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Free Download R Programming For Data Science- Practise 250 Exercises-Part2 Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 644.41 MB | Duration: 3h 0m Level Up Your Skills: Advanced Challenges & Expert Insights in R Programming! What you'll learn Develop a strong foundation in R programming by solving diverse exercises, reinforcing key concepts like data types, control structures, and functions. Gain hands-on experience with popular R libraries such as dplyr, ggplot2, tidyverse, and caret to manipulate and visualize datasets effectively. Apply data wrangling techniques to clean, transform, and organize real-world datasets using R. Master data visualization by creating insightful and professional-quality plots with ggplot2 and other visualization libraries. Enhance your statistical analysis skills by performing descriptive statistics, hypothesis testing, and regression analysis in R. Explore different datasets available in R and use them to practice machine learning algorithms such as linear regression, classification, and clustering. Debug and optimize R code by identifying common errors and applying best practices for efficient coding. Prepare for real-world data science challenges by solving exercises that reflect common tasks in data analysis and machine learning projects. Requirements Basic understanding of R programming: Familiarity with R syntax, variables, data types, and basic functions. Introduction to data structures in R: Knowledge of common data structures like vectors, data frames, and lists. Passion to become Data Scientist Internet connection and Laptop Description Welcome to R Programming for Data Science - Practice 250 Exercises: Part 2! If you're ready to take your R programming skills to the next level, this course is the ultimate hands-on experience you've been waiting for. Designed for data enthusiasts, aspiring data scientists, and R programmers, this course brings you 250 brand-new challenges that will deepen your understanding of R programming, data analysis, and machine learning.Whether you're continuing from Part 1 or just starting here, this course promises to engage, challenge, and refine your skills in real-world applications of R. Dive into problem-solving scenarios, practice advanced techniques, and get ready to supercharge your data science career!10 Reasons Why You Should Enroll in This Course:250 New Exercises: Gain practical, hands-on experience with 250 fresh challenges that will test your R programming skills.Real-World Data Science Scenarios: Solve exercises designed to mimic real data science problems, giving you valuable experience that you can apply in your job.Advanced R Concepts: This course builds on foundational R knowledge, introducing more advanced topics such as data visualization, statistical analysis, and machine learning.Project-Based Learning: Learn by doing! Each exercise is a mini-project that will help you understand complex concepts in a simple, practical way.Self-Paced Learning: Enjoy the flexibility to learn at your own speed, whether you're a full-time student or a working professional.Skill-Building for Data Science: Strengthen your R programming and data science abilities, making you more competitive in the job market.Instant Feedback & Solutions: Get access to detailed solutions and explanations for each exercise, so you can learn from your mistakes and improve rapidly.Perfect for Career Growth: Whether you're aiming for a data scientist, analyst, or R programming role, this course will provide the expertise you need to succeed.Expand Your Data Science Toolkit: Learn to use R effectively for data manipulation, analysis, and visualization, essential tools for any data science professional.Supportive Learning Environment: Benefit from an active Q&A section and a community of learners who are just as passionate about data science as you are.Enroll now and take your R programming skills to the next level with R Programming for Data Science - Practice 250 Exercises: Part 2! Overview Section 1: Introduction Lecture 1 Welcome to the Course Lecture 2 Introduction to AI and ML Lecture 3 Introduction to R Programming Lecture 4 Art of Good Programming Lecture 5 Course Overview Section 2: 251-270 Lecture 6 Problem 251 Lecture 7 Soln 251 Lecture 8 Problem 252 Lecture 9 Soln 252 Lecture 10 Problem 253 Lecture 11 Soln 253 Lecture 12 Problem 254 Lecture 13 Soln 254 Lecture 14 Problem 255 Lecture 15 Soln 255 Lecture 16 Problem 256 Lecture 17 Soln 256 Lecture 18 Problem 257 Lecture 19 Soln 257 Lecture 20 Problem 258 Lecture 21 Soln 258 Lecture 22 Problem 259 Lecture 23 Soln 259 Lecture 24 Problem 260 Lecture 25 Soln 260 Lecture 26 Problem 261 Lecture 27 Soln 261 Lecture 28 Problem 262 Lecture 29 Soln 262 Lecture 30 Problem 263 Lecture 31 Soln 263 Lecture 32 Problem 264 Lecture 33 Soln 264 Lecture 34 Problem 265 Lecture 35 Soln 265 Lecture 36 Problem 266 Lecture 37 Soln 266 Lecture 38 Problem 267 Lecture 39 Soln 267 Lecture 40 Problem 268 Lecture 41 Soln 268 Lecture 42 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Lecture 479 Soln 487 Lecture 480 Problem 488 Lecture 481 Soln 488 Lecture 482 Problem 489 Lecture 483 Soln 489 Lecture 484 Problem 490 Lecture 485 Soln 490 Section 14: 491-500 Lecture 486 Problem 491 Lecture 487 Soln 491 Lecture 488 Problem 492 Lecture 489 Soln 492 Lecture 490 Problem 494 Lecture 491 Soln 494 Lecture 492 Problem 495 Lecture 493 Soln 495 Lecture 494 Problem 496 Lecture 495 Soln 496 Lecture 496 Problem 497 Lecture 497 Soln 497 Lecture 498 Problem 498 Lecture 499 Soln 498 Lecture 500 Problem 499 Lecture 501 Soln 499 Lecture 502 Problem 500 Lecture 503 Soln 500 Aspiring Data Scientists: Those looking to build a strong foundation in R programming while solving real-world data science problems.,Students and Academics: Learners studying data science or related fields who want hands-on practice with R and its various libraries and datasets.,Professionals in Data-Driven Roles: Individuals working in fields like business analytics, finance, healthcare, or marketing who want to enhance their data analysis skills using R.,Self-Learners and Coding Enthusiasts: Those passionate about learning R programming through practical exercises and improving their coding proficiency in data science projects. 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Free Download Microsoft Azure Data Engineer Associate (DP-203) Cert Prep by Microsoft Press (2024) Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 9h 27m | Size: 1.2 GB In this course, Microsoft MVP Tim Warner walks you through what to expect on the DP-203 Data Engineering on Microsoft Azure exam, covering every Exam DP-203 objective in a friendly and logical way. Tim dives into the intricacies of data engineering on Microsoft Azure, focusing on deploying efficient, secure, and robust data processing solutions. Learn how to design and implement diverse data storage strategies, including leveraging Azure Synapse Analytics for managing massive datasets efficiently. Discover techniques for data compression, partitioning, and sharding to optimize storage and access speed. Investigate table geometries, data redundancy, and archival methods to ensure data is both accessible and protected. Ideal for IT professionals, data scientists, and anyone interested in the data engineering capabilities of Azure, this course empowers you to build scalable data solutions and ensure that your data-driven applications perform seamlessly. Homepage https://www.linkedin.com/learning/microsoft-azure-data-engineer-associate-dp-203-cert-prep-by-microsoft-press TakeFile https://takefile.link/4awlt879y3zo/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part1.rar.html https://takefile.link/pctsqp8vi8gv/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part2.rar.html Rapidgator https://rg.to/file/cc0186b89583e07896400d88069c047d/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part1.rar.html https://rg.to/file/25dd1f0ed33d3397d99928c84a2c6b7c/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part2.rar.html Fikper Free Download https://fikper.com/6NWouRDucs/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part1.rar.html https://fikper.com/CT77ZHofs9/httrn.Microsoft.Azure.Data.Engineer.Associate.DP203.Cert.Prep.by.Microsoft.Press.2024.part2.rar.html No Password - Links are Interchangeable

Free Download Data Visualization - How to Choose the Right Graph Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: General | Genre: eLearning | Language: English + srt | Duration: 44m | Size: 169 MB It is easy for data insights to get lost in a convoluted graph. In this course, data expert Rebeca Pop guides you through the nuances of selecting graph types that best convey your data. Rebeca shares how to understand your target audience and choose graphs that will resonate with them. She explores helpful tools such as RAWGraphs and Figma for sketching and designing custom graphs. After this course, you'll be able to choose graphs for compelling and effective data communication. Homepage https://www.linkedin.com/learning/data-visualization-how-to-choose-the-right-graph TakeFile https://takefile.link/5873w05nl4n0/zgoet.Data.Visualization.How.to.Choose.the.Right.Graph.rar.html Rapidgator https://rg.to/file/dcc40be9719eb29805a7e7913f2aece1/zgoet.Data.Visualization.How.to.Choose.the.Right.Graph.rar.html Fikper Free Download https://fikper.com/COdn6UUp92/zgoet.Data.Visualization.How.to.Choose.the.Right.Graph.rar.html No Password - Links are Interchangeable

Free Download Data Platforms - Spark to Snowflake Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 54m | Size: 96 MB Explore the expansive realm of big data in this course from industry leader Pragmatic AI Labs. Instructor Kennedy Behrman covers the essentials of big data processing on key platforms like Apache Hadoop, Apache Spark, and Snowflake. Throughout the course, learn how to work with resilient distributed datasets (RDDs) and leverage Spark SQL for managing dataframes. Delve into Snowflake to understand its unique architecture, navigate its web UI, and utilize its Python connector for data manipulation. Along the way, engaging demos provide opportunities for you to get hands-on experience with each platform, ensuring you not only grasp the theoretical concepts but also how to apply them in practical scenarios. Whether you're a data scientist, a data analyst, an IT professional, or simply eager to learn more about big data, this course offers valuable insights and skills on some of the most cutting-edge data management technologies available today. Homepage https://www.linkedin.com/learning/data-platforms-spark-to-snowflake TakeFile https://takefile.link/yvva6z6adlp2/bzfwb.Data.Platforms.Spark.to.Snowflake.rar.html Rapidgator https://rg.to/file/fd65513876ac5d64a3395b469045cb22/bzfwb.Data.Platforms.Spark.to.Snowflake.rar.html Fikper Free Download https://fikper.com/OBzpli8knd/bzfwb.Data.Platforms.Spark.to.Snowflake.rar.html No Password - Links are Interchangeable

Free Download Data Analytics Beginner Course (2024) Published 9/2024 Created by Rifkat Galeev MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 6 Lectures ( 29m ) | Size: 583 MB Learn Data Analytics Fundamentals: Master Databases, SQL, and Data Transformation Techniques for Beginners What you'll learn: Understand Data Analytics Basics: Learn the importance of data analytics and explore different types of data analysis methods. Master Relational Database Concepts: Gain foundational knowledge of relational databases and data warehouses for effective data management. Learn SQL Fundamentals: Understand SQL syntax, retrieve data with SELECT statements, and filter data using WHERE clauses. Perform Data Transformation: Learn essential data cleaning techniques to prepare data for analysis and reporting. Requirements: This course is designed for beginners, so there are no specific prerequisites. Basic computer skills and an interest in working with data are all you need. You don't need any prior experience with databases, SQL, or data analytics. A willingness to learn and explore data-driven insights is the only requirement to get started! Description: Welcome to the Data Analytics Beginner course! This course is designed for anyone looking to start a journey in data analytics, with no prior experience required. Over the course of 6 lectures, you will learn the fundamentals of data analytics, including relational database concepts, data modeling, SQL basics, and data transformation techniques. We will start with an introduction to data analytics, exploring its importance and the various types used across industries.Next, you'll dive into relational database concepts, where you'll learn how data is stored and managed in databases, including an introduction to data warehouses. You'll then move on to data modeling and database design, where you'll gain hands-on experience in planning and structuring data effectively for analysis.In the SQL fundamentals section, you'll learn how to write SQL queries to retrieve and filter data, mastering essential commands like SELECT statements and WHERE clauses. Finally, you'll explore data transformation techniques, focusing on cleaning and preparing data for accurate analysis and reporting.The course also includes practical case studies and a workshop to reinforce your learning through real-world scenarios. By the end of this course, you'll have a solid foundation in data analytics, enabling you to make data-driven decisions and set the stage for more advanced analytics studies. Whether you're a student, professional, or just curious about data, this course will equip you with the essential skills needed to begin working with data confidently and effectively. Who this course is for: This course is ideal for anyone interested in starting a career in data analytics or looking to build foundational skills in working with data. It's perfect for beginners with no prior experience in databases or SQL, as well as professionals from other fields who want to learn the basics of data analysis to enhance their decision-making and reporting skills. Whether you're a student, career changer, or a business professional aiming to add data analytics to your skill set, this course will provide you with the essential knowledge to begin your journey in data analytics. Homepage https://anonymz.com/https://www.udemy.com/course/data-analytics-beginner-course/ Rapidgator https://rg.to/file/591ea18f6dff79560ab7062ee6c7cc76/idzlp.Data.Analytics.Beginner.Course.2024.rar.html Fikper Free Download https://fikper.com/9jcXJxpe8W/idzlp.Data.Analytics.Beginner.Course.2024.rar.html No Password - Links are Interchangeable

Free Download Advanced Power BI - Expert Data Analysis and Visualization Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 7h 38m | Size: 5.46 GB From Data to Decisions with Advanced Modeling Techniques What you'll learn Understand the Power BI Data Cycle: Learn how to gather, transform, consolidate, and enrich data for effective visualization and sharing. Develop Robust Data Models: Learn how to create and manage fact tables, dimension tables, and role-playing dimensions for comprehensive data modeling. Master Data Transformation Techniques: Gain expertise in using advanced transformation methods, including fuzzy matching and complex data set adjustments. Apply Role-Level Security: Learn to implement role-level security settings to control data access effectively. Conduct Data-driven Storytelling: Develop the ability to use Power BI to tell compelling data stories, highlighting key insights and trends. Requirements Learners should have a fundamental understanding of Power BI's interface, including how to create basic reports and dashboards. Learners should have Power BI Desktop installed on their computer to practice and follow along with course exercises. Description This Advanced Power BI course is meticulously designed to equip professionals with the expertise needed to master data analytics and visualization at an advanced level. By delving into critical aspects such as data transformation, modeling, and visualization, this course ensures you gain comprehensive skills to handle complex data scenarios effectively. Parti[beeep]nts will learn to connect and consolidate data from diverse sources, automate data processes, and build robust data models. The course also covers advanced topics like role-level security, fuzzy matching, and the creation of transformation tables, enabling you to manage and protect data with confidence.Taking this course will provide you with practical, hands-on experience through real-world applications and case studies. You will learn to create insightful reports and compelling visualizations that drive informed decision-making. By the end of the course, you will be equipped not only with advanced technical skills but also with the ability to apply these techniques to solve business problems and optimize data-driven strategies. This course is ideal for professionals looking to elevate their Power BI capabilities and leverage data analytics to achieve business success.Course Outline:Introduction to Advanced Power BI CourseIntroduction to the trainerOverview of the courseCommon challenges in mastering Power BIImportance of core conceptsData Cycle: Getting DataStarting with a vision and end goalsIdentifying data sourcesConnecting to disparate systemsCentralized data warehousesMethods for importing dataData Cycle: Data TransformationImportance of data transformationCommon data issuesAutomating data transformationData wrangling and mungingData Cycle: Data ConsolidationImportance of data consolidationData flattening vs. data modelingBenefits of data modelingHandling large datasetsData Cycle: Enrichment, Visualization & SharingData enrichment techniquesCreating compelling visualizationsEffective data sharing methodsData Transformation: Finding Problems & Understanding Column ProfileIdentifying data problemsUnderstanding column profilesUsing data profiling toolsData Transformation: Fuzzy MatchConcept of fuzzy matchingImplementing fuzzy matching in Power BIHandling data quality issuesData Transformation: Transformation Table with Fuzzy MatchCreating transformation tablesUsing transformation tables with fuzzy matchingBest practices for accurate data mappingData Transformation: Fuzzy, Transformation Table PracticeHands-on practice with transformation tablesTroubleshooting common problemsPerforming sense checksData Transformation: Transforming City Data SetCase study: transforming city dataApplying learned techniquesReinforcing key concepts through practical applicationData Transformation: Completing Sales FileCleaning and transforming sales dataHandling errors and missing valuesMaking executive decisions on data handlingData Transformation: Product FileImporting and cleaning product dataStandardizing product informationDealing with inconsistent data entriesData Consolidation: Model FormattingUnderstanding automatic relationship detectionDeactivating auto-detect for manual relationship managementFormatting and enriching dataData Enrichment: Calendar Table (Simple)Creating a simple calendar tableUsing DAX for date-related calculationsEnhancing reports with date intelligenceData Enrichment: Calendar Table (Fiscal Year)Creating a fiscal year calendar tableCustomizing date intelligence for fiscal reportingUtilizing DAX for advanced date calculationsQ&A SessionRecap of previous sessionsAddressing parti[beeep]nt questions and concernsPractical tips and insights from real-world use casesData Model: Fact TableUnderstanding fact tablesCharacteristics and purpose of fact tablesCreating and managing fact tables in Power BIData Model: Dimension Table & Star SchemaUnderstanding dimension tablesCharacteristics and purpose of dimension tablesImplementing star schema in data modelingData Model: Cardinality and Cross Filter DirectionUnderstanding cardinality in relationshipsManaging cross-filter directionBest practices for relationship managementData Model: Merge and Role-Playing DimensionsMerging tables for optimized data modelsCreating role-playing dimensionsAdvanced data modeling techniquesData Model: Comparing 2 Fact Tables (Theory)Theoretical concepts of comparing fact tablesUnderstanding common grainsImplications of comparing different grainsData Model: Comparing 2 Fact Tables (Practice)Practical application of comparing fact tablesHandling many-to-many relationshipsBest practices for accurate comparisonsComparing Sales and Inventory (Considerations & Reporting)Comparing sales and inventory dataManaging data discrepanciesEffective reporting techniquesRecap and Data Enrichment Using Custom Columns CCRecap of key conceptsData enrichment techniques using custom columns (CC)Practical examples and hands-on exercisesComparing Order Date and Ship DateComparing different date fieldsHandling date discrepanciesCreating meaningful insights from date comparisonsComparing Target Sales vs Actual Sales Part 1Introduction to target vs actual sales comparisonSetting up the data modelCreating relationships and calculationsComparing Target Sales vs Actual Sales Part 2Advanced techniques for comparing target vs actual salesHandling complex data modelsBest practices for accurate reportingRole Level SecurityImplementing role-level security in Power BIManaging user access and permissionsBest practices for secure data modelsNormalizing a Flat FileIntroduction to normalizing flat filesStep-by-step process for creating dimension tablesBest practices for efficient data modelingClosing and Q&ARecap of the entire courseFinal questions and answers Who this course is for Those looking to build a career in data science and analytics and want to add advanced Power BI skills to their toolkit. 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Free Download Explore Data with PivotTables in Microsoft Excel Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 46m | Size: 341 MB Do you feel like you're drowning in data? This course will teach you how to create and customize PivotTables, filter and sort data, use calculated fields, and generate insightful reports to enhance your decision making. Around 90% of the world's business data is held in Excel and it's one of the most widely used tools for data storage, analysis, and reporting due to its versatility and ease of use. However, the sheer quantity of data in an Excel report can make gaining meaningful insights at best difficult and at worst impossible. In this course, Explore Data with PivotTables in Microsoft Excel, you'll learn to easily summarize and gain insights into large Excel datasets. First, you'll explore how to create PivotTables. Next, you'll discover how you can analyze the data in a PivotTable, creating SUMS, averages, and more. Finally, you'll learn how to filter and sort data in a PivotTable, allowing you to quickly find answers to specific questions. When you're finished with this course, you'll have the skills and knowledge of PivotTables needed to gain meaningful insights into large Excel datasets. Homepage https://www.pluralsight.com/courses/microsoft-excel-explore-data-pivot-tables TakeFile https://takefile.link/zfyf9jrv0l0j/tisty.Explore.Data.with.PivotTables.in.Microsoft.Excel.rar.html Rapidgator https://rg.to/file/4a9a1a63a4d160ffed8e2f1fc09174ba/tisty.Explore.Data.with.PivotTables.in.Microsoft.Excel.rar.html Fikper Free Download https://fikper.com/lJUQgsVuM8/tisty.Explore.Data.with.PivotTables.in.Microsoft.Excel.rar.html No Password - Links are Interchangeable

Free Download Excel Data Analytics In Aml Financial Intelligence Analysis2 Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.12 GB | Duration: 1h 22m Mastering Excel Functions and Advanced Tools for AML/CFT Financial Intelligence Analysis What you'll learn Applying the IF Function alongside essential functions effective for AML/CFT Analysis Utilizing Power Query for Table Combination and Transformation in AML/CFT Analysis Managing Data Models with Power Pivot Automating Tasks with AI: Creating Macros and VBA Codes Requirements Having some prior knowledge of Excel will make the content easier to understand. Description "This course is a follow up to my best selling course on "Excel Data Analytics for AML/CFT Financial Intelligence Analysis"This course offers much more in leveraging Excel for AML/CFT (Anti-Money Laundering/Combating the Financing of Terrorism) analysis and investigation. Withe Excel standing out as a cost-effective and accessible alternative to specialized vendor tools traditionally used in this domain Excel is being used for more complex data analysis in this domain , as such the need for the analyst to go beyond just the basic excel features and functions of this powerful analysis tool.The primary goal of this course is to introduce more features and functions to update the skills of the analysts in Excel data analytics to meet up with current demands of the AML/CFT and financial intelligence analyst and investigator . It is designed for individuals seeking a rapid yet thorough understanding of applying Excel's powerful capabilities in this specialized field. Whether you are new to AML/CFT analysis or aiming to enhance your proficiency in financial intelligence through practical Excel applications, this course equips you with essential skills.Parti[beeep]nts will learn to effectively utilize Excel functions such as IF, COUNTIF, MATCH and much more , alongside advanced tools like Power Query and Power Pivot. Additionally, the course covers a basic introduction to integration of AI-driven macros and VBA coding for automating repetitive tasks and enhancing analytical workflows. Overview Section 1: Introduction Lecture 1 Introduction Section 2: Excel Function combinations essential for AML/CFT analysis Lecture 2 IF() and IF(AND()) Lecture 3 IF(COUNTIF() Lecture 4 IF(OR()) Lecture 5 Practice Exercise Lecture 3,4 and 5 Lecture 6 MATCH(), IF(ISNUMBER(MATCH())) Lecture 7 Practice Exercise Lecture 7 Lecture 8 MAXIFS(), IF(MAXIFS()) Lecture 9 Practice Exercise 9 Lecture 10 XLOOKUP, (COUNTIF()) Lecture 11 Practice Exercise Lecture 11 Section 3: Introduction to Power Query, Power Pivot and Excel Data Models Lecture 12 Introduction to Power Pivot Lecture 13 Power Pivot Data Model 1 Lecture 14 Power Pivot Data Model 2 Lecture 15 Power Pivot Data Model 3 Lecture 16 Power Pivot Data Model 4- RELATED() Function Lecture 17 Practice Exercise Power Pivot Lectures Lecture 18 Introduction to Power Query Lecture 19 Some Basic Transformations using Power Query Lecture 20 Adding Tables to Existing Data Model Lecture 21 Practice Exercise Power Query Lectures Section 4: AI assisted analysis using the data Analyse Feature in Excel 365 Lecture 22 DATA ANALYSE in Excel Lecture 23 Practice Exercise DATA ANALYSE AML/CFT professionals,Financial Intelligence Analysts,Compliance officers of financial and other financial institutions,Analysts curious about using excel data analytics in financial intelligence analysis,Persons interested in exploring a career as an AML/CFT analyst or investigator Homepage https://www.udemy.com/course/excel-data-analytics-in-aml_cft-financial-intelligence-analysis2/ Rapidgator https://rg.to/file/2fd079db8dda2c58b04d58d49655c3dd/eyedl.Excel.Data.Analytics.In.Aml.Financial.Intelligence.Analysis2.part1.rar.html https://rg.to/file/3a4e525cabb186633374bdb34ace2b98/eyedl.Excel.Data.Analytics.In.Aml.Financial.Intelligence.Analysis2.part2.rar.html Fikper Free Download https://fikper.com/5TqtNtiDru/eyedl.Excel.Data.Analytics.In.Aml.Financial.Intelligence.Analysis2.part1.rar.html https://fikper.com/mXjUPkjFfw/eyedl.Excel.Data.Analytics.In.Aml.Financial.Intelligence.Analysis2.part2.rar.html No Password - Links are Interchangeable

Free Download Data Engineer Technical Interview - Get a well paid job Published 9/2024 Created by Alvaro Barber MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 22 Lectures ( 57m ) | Size: 323 MB Learn how to prepare for the data engineer technical interview and get the best salary. What you'll learn: How to build an ETL pipeline from a source db in order to create reports in Power BI Learn the basics of Data Modelling to become familiar with core concepts Learn the fundamentals of Data Vault 2.0 Hub, Links and Satellites and the use of hash keys and hash diffs Data Acquisition with Azure Data Factory(ADF) Data Storage with Azure Blob Data Warehousing with Snowflake and creation of data model Testing scenarios with SQL code snippets Python, SQL and Pyspark Version Control with GIT Requirements: Understading the SQL fundamentals No programming experience required Description: There is too much information out there that one gets really paralyzed trying to know how to get the most in the least amount of time. That includes asking for the best salary too.This course is intended for those ones who want to get quickly a data engineer job in the market and check the 80% of the theory questions that will be asked in the live interview. After my experience of more than 20 data engineering interviews done in 2024 and 5 final offers received, I will help you by showing the most frequent questions, so you don't have to spend big amount of time going through this painful process.The course is going straight to the point with the list of questions most frequently asked and supported by some lectures and tips to boost your CV and the most common basic coding tests.The technologies that will be reviewed:Make a readable CVData Warehousing, Data Lake and Data ModellingSQLSnowflakePythonPysparkGITData Integration tools in the Azure Cloud with Azure Data Factory (ADF) CI/CD project developmentAgileTesting scenariosFINAL SECRET : The trick to get the best salary offer (worked 5/5 times) and that will boost your salary in a 10%. Never fails.Be confident , that after reviewing all these questions and lectures in about ~1hyou will land a very good remote data engineering job or at least be paid like you deserve.You may fail once but not twice! Who this course is for: Business Analysts/Data Engineers/Data Scientists that want to have an overall picture of whole ETL pipeline Those in data related positions who want to solidify their knowledge with real example data engineering tasks Those facing a job interview and want to grab a first experience data engineering project Homepage https://www.udemy.com/course/technical-interview-data-engineering-get-a-well-paid-job/ Rapidgator https://rg.to/file/66628a187b7d90baeeb6189692204cde/atheh.Data.Engineer.Technical.Interview..Get.a.well.paid.job.rar.html Fikper Free Download https://fikper.com/HxgIQsTLvz/atheh.Data.Engineer.Technical.Interview..Get.a.well.paid.job.rar.html No Password - Links are Interchangeable

Free Download Certified Data Management Professional (Cdmp) - Associate Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.93 GB | Duration: 20h 47m Master the Core Principles of Data Management and Prepare for the CDMP Associate Certification What you'll learn Master the core principles of data governance and the roles and responsibilities involved. Understand the DAMA-DMBOK framework and its importance in data management. Learn the structure and certification levels of the CDMP exam. Explore strategies for preparing and studying for the CDMP certification exam. Grasp the foundational concepts of data architecture, including logical and physical data models. Design scalable data systems that meet organizational needs. Gain a deep understanding of data modeling, including normalization and denormalization techniques. Develop expertise in data storage models, data retention, backup, and recovery strategies. Understand the principles of data security and how to mitigate risks in data management. Implement best practices for ensuring data quality and improving data integrity. Explore the concepts of master and reference data management and their role in data consistency. Learn the fundamentals of metadata management and how it enhances data accessibility and governance. Understand the role of data warehousing and business intelligence in strategic decision-making. Explore emerging technologies like AI and big data and their impact on data management. Dive into cloud data management and its benefits for scalable and secure data storage. Understand ethical data management practices and how to ensure regulatory compliance. Requirements No Prerequisites. Description This course offers an in-depth exploration of the core principles and frameworks surrounding data management, with a specific emphasis on preparing students for the CDMP (Certified Data Management Professional) certification. The course is designed to provide a comprehensive overview of the various aspects of data management, including governance, architecture, modeling, security, quality, and more. While the course encompasses the theory of these data management concepts, it also provides valuable insights into how they can be applied in real-world scenarios, making it an essential resource for those looking to deepen their understanding of data management or prepare for the CDMP exam.Beginning with an introduction to the CDMP certification process, students will gain a detailed understanding of the certification levels, exam structure, and essential study strategies. This foundational knowledge not only prepares students for the certification itself but also provides a solid framework for comprehending the broader field of data management. In particular, students will appreciate the subtle focus on theoretical aspects that underpin data management, allowing them to explore the key concepts without the distraction of immediate hands-on applications.The course delves into data governance, one of the most crucial pillars of effective data management. Students will examine the roles and responsibilities that come with governance, as well as the policies, procedures, and frameworks that support a strong data governance strategy. Understanding governance frameworks is essential for ensuring that data remains secure, accurate, and compliant with industry standards. Students will learn how governance ties into the overall architecture of data systems and how it forms the backbone of a sustainable data management strategy.Next, the course takes a closer look at data architecture, providing insights into how data is structured, modeled, and managed across an organization. Key concepts such as logical versus physical data models and the principles of designing scalable data systems are explored in detail. Students will also study enterprise architecture and its integration with data management practices, which is crucial for organizations aiming to align their data systems with strategic business goals. This section encourages students to think critically about the theoretical models that shape modern data architecture and how these models can be adapted to meet an organization's unique needs.Data modeling and design are fundamental to ensuring that data is both useful and efficient in meeting organizational objectives. The course covers essential topics such as normalization, denormalization, and data relationships, providing students with the knowledge needed to design and optimize data models for various industries. In doing so, students will gain an understanding of best practices in data design, with an emphasis on conceptual, logical, and physical data models, further cementing their grasp of data management theory.Students will also explore the intricacies of data storage and operations, including storage models, techniques, and policies for data retention, backup, and recovery. The importance of data security management is also highlighted, focusing on principles, policies, and strategies for mitigating risks and ensuring regulatory compliance. In today's digital age, where data breaches and cybersecurity threats are constant concerns, understanding these security principles is vital for anyone working in data management.Furthermore, the course covers essential topics such as data quality management, metadata management, and reference and master data management. Each of these areas contributes to the overall goal of maintaining high standards of data integrity, accessibility, and usability. By the end of these sections, students will be equipped with the knowledge to assess and improve data quality, manage metadata repositories, and ensure that master and reference data are handled efficiently.As the course progresses, students will learn about data warehousing and business intelligence, which are critical for leveraging data in decision-making processes. The course also addresses emerging trends in data management, including the role of big data, artificial intelligence, and cloud technologies, which are reshaping the future of data systems.In summary, this course offers a thorough examination of data management principles with a focus on preparing students for CDMP certification. Through its structured approach to theoretical concepts, students will build a robust foundation in data management, which can be applied to a wide range of professional settings. Whether you are new to the field or looking to formalize your expertise, this course provides the essential knowledge and tools needed to excel in the dynamic and evolving world of data management. Overview Section 1: Course Resources and Downloads Lecture 1 Course Resources and Downloads Section 2: Introduction to CDMP and Data Management Lecture 2 Section Introduction Lecture 3 Overview of CDMP Certification Lecture 4 Case Study: Empowering Data Management Careers Lecture 5 Introduction to Data Management Lecture 6 Case Study: Enhancing RetailHub's Data Management Lecture 7 The DAMA-DMBOK Framework Lecture 8 Case Study: Transforming Data Management at TechNova Lecture 9 Certification Levels and Exam Structure Lecture 10 Case Study: Achieving the CDMP Lecture 11 Study Strategies for the CDMP Exam Lecture 12 Case Study: Strategic Mastery Lecture 13 Section Summary Section 3: Data Governance Lecture 14 Section Introduction Lecture 15 Introduction to Data Governance Lecture 16 Case Study: Empowering RetailNet Lecture 17 Governance Roles and Responsibilities Lecture 18 Case Study: Enhancing Data Governance Lecture 19 Data Stewardship and Accountability Lecture 20 Case Study: Implementing Data Governance at NexFinance Lecture 21 Policies, Procedures, and Standards Lecture 22 Case Study: TechCo's Data Governance Overhaul Lecture 23 Governance Frameworks and Best Practices Lecture 24 Case Study: Implementing Data Governance Frameworks Lecture 25 Section Summary Section 4: Data Architecture Lecture 26 Section Introduction Lecture 27 Introduction to Data Architecture Lecture 28 Case Study: Data Architecture Excellence Lecture 29 Data Architecture Principles Lecture 30 Case Study: Transforming Data Architecture Lecture 31 Logical vs. Physical Data Models Lecture 32 Case Study: Optimizing CRM Data Management Lecture 33 Enterprise Architecture and Data Management Lecture 34 Case Study: Strategic Integration of Enterprise Architecture and Data Management Lecture 35 Designing Scalable Data Systems Lecture 36 Case Study: Balancing Vertical and Horizontal Scalability Lecture 37 Section Summary Section 5: Data Modeling and Design Lecture 38 Section Introduction Lecture 39 Introduction to Data Modeling Lecture 40 Case Study: Optimizing Data Modeling for Business Growth Lecture 41 Conceptual, Logical, and Physical Data Models Lecture 42 Case Study: Optimizing Patient Management Systems Lecture 43 Normalization and Denormalization Lecture 44 Case Study: Balancing Normalization and Denormalization Lecture 45 Data Relationships and Entities Lecture 46 Case Study: Building a Scalable Data Model Lecture 47 Best Practices in Data Design Lecture 48 Case Study: Transforming Data Management Lecture 49 Section Summary Section 6: Data Storage and Operations Lecture 50 Section Introduction Lecture 51 Introduction to Data Storage Lecture 52 Case Study: Optimizing Data Storage Lecture 53 Storage Models and Techniques Lecture 54 Case Study: Strategizing Scalable and Secure Data Storage Lecture 55 Data Retention Policies Lecture 56 Case Study: Developing a Robust Data Retention Policy Lecture 57 Data Backup and Recovery Lecture 58 Case Study: Strengthening Data Resilience Lecture 59 Data Archiving and Deletion Lecture 60 Case Study: Optimizing Data Management Lecture 61 Section Summary Section 7: Data Security Management Lecture 62 Section Introduction Lecture 63 Introduction to Data Security Lecture 64 Case Study: Enhancing Data Security Lecture 65 Data Security Principles and Policies Lecture 66 Case Study: Enhancing Data Security Lecture 67 Data Access Control and Encryption Lecture 68 Case Study: Integrated Data Access Control and Encryption Strategies Lecture 69 Security Risks and Mitigation Strategies Lecture 70 Case Study: Enhancing Data Security Lecture 71 Regulatory Compliance and Data Privacy Lecture 72 Case Study: TechNova Data Breach Lecture 73 Section Summary Section 8: Data Quality Management Lecture 74 Section Introduction Lecture 75 Introduction to Data Quality Lecture 76 Case Study: Enhancing Data Quality in Healthcare Lecture 77 Data Quality Dimensions Lecture 78 Case Study: Optimizing Data Quality Dimensions Lecture 79 Data Quality Assessment Techniques Lecture 80 Case Study: Enhancing Data Quality Lecture 81 Tools and Processes for Data Quality Improvement Lecture 82 Case Study: Enhancing Data Quality at TechNova Lecture 83 Implementing a Data Quality Program Lecture 84 Case Study: Enhancing Decision-Making Lecture 85 Section Summary Section 9: Reference and Master Data Management Lecture 86 Section Introduction Lecture 87 Introduction to Master and Reference Data Lecture 88 Case Study: Transforming Data Management Lecture 89 Differences Between Master and Reference Data Lecture 90 Case Study: Enhancing GlobalTech's Data Management Lecture 91 Master Data Management Frameworks Lecture 92 Case Study: Optimizing Healthcare and Retail Operations Lecture 93 Reference Data Standardization Lecture 94 Case Study: Enhancing Data Integrity and Compliance Lecture 95 Tools for Managing Master and Reference Data Lecture 96 Case Study: Implementing Master and Reference Data Management Lecture 97 Section Summary Section 10: Metadata Management Lecture 98 Section Introduction Lecture 99 Introduction to Metadata Management Lecture 100 Case Study: Enhancing Retail Success through Robust Metadata Management Lecture 101 Types of Metadata: Descriptive, Structural, and Administrative Lecture 102 Case Study: Transformative Metadata Management Lecture 103 Metadata Repositories and Standards Lecture 104 Case Study: Enhancing Data Management Lecture 105 Metadata Governance Lecture 106 Case Study: TechNova's Metadata Governance Lecture 107 The Role of Metadata in Data Integration Lecture 108 Case Study: Leveraging Metadata for Efficient and Quality-Driven Data Lecture 109 Section Summary Section 11: Data Warehousing and Business Intelligence Lecture 110 Section Introduction Lecture 111 Introduction to Data Warehousing Lecture 112 Case Study: Strategic Data Warehousing Lecture 113 Data Marts and Data Lakes Lecture 114 Case Study: Balancing Data Marts and Data Lakes for Scalable Analytics Lecture 115 Business Intelligence Principles Lecture 116 Case Study: Unlocking the Full Potential of Business Intelligence at TechNova Lecture 117 Data Warehousing Models: Star and Snowflake Lecture 118 Case Study: Optimizing Data Warehousing Lecture 119 Reporting and Analytics in BI Lecture 120 Case Study: Transforming Patient Care Through Business Intelligence Lecture 121 Section Summary Section 12: Data Integration and Interoperability Lecture 122 Section Introduction Lecture 123 Introduction to Data Integration Lecture 124 Case Study: TechFusion's Comprehensive Data Integration Strategy Lecture 125 Data Sources and Extraction Techniques Lecture 126 Case Study: Integrating Diverse Data Sources for Enhanced Business Insights Lecture 127 Data Transformation and Loading (ETL) Lecture 128 Case Study: Optimizing ETL for Data Integration Lecture 129 Data Interoperability Standards Lecture 130 Case Study: Enhancing Patient Care Through HL7 Interoperability Lecture 131 Managing Data Silos Lecture 132 Case Study: Overcoming Data Silos Lecture 133 Section Summary Section 13: Document and Content Management Lecture 134 Section Introduction Lecture 135 Introduction to Document Management Lecture 136 Case Study: Transforming Healthcare Efficiency Lecture 137 Content Management Systems (CMS) Lecture 138 Case Study: Transforming E-Commerce Efficiency Lecture 139 Managing Unstructured Data Lecture 140 Case Study: Optimizing Unstructured Data Management Lecture 141 Version Control and Document Security Lecture 142 Case Study: Integrating Version Control and Document Security Lecture 143 Document Classification and Retrieval Lecture 144 Case Study: Optimizing Document Management with Machine Learning Lecture 145 Section Summary Section 14: Big Data and Emerging Technologies Lecture 146 Section Introduction Lecture 147 Introduction to Big Data Lecture 148 Case Study: Strategic Evolution at TechNova Lecture 149 The Role of Big Data in Data Management Lecture 150 Case Study: TechNova's Big Data Transformation Lecture 151 Big Data Storage and Processing Lecture 152 Case Study: Strategic Innovations in Big Data Management Lecture 153 Emerging Data Technologies (AI, Blockchain) Lecture 154 Case Study: Integrating AI and Blockchain Lecture 155 Implications of Big Data for Data Management Lecture 156 Case Study: Transforming Data Management Lecture 157 Section Summary Section 15: Data Management Ethics and Compliance Lecture 158 Section Introduction Lecture 159 Introduction to Data Ethics Lecture 160 Case Study: Ethical Data Management in Tech Lecture 161 Ethical Data Use and Decision Making Lecture 162 Case Study: Navigating Ethical Challenges Lecture 163 Regulatory Frameworks (GDPR, HIPAA) Lecture 164 Case Study: Navigating GDPR and HIPAA Lecture 165 Legal Considerations in Data Management Lecture 166 Case Study: Ensuring GDPR and HIPAA Compliance in Global Healthcare Lecture 167 Building an Ethical Data Culture Lecture 168 Case Study: Building an Ethical Data Culture Lecture 169 Section Summary Section 16: Emerging Trends in Data Management Lecture 170 Section Introduction Lecture 171 Introduction to Data Management Trends Lecture 172 Case Study: Mastering Data Management Lecture 173 Data Governance in the Age of Big Data Lecture 174 Case Study: TechNova's Strategic Overhaul Lecture 175 Artificial Intelligence and Data Management Lecture 176 Case Study: TechNova's AI-Driven Transformation Lecture 177 Cloud Data Management Lecture 178 Case Study: Transforming E-Commerce Lecture 179 Data Privacy and Ethical Challenges in the Digital Age Lecture 180 Case Study: ZypherTech Data Breach Lecture 181 Section Summary Section 17: Course Summary Lecture 182 Conclusion Aspiring data management professionals seeking to earn the CDMP Associate certification.,IT and data professionals looking to enhance their knowledge of data governance, architecture, and security.,Individuals transitioning into data management roles who want a comprehensive understanding of key principles.,Business analysts and data analysts aiming to improve their data modeling and quality management skills.,Project managers and team leads overseeing data-driven projects and seeking to improve data strategies.,Recent graduates in IT, computer science, or business-related fields who want to specialize in data management.,Professionals interested in staying updated on emerging trends like AI, big data, and cloud technologies in data management. 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Free Download Google Cloud Professional Data Engineer Get Certified 2022 Last updated 1/2023 Created by Dan Sullivan MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 115 Lectures ( 6h 19m ) | Size: 1 GB Build scalable, reliable data pipelines, databases, and machine learning applications. What you'll learn: How to pass the Google Cloud Professional Data Engineer Exam Build scalable, reliable data pipelines Choose appropriate storage systems, including relational, NoSQL and analytical databases Apply multiple types of machine learning techniques to different use cases Deploy machine learning models in production Monitor data pipelines and machine learning models Design scalable, resilient distributed data intensive applications Migrate data warehouse from on-premises to Google Cloud Evaluate and improve the quality of machine learning models Grasp fundamental concepts in machine learning, such as backpropagation, feature engineering, overfitting and underfitting. Requirements: Understanding of basic cloud computing concepts such as virtual machines and databases. One year or more experience working with data management or data analysis Description: The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning. Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high performance data engineering systems.This course is designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning. This course combines lectures with quizzes and hands-on practical sessions to ensure you understand how to ingest data, create a data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc. The final portion of the course is dedicated to the most challenging part of the exam: machine learning. If you are not familiar with concepts like backpropagation, stochastic gradient descent, overfitting, underfitting, and feature engineering then you are not ready to take the exam. Fortunately, this course is designed for you. In this course we start from the beginning with machine learning, introducing basic concepts, like the difference between supervised and unsupervised learning. We'll build on the basics to understand how to design, train, and evaluate machine learning models. In the process, we'll explain essential concepts you will need to understand to pass the Professional Data Engineer exam. We'll also review Google Cloud machine learning services and infrastructure, such as BigQuery ML and tensor processing units.The course includes a 50 question practice exam that will test your knowledge of data engineering concepts and help you identify areas you may need to study more.By the end of this course, you will be ready to use Google Cloud Data Engineering services to design, deploy and monitor data pipelines, deploy advanced database systems, build data analysis platforms, and support production machine learning environments.ARE YOU READY TO PASS THE EXAM? Join me and I'll show you how! Who this course is for: Cloud engineers and architects who want to pass the Professional Data Engineer exam Data engineers who want to learn about Google's advanced tools and services for data engineering Data scientists and data engineers who want to understand machine learning concepts Cloud application developers who want to use machine learning to build applications Devops engineers who need to support data engineering pipelines and machine learning models Homepage https://www.udemy.com/course/google-cloud-professional-data-engineer-get-certified/ TakeFile https://takefile.link/x779wj2a06ua/yrtre.Google.Cloud.Professional.Data.Engineer.Get.Certified.2022.part1.rar.html https://takefile.link/scr3wdy7r33w/yrtre.Google.Cloud.Professional.Data.Engineer.Get.Certified.2022.part2.rar.html Rapidgator https://rg.to/file/cb1f5d0f760cf08276ee333385df4ffe/yrtre.Google.Cloud.Professional.Data.Engineer.Get.Certified.2022.part1.rar.html https://rg.to/file/dcbb0f4ae0e66cff74a81ef6e3d446ed/yrtre.Google.Cloud.Professional.Data.Engineer.Get.Certified.2022.part2.rar.html No Password - Links are Interchangeable

Free Download Data Science for beginners (2024) Published 9/2024 Duration: 48m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.99 GB Genre: eLearning | Language: English Master the Foundations of Data Science: From Theory to Real-World Applications What you'll learn Understand the Data Science Process Develop Foundational Skills in Data Manipulation and Visualization Apply Machine Learning Algorithms Implement Data Science Solutions in Real-World Scenarios Requirements Basic Knowledge of Mathematics and Statistics, Familiarity with Programming , Access to a Computer with Internet,Curiosity and Eagerness to Learn Description This comprehensive data science course is designed to build a solid foundation in both the theoretical and practical aspects of data science. Data science continues to be one of the most in-demand fields across industries, but many learners face challenges in grasping the theoretical underpinnings that are crucial for long-term success. This course bridges that gap by covering everything from fundamental concepts to advanced machine learning techniques, ensuring you gain both the knowledge and practical skills necessary to excel in the field. Starting with an introduction to data science and its processes, the course moves into key topics like statistics, probability theory, data wrangling, and data visualization. You will explore machine learning essentials such as supervised and unsupervised learning, key algorithms, model evaluation, and selection. For those looking to dive deeper, the course delves into advanced topics like neural networks, deep learning, feature engineering, model tuning, and ensemble methods. In addition to the technical content, you'll work through case studies and real-world applications, culminating in a capstone project where you'll apply your knowledge to solve a real-world data science problem. This hands-on experience, coupled with the theoretical depth, prepares you for a successful career in data science. What You Will Learn Introduction to Data Science and its applications across industries. Core data science processes, including data collection, cleaning, and exploratory data analysis. Theoretical foundations of statistics and probability theory in data science. Hands-on data manipulation with Python libraries like Pandas. Data visualization techniques using Matplotlib and Seaborn. Machine learning fundamentals, including supervised and unsupervised learning with real-world examples. Key algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Advanced machine learning techniques: Neural Networks, Deep Learning, Feature Engineering, and Model Tuning. Practical applications of data science, ethics, and building a real-world data science project. Preparation for a capstone project and final exam, demonstrating your skills in a real-world context. Requirements A laptop or PC with internet access. Basic understanding of mathematics and statistics. Willingness to learn and apply data science concepts. Who Should Take This Course Aspiring data scientists who want to build a solid foundation. Professionals looking to switch to a career in data science. Data science enthusiasts aiming to enhance their theoretical knowledge and practical skills. Beginners who want to learn data science from scratch and gain real-world experience through projects. Who this course is for Data Scientists,Data Analysts and Software Engineers,Industry Professionals, Homepage https://www.udemy.com/course/data-science-for-beginners-a TakeFile https://takefile.link/vspmsopy8k9v/wgwfk.Data.Science.for.beginners.2024.part1.rar.html https://takefile.link/a7cmhtv6e50k/wgwfk.Data.Science.for.beginners.2024.part2.rar.html https://takefile.link/zglber2s8m9t/wgwfk.Data.Science.for.beginners.2024.part3.rar.html Rapidgator https://rg.to/file/a9b9c9929cde75f618215f938b96f185/wgwfk.Data.Science.for.beginners.2024.part1.rar.html https://rg.to/file/c54ddf62e435e25242169f448f7ff241/wgwfk.Data.Science.for.beginners.2024.part2.rar.html https://rg.to/file/7a59549beb02c56d58a1ada49ee9acbc/wgwfk.Data.Science.for.beginners.2024.part3.rar.html No Password - Links are Interchangeable

Free Download Data Entry - Ultimate Beginner Course With Practice Examples! Published 9/2024 Created by Matt Starky MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 10 Lectures ( 46m ) | Size: 828 MB Get your first or next data entry job and start earning as a virtual assistant now! What you'll learn: Master Data Entry Fundamentals Learn How to Execute Data Entry Projects Microsoft Office ChatGPT Requirements: NO Experience Needed. You'll learn what you need here, but it's a bonus if you have some skills. Description: Welcome to the ultimate Data Entry Mastery Course for Beginners, where you'll dive deep into the various types of data entry projects available on freelance platforms like Upwork and Fiverr.I'm Matt Starky, known for being #1 on Freelancer among 75 million users and my highly popular YouTube video on data entry. In this course, I'm going even further, sharing insider tips, tools, and strategies to kickstart your career as a data entry specialist and virtual assistant.You'll learn the essentials of data entry from a seasoned expert who's been providing virtual assistance and data entry services to global clients since 2012. By the end of the course, you'll be equipped to handle real-world tasks such as working with PDF to Excel/Word conversions, spreadsheet management, and more.This course offers hands-on project examples to help you practice and get familiar with the work. In addition to demo projects, I'll also share real project case studies from my experience, showing you:- The variety of data entry jobs available in freelance marketplaces- How clients typically provide instructions and requirements- How to execute data entry tasks efficientlyBy following the lessons and completing the practice projects, you'll gain the confidence to secure clients, manage projects, and grow into a successful freelance data entry professional.Who is this course for?- Anyone eager to learn about data entry and become proficient in tools like Microsoft Word, Excel, and handling PDF conversions.- Those looking to start a freelance career in data entry and virtual assistance.Start your journey with me today and learn the skills to succeed in the world of freelance data entry! Who this course is for: This data entry course is brilliant for beginner data entry professionals. Homepage https://www.udemy.com/course/data-entry-ultimate-beginner-course-with-practice-examples/ TakeFile https://takefile.link/xcapl9flu0pt/wkgyh.Data.Entry.Ultimate.Beginner.Course.With.Practice.Examples.rar.html Rapidgator https://rg.to/file/992aa739c709ef1090a27c6e03a686bc/wkgyh.Data.Entry.Ultimate.Beginner.Course.With.Practice.Examples.rar.html No Password - Links are Interchangeable

Free Download Cracking the Data Engineering Interview Published 9/2024 Created by Milo Kite MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 102 Lectures ( 3h 55m ) | Size: 3 GB Use SQL, Python, Data Modelling and Project Interview preparation to land your first job in Data Engineering! What you'll learn: To query Data in a Database To write your own scripts in Python The most powerful SQL transformations Python's control flow and conditional logic To sell your project experience effectively To navigate AI's role in Data Strategies for modelling Data in a Database How problems are posed in industry Navigate live-coding interviews calmly Write scalable, professional code Know what role a Data Engineer performs Gain confidence in yourself and experience Set Theory's relation to Data What hiring managers really look for Requirements: No programming experience required, all you need is an email address and an internet connection. All of the tools used can be accessed from your browser. Description: Ready to Launch Your Career in Data Engineering?Whether you're curious about the world of data, a student, looking to change up your career, in a related field, or just looking to sharpen up your skills this course is designed just for you! Cracking the Data Engineering Interview is your gateway to landing a job in one of the fastest-growing, high-demand fields in tech today.Course OverviewCracking the Data Engineering Interview is a beginner-friendly course that takes you step-by-step through everything you need to succeed in your data engineering job hunt. We start with the basics and gradually build your skills, ensuring you're fully equipped to crack any interview. Throughout the course, you'll find loads of bite-sized coding example questions designed to test your skills and remove the scare factor. These exercises are not just practice-they're a built-in study plan, helping you focus on key concepts without the extra effort of planning your own study sessions. What this course DOESN'T include:Downloading any softwareConfusing terminologyWriting code in your terminalLengthy hard to digest lectures Course ContentIntroduction | We'll kick off with an overview of the data engineering landscape, what the role entails, and why it's such a critical function in the age of big data and AI.Data Modelling | Learn the fundamentals of data modelling, including how to structure and organise data in ways that make it accessible, reliable, and easy to analyse.SQL | SQL is the backbone of data engineering. In this module, you'll master the art of writing efficient queries, understanding joins, and optimising databases. You'll also get hands-on practice with exercises that mimic real-world scenarios you'll face in a data engineering role.Projects | This section also teaches you how to effectively present and sell your project experience to hiring managers. You'll learn how to highlight your contributions, explain the impact of your work, and showcase your skills in a way that makes you stand out in interviews.Python | Python is a key tool in the data engineer's toolkit. We'll start from the basics and move into more advanced concepts like data manipulation, scripting, and automation.Start Learning Today!This course is structured to make learning easy and accessible, with no need for complex installations or deep technical know-how to get started. We believe that the best way to learn is by doing, and we're here to support you every step of the way.Get ready to ace your data engineering interview and step into a rewarding career with confidence. Enrol now and take the first step towards your new future!If you have any questions about the content of the course or need any support during the course feel free to reach out to me by email. Who this course is for: This course is designed for programming beginners looking to get their first job in Data Engineering! The most common types are people looking for a career change, graduate students, professionals in related fields, or even Data Engineers looking to round out their experience. 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Free Download Snowpark for Data Engineers Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 51m | Size: 217 MB Take a deep dive into Snowpark Python in Snowflake, the library set specifically designed for data engineers and professionals seeking to leverage the capabilities of the Snowflake managed data platform. Join instructor Janani Ravi as she shows how to get up and running with Snowpark, from setting up a Snowflake trial account to writing Snowpark handlers, performing data transformations, and working with both structured and semistructured data. Along the way, explore more advanced concepts such as creating and managing user-defined functions (UDFs), user-defined table functions (UDTFs), and stored procedures, as well as how to install necessary packages, access custom packages, and connect to Snowflake from a Jupyter notebook. By the end of this course, you'll be prepared to start manipulating data frames, performing data engineering tasks, and implementing complex data processing functions within Snowflake. Homepage https://www.linkedin.com/learning/snowpark-for-data-engineers TakeFile https://takefile.link/gcrdumvpe55f/jyfhz.Snowpark.for.Data.Engineers.rar.html Rapidgator https://rg.to/file/957233dca839901a00764c83a750c55a/jyfhz.Snowpark.for.Data.Engineers.rar.html Fikper Free Download https://fikper.com/mEY1iRRnOk/jyfhz.Snowpark.for.Data.Engineers.rar.html No Password - Links are Interchangeable