numeric data, and creating basic formulas with relative and absolute references.Excel Functions: Master key functions such as SUM, MIN, MAX, AVERAGE, and COUNT, and explore the use of AutoSum and AutoFill commands.Worksheet Management: Discover how to modify worksheets, including moving/copying data, inserting/deleting rows and columns, and formatting data with font, borders, and conditional formatting.SQL for Data ManagementMySQL Basics: Start with the fundamentals of MySQL, including database creation, table management, and basic data manipulation tasks like inserting, updating, and deleting records.Advanced SQL Functions: Delve into more complex SQL operations such as DISTINCT, ORDER BY, LIMIT, and GROUP BY, as well as working with subqueries.Joins and Relationships: Learn how to perform INNER, LEFT, RIGHT, and FULL OUTER joins, and understand how to manage one-to-many and many-to-many relationships in databases.Python for Data AnalyticsPython Basics: Begin with Python programming fundamentals, covering variables, data types, operators, lists, dictionaries, and control flow with if-else statements and loops.Data Manipulation with Python: Explore Python's powerful data manipulation libraries like NumPy and Pandas, and learn how to visualize data using Matplotlib.Machine Learning Introduction: Get introduced to machine learning concepts, including regression analysis, decision trees, and data distribution techniques.Power BI for Data VisualizationPower BI Fundamentals: Understand the importance of data visualization, learn the basics of Power BI, and how it compares to other tools like Tableau.Data Import and Cleaning: Learn how to import datasets into Power BI, clean and format data, and use Power BI's Query Editor to manipulate datasets.Advanced Power BI Techniques: Explore advanced Power BI features such as creating data models, merging/joining tables, and using DAX functions for deeper analysis.Tableau for Data VisualizationTransforming Data into Visualizations: Enable learners to transform raw data into meaningful visualizations that drive business decisions.Data Connection and Cleaning: Cover the basics of connecting to data sources and cleaning datasets.Creating Visual Representations: Teach learners to create complex visual representations, including heat maps, bar charts, and line graphs.Interactive Dashboards and Reports:By the end of the module, learners will be able to create interactive dashboards and reports.Effective Communication of Insights: Equip learners with the skills to effectively communicate data insights to stakeholders.This curriculum ensures that you gain a solid foundation in data analytics, with practical skills in the most widely used tools in the industry. Whether you're aiming to enhance your current role or pivot to a new career, this course equips you with the essential knowledge to succeed in the data-driven world. Who this course is for: Aspiring Data Analysts: If you're new to data analytics, this course will provide you with the foundational skills to start your career. Working Professionals: Enhance your existing skills and stay ahead in your field by learning the latest data analytics techniques. Business Professionals: Learn how to incorporate data-driven decision-making into your strategic planning processes. 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Free Download Complete Cognos Analytics Training Course (2023) Last updated 3/2023 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.68 GB | Duration: 5h 43m Cognos Analytics What you'll learn This course is designed to make you an expert on Cognos Analytics. You will learn how to Develop Dash Boards using KPIs and consolidated data points Get Free Access to Cognos Analytics - IBM Cloud Account Prepare data for Reporting & Dash-Boarding Upload Your Own data into Cognos Analytics and Understanding it through Analytics' Exploration Tool Use of Filters, Prompts and Functions Develop Complex Reporting using Master/Detail & Drill-Through techniques Develop Simple and then Complex Reports Requirements No programming or IT background required. Just willingness to Learn Business Intelligence Technology especially Cognos Analytics Description IBM Cognos Analytics is one of the largest and successful business intelligence software used for reports and dashboard development. It is a very simple and straight forward tool that does not require prior IT knowledge. In this course you will learn everything COGNOS ANALYTICS offers. I have been teaching Cognos in a classroom environment in New York City for the past 10 years. It is important to note that close to 90% of my students got jobs and are enjoying their successful careers in Business Intelligence. Over 75% of these student had zero prior knowledge of Cognos or IT except Internet surfing. I want you to imagine yourself in that role - It will be a great journey indeed.The course is designed to make you an expert on Cognos Analytics and all of its features. You will be able to accomplish following with ease:Course outline 11.2.4Introduction - IntroductionSyllabus OutlineModule -1: Get access to Cognos Analytics- Access to Analytics- Upload and Prepare data- ExplorationModule - 2: Development- Create Reports- Report FormattingModule - 3: More Development & Organization- Sorting- Grouping n Sectioning- CalculationsModule - 4: Deep Dive Development- Deep dive- Filters- Cross-TabsModule - 5: Charts- VisualizationModule - 6: Prompts- Prompts Overview- Text Box Prompt- Value Prompt- Select & Search Prompt- Date Prompt- Cascading PromptsModule - 7: Functions, Master Detail & Drill-through Reporting- Functions- Master/Detail- Drill-Through ReportsModule - 8: Dash-boarding- Building a DashboardI have also included a module on successfully finding a Cognos Business Intelligence Job which will definitely help you finding your dream IT job. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Course Outline Section 2: Introduction to Cognos Analytics Lecture 3 Analytics Overview Lecture 4 How to Launch Lecture 5 Interface Explained Section 3: Get access to Cognos Analytics Lecture 6 Access to Analytics Lecture 7 Upload and Prepare data Lecture 8 Exploration Section 4: Development Lecture 9 Create Reports Lecture 10 Report Formatting Section 5: More Development & Organization Lecture 11 Sorting Lecture 12 Grouping n Sectioning Lecture 13 LAB Lecture 14 Calculations Lecture 15 Calculations LAB Section 6: Deep Dive into Development Lecture 16 Deep dive Lecture 17 Filters Lecture 18 Cross-Tab Multi-Dimensional Reporting Section 7: Charts Lecture 19 Visualization Section 8: Prompts Lecture 20 Prompts Overview Lecture 21 Text Box Prompt Lecture 22 Text Box Prompt LAB Lecture 23 Value Prompt Lecture 24 Value Prompt LAB Lecture 25 Select & Search Prompt Lecture 26 Date Prompt Lecture 27 Cascading Prompts Section 9: Functions, Master Detail & Drill-through Reporting Lecture 28 Functions Lecture 29 Date Related Functions Lecture 30 Master/Detail Reporting Lecture 31 Master/Detail LAB Lecture 32 Drill-Through Reports Section 10: Dash Boards Lecture 33 Building a Dashboard Section 11: Landing a Job Lecture 34 Resume Lecture 35 Job Search Lecture 36 Applying and Follow-ups Anyone interested in business intelligence and data analytics without having programming background including professionals in Management business, Finance, and marketing. Also People with legacy technical skills who want to learn new technology. Additionally, College students who want a career in IT, but did not take IT classes during school years. - Any individual who wants to get an IT job with little to no IT experience. 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