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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 CompTIA Data+: Complete coverage of the new CompTIA Data+ (DAO-001) exam to help you pass on the first attempt by Cameron Dodd English | December 23, 2022 | ISBN: 1804616087 | 6 hours and 39 minutes | MP3 128 Kbps | 561 Mb Learn data analysis essentials and prepare for the Data+ exam with this CompTIA exam guide, complete with practice exams towards the end. Key Features:Apply simple methods of data analysis and find out when and how to apply more complicated onesTake business requirements and produce a remote to the correct audience using appropriate visualizationsLearn about data governance rules, including quality and control Book Description: The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest growing fields in the world, but is also starting to standardize language and concepts within the field. However, there's a lot of conflicting information and lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt. The CompTIA Data + (DAO-001) Certification Guide will give you a solid foundation on how to prepare, analyze and report the data for better insights. You'll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you'll cover data analysis topics like types of analysis, common techniques, hypothesis techniques, and statistical analysis before tackling data reporting, common visualizations, and data governance. All knowledge you've gained throughout the book will be tested of mock tests that appear in the final chapters. By the end of this book, you'll be ready to pass the Data+ exam with confidence and take the next step in your career. What You Will Learn:Get well versed with the five domains covered in the DAO-001 examGain an understanding of all the major concepts covered in the exam and when to apply themUnderstand the fundamental concepts behind ETL and ELTExplore various imputation and deletion methods to deal with missing dataIdentify and deal with outliersLearn and perform hypothesis testingCreate insightful reports to showcase your findings Who this book is for: If you are a data analyst looking to get certified with DAO-001 exam this is the book for you. This CompTIA book is also ideal for who needs help in entering the quickly growing field of Data Analytics and are seeking professional certifications. Rapidgator https://rg.to/file/5df70993696d67a0d54f2d0aec8d7688/32nlo.rar.html Fikper Free Download https://fikper.com/TZQuAf62XC/32nlo.rar.html Links are Interchangeable - No Password - Single Extraction

Free Download Grokking Data Structures Author: Marcello La Rocca Narrator: n/a English | 2024 | ISBN: 9781633436992 | MP3@64 kbps | Duration: 7h 11m | 626 MB Grokking Data Structures makes it a breeze to learn the most useful day-to-day data structures. You'll follow a steady learning path from absolute basics to advanced concepts, all illustrated with fun examples, engaging industry stories, and hundreds of graphics and cartoons. In Grokking Data Structures you'll learn how to: Understand the most important and widely used data structures Identify use cases where data structures make the biggest difference Pick the best data structure solution for a coding challenge Understand the tradeoffs of data structures and avoid catastrophes Implement basic data collections like arrays, linked lists, stacks, and priority queues Use trees and binary search trees (BSTs) to organize data Use graphs to model relationships and learn about complex data Efficiently search by key using hash tables and hashing functions Reason about time and memory requirements of operations on data structures Grokking Data Structures carefully guides you from the most basic data structures like arrays or linked lists all the way to powerful structures like graphs. It's perfect for beginners, and you won't need anything more than high school math to get started. Each data structure you encounter comes with its own complete Python implementation so you can start experimenting with what you learn right away. Rapidgator https://rg.to/file/565113a01b55b7fa2b20a6b5bd784851/uzpg1.Grokking.Data.Structures.zip.html Fikper Free Download https://fikper.com/nh3NaBSdT1/uzpg1.Grokking.Data.Structures.zip.html Links are Interchangeable - No Password - Single Extraction

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Free Download Data Analytics Masters - From Basics To Advanced Published 9/2024 Created by Satyajit Pattnaik MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 266 Lectures ( 46h 11m ) | Size: 35.1 GB Master Data Analysis: Learn Python, EDA, Stats, MS Excel, SQL, Power BI, Tableau, Predictive Analytics & ETL Basics What you'll learn: Discover how to effectively handle, analyze, and visualize data using Python and its robust libraries, including Pandas, NumPy, Matplotlib, and Seaborn. Learn how to conduct Exploratory Data Analysis (EDA) to reveal insights, detect patterns, and prepare data for further analysis through effective visualization Acquire the skills to extract, manipulate, and aggregate data using SQL. You will utilize MySQL to handle complex databases and execute sophisticated queri Master the art of creating interactive and insightful dashboards using Power BI and Tableau. You'll apply DAX for complex calculations in Power BI and integrate Explore the fundamentals of machine learning, including classification, regression, and time series analysis, to enhance your predictive analytics skills. Learn the fundamentals of ETL processes to effectively extract, transform, and load data for analysis. Requirements: No pre-requisites are required for this course Description: Congrats on enrolling in the Data Analytics Masters Course!!Need of Data AnalyticsThe outburst of data is transforming businesses. 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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

Free Download Data Resilience with Spring and RabbitMQ Event Streaming Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 2h 3m | Size: 275 MB RabbitMQ is a widely deployed messaging solution and Spring is a popular framework for building modern Java applications. In this course, Gregory Green explains how Spring and RabbitMQ can be used to build a resilient data architecture for critical applications. Learn about RabbitMQ features such as quorum queues, streams, and multi-site replication. Gregory also reviews Spring projects like Spring Cloud and AMQP that simplify building production-ready applications. Plus, learn how Kubernetes improves resiliency for Spring applications and RabbitMQ. Homepage https://www.linkedin.com/learning/data-resilience-with-spring-and-rabbitmq-event-streaming TakeFile https://takefile.link/4cl4j828fwm3/prdro.Data.Resilience.with.Spring.and.RabbitMQ.Event.Streaming.rar.html Rapidgator https://rg.to/file/508cf34980d9e2223622c5961c814e3c/prdro.Data.Resilience.with.Spring.and.RabbitMQ.Event.Streaming.rar.html Fikper Free Download https://fikper.com/QMk6SPzzRv/prdro.Data.Resilience.with.Spring.and.RabbitMQ.Event.Streaming.rar.html No Password - Links are Interchangeable

Free Download Mastering Data Integration Patterns Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 35m | Size: 314 MB Mastering Data Integration: Design, Development and Management What you'll learn Understand the fundamentals of data integration and its applications in business intelligence. Analyze and design data integration architectures and models. Develop robust data integration pipelines using best practices for data extraction, transformation, and loading. Deploy and manage data integration solutions with modern deployment practices (CI/CD). Tackle advanced integration challenges such as real-time and big data integration. Keep up with emerging trends in cloud-based platforms and governance techniques. Requirements Basic Understanding of Databases and SQL Familiarity with Data Management Concepts (Preferred) Knowledge of Business Intelligence (BI) Concepts (Preferred) Exposure to ETL Tools and Technologies (Optional) Basic Understanding of Cloud Computing (Optional) Description This six-module course takes you through every stage of data integration, from fundamental concepts to advanced techniques and modern trends. You will learn to analyze, design, develop, deploy, and manage data integration solutions that enhance business intelligence and unlock the power of your data assets.Course Modules:Module 1: Introduction to Data IntegrationThis introductory module lays the foundation for understanding data integration.Key Topics:What is data integration?Challenges and benefits of data integration.Business use cases for data integration.Introduction to data integration architectures: ETL, ELT, Data Vault.Introduction to Business Intelligence (BI) and its relationship to data integration.Module 2: Data Integration AnalysisThis module focuses on analyzing your data environment and defining integration needs.Key Topics:Defining data integration requirements.Source system analysis and profiling.Data quality assessment and cleansing techniques.Data volume analysis and target system mapping.Introduction to data integration modeling concepts.Module 3: Data Integration DesignLearn how to design an effective data integration solution in this module.Macro Design Best Practices:Source system selection and prioritization.Data transformation strategies.Target system design considerations.Micro Design Best Practices:Component-based design principles.Physical data integration modeling techniques.Coding standards and documentation practices.Data security and access control considerations.Module 4: Data Integration DevelopmentThis module covers the development phase, transforming design into action.Key Topics:Data extraction techniques: full vs. incremental loads.Change data capture (CDC) methods.Error handling and data integrity checks.Data transformation and cleansing in development environments.Unit testing and integration testing strategies for data integration processes.Module 5: Data Integration Deployment and ManagementEffective deployment and management are crucial for sustainable data integration.Key Topics:Building and deploying data integration pipelines.Continuous integration and continuous delivery (CI/CD) for data integration.Data integration monitoring and performance optimization techniques.Production support considerations and troubleshooting procedures.Module 6: Advanced Data Integration Topics with Modern TrendsThe final module explores advanced data integration concepts and emerging trends.Key Topics:Real-time data integration best practices.Big data integration challenges and solutions.Cloud-based data integration platforms.Data integration governance and metadata management.Emerging trends in data integration. Who this course is for Data professionals (data analysts, data engineers) seeking to expand their knowledge of integration processes. IT professionals involved in managing or implementing data systems. Developers and engineers interested in learning more about ETL processes and data management. Business intelligence professionals wanting to deepen their understanding of how integrated data supports BI systems. Anyone looking to build or improve data integration solutions within their organization. Homepage https://www.udemy.com/course/mastering-data-integration-patterns/ Rapidgator https://rg.to/file/b417b1af5fb3fd7d8fd0a4c365ff7868/kokxp.Mastering.Data.Integration.Patterns.rar.html Fikper Free Download https://fikper.com/vQLLyYxmIv/kokxp.Mastering.Data.Integration.Patterns.rar.html No Password - Links are Interchangeable

Free Download Mastering Azure Data Factory 6-Interview Questions & Answer Published 9/2024 Duration: 34m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.29 GB Genre: eLearning | Language: English Your Ultimate Guide to Acing ADF Interviews with In-Depth Q / A: Mastering Azure Data Factory: 6-Interview Questions What you'll learn Comprehensive Coverage: This course covers every aspect of Azure Data Factory, from basic concepts to advanced techniques, ensuring you're well-prepared for any Real-World Scenarios: Our hands-on approach ensures that you're not just learning theory but applying your knowledge to solve real-world problems Basic Azure Data factory interview questions and answer Intermediate level azure Azure Data factory interview questions and answer Advanced Azure Data factory interview questions and answer Requirements No Prerequisites: There are no prerequisites for this course. Anyone interested in learning about ADF can join. Basic Internet Access: All you need is an internet connection to access the course materials and practice tests. Accessible on Multiple Devices: You can access this course using a phone, laptop, computer, or tablet with an internet connection. Beginner-Friendly: This course is designed to accommodate learners of all levels, from beginners to experienced professionals. Open to Everyone: Whether you have prior experience with Azure or not, this course is open to everyone interested in data engineering. Self-Paced Learning: The course is structured for self-paced learning, allowing you to study at your convenience, regardless of your prior knowledge or experience. Basic Understanding of Cloud Computing: Familiarity with cloud concepts and services, particularly Azure, will help you grasp the course content more effectively. Willingness to Learn: A proactive mindset and a willingness to dive into practical scenarios will ensure you get the most out of this course. Description Unlock Your Potential in Azure Data Factory with Comprehensive Interview Preparation Are you aiming to land your dream job in Azure Data Engineering? Do you want to stand out in interviews by confidently answering questions on Azure Data Factory (ADF)? Look no further! Our course, "Mastering Azure Data Factory: Interview Questions & Expert Answers," is meticulously designed to equip you with the knowledge and skills needed to excel in any ADF-related interview. Azure Data Factory is at the heart of data integration and transformation in the cloud. As more organizations migrate to Azure, the demand for skilled professionals who can efficiently design, deploy, and manage data pipelines using ADF is soaring. This course is your one-stop solution to mastering the most commonly asked interview questions and understanding the underlying concepts that will set you apart from other candidates. What You'll Learn Core Concepts of Azure Data Factory Understand the architecture, components, and key features of ADF. Learn how to create and manage data pipelines, linked services, datasets, and triggers. Master the integration of on-premises and cloud data sources using ADF. Hands-On Scenarios Dive into real-world scenarios that mimic the challenges you'll face in the workplace. Learn how to design efficient data pipelines, implement data flows, and troubleshoot common issues. In-Depth Q&A Get access to a comprehensive collection of interview questions categorized by difficulty level (beginner, intermediate, and advanced). Explore detailed answers that not only cover the "what" but also the "why" behind each question, ensuring a deep understanding of ADF. Best Practices Discover best practices for optimizing data pipelines, securing data, and managing costs in Azure Data Factory. Mock Interviews License to mock interview sessions and answering ADF-related questions under pressure. Why Choose This Course? Real-World Scenarios Our hands-on approach ensures that you're not just learning theory but applying your knowledge to solve real-world problems. Expert Guidance The course is taught by Sarafudheen PM, a certified cloud data architect with over 7 years of experience in Azure and AWS. With a wealth of practical knowledge and teaching experience, Sarafudheen has trained over 85,000 students on Udemy and beyond, helping them achieve their career goals in cloud computing. Career-Focused We understand that your goal is to secure a job or advance in your career. This course is designed with that in mind, providing you with the tools and knowledge you need to succeed in the competitive job market. 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Free Download EssentialSQL - Microsoft Fabric Data Engineering Mastery Published 9/2024 Duration: 7h55m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 3.83 GB Genre: eLearning | Language: English Data Engineering | Microsoft Fabric | Azure Data Factory | Spark | Medallion | Lakehouse | Data Warehouse | SQL | Python What you'll learn Optimize Microsoft Fabric: Learn best practices for efficient data engineering organization. Understand Azure Data Factory: Grasp workflow orchestration for foundational knowledge in data engineering. Apply Data Integration: Utilize principles for end-to-end solutions in comprehensive data engineering. Evaluate Data Transformations: Compare techniques within Microsoft Fabric for informed data processing. Implement Solutions with Azure Data Factory: Leverage it with SQL Data Warehouse for comprehensive data engineering. Develop Solutions with Spark: Create end-to-end solutions for large-scale data processing and analytics. Construct Fabric Data Flows: Tailor flows in Microsoft Fabric for transforming and managing data in Lakehouse. Build Fabric Spark Jobs: Create advanced Spark jobs within Microsoft Fabric for complex data transformations. Establish Data Warehouse: Ingest from Fabric Lakehouse for a scalable and reliable data warehouse. Design ADF Pipelines: Develop Azure Data Factory pipelines for efficient end-to-end data transformations. Create Data Solutions with Dataflows: Construct end-to-end solutions, integrating components for seamless data processing. Apply DevOps for Version Control: Utilize Microsoft DevOps for systematic source code management. Implement Medallion Architecture: Apply advanced principles in Microsoft Fabric for efficient data handling. Requirements Familiarity with SQL and basic programming skills can be beneficial. We'll focus on SQL and Python. I provide to you all the sample you need to complete the case study. Proficiency in programming will be valuable when working with scripting and data transformation tasks Experience in troubleshooting and debugging technical issues will aid in building and troubleshooting Azure Data Factory pipelines. Description Unlock the full potential of data engineering with our comprehensive course on Microsoft Fabric. This hands-on program is designed for individuals seeking to master the latest in data transformation and integration technologies using Microsoft Fabric. Whether you're a data engineer, analyst, or IT professional, this course will equip you with the skills to build robust, scalable data solutions. What You Will Learn Introduction to Microsoft Fabric : Get started with Microsoft Fabric, exploring its core features and capabilities for data engineering. Learn how to set up your workspace and prepare for data ingestion. Medallion Architecture : Understand and implement the Medallion Architecture in Microsoft Fabric. Gain insights into organizing data into Raw, Bronze, Silver, and Gold layers for efficient processing and analysis. Dataflows Gen 2 : Discover the power of Dataflows Gen 2 for transforming and managing data. Learn how to ingest data through various stages, from Raw to Gold, and address common challenges. SQL and Python Integration : Dive into SQL and Python for advanced data transformation and processing. Create pipelines, automate workflows, and leverage SQL queries and PySpark to enhance data management. Visualization and Reporting : Use PowerBI to visualize data and generate insightful reports. Understand how to prepare data for visualization and create meaningful dashboards. End-to-End Solutions : Construct comprehensive end-to-end data engineering solutions. Learn how to integrate dataflows, pipelines, and transformation techniques to build a scalable data architecture. Practical Application : Engage in hands-on exercises with real-world examples, including a Car Sales case study. Work alongside the instructor to implement data engineering solutions and solve practical problems. Course Highlights Hands-On Learning : Experience a practical, "work with me" approach where you'll actively engage with the tools and concepts alongside the instructor. Flexible Learning Path : Start with foundational concepts and advance to complex topics at your own pace. Utilize SQL and Python methods to master different aspects of data transformation. Interactive Exercises : Build and optimize dataflows, create pipelines, and develop comprehensive data models. Address real-world scenarios and troubleshoot common issues. Expert Guidance : Benefit from direct access to course instructors for questions and support. Engage with a community of learners and share insights and solutions. Career Advancement : Enhance your data engineering skills and prepare for roles that require expertise in Microsoft Fabric, SQL, and Python. Gain practical experience that will set you apart in the data engineering field. Who Should Enroll Data Engineers : Professionals looking to enhance their skills in data architecture and processing using Microsoft Fabric. Data Analysts : Individuals seeking to expand their capabilities in data integration and transformation. Database Administrators : Those interested in optimizing data management with Microsoft Fabric. Business Intelligence Professionals : Experts aiming to develop end-to-end solutions for business analytics. Data Scientists : Professionals wanting to strengthen their data engineering skills, especially with large-scale data processing. IT Professionals and Technology Enthusiasts : Individuals interested in the latest Microsoft technologies for data engineering and analytics. Join us to gain hands-on experience with Microsoft Fabric and elevate your data engineering skills to the next level! Note: This course is a good introduction to Fabric and covers many of the topics required for the Azure Data Engineer Associate certification. Check out the preview lesson "Introduction to Microsoft Fabric Data Engineering and Certification Alignment" to see what's covered and what is not. Who this course is for Data Engineers: Professionals responsible for designing, constructing, and maintaining data architecture, databases, and processing systems. Data Analysts: Individuals seeking to expand their data processing and integration skills for comprehensive data analysis. Database Administrators: Those looking to enhance their knowledge of Microsoft Fabric and related tools for optimizing data management. Business Intelligence Professionals: Individuals interested in developing end-to-end solutions for business intelligence and analytics. Data Scientists: Professionals aiming to strengthen their data engineering capabilities, especially in large-scale data processing using technologies like Spark. IT Professionals: Those involved in IT roles looking to specialize in data engineering within the Microsoft ecosystem. Students and Graduates: Individuals pursuing education or recently graduated in fields related to data science, computer science, or information technology. Technology Enthusiasts: Individuals with a keen interest in learning about the latest tools and best practices in the field of data engineering. Professionals Transitioning Careers: Individuals seeking a career change into the field of data engineering or related roles. Anyone Interested in Microsoft Data Technologies: Enthusiasts looking to gain expertise in Microsoft technologies for data engineering and analytics. 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Free Download Coursera - Meta Data Analyst Professional Certificate Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 326 Lessons ( 22h 26m ) | Size: 5.8 GB Launch your career in data analytics. Build job-ready skills - and must-have AI skills - for an in-demand career. Earn a credential from Meta in 5 months or less. No degree or prior experience required. What you'll learn Collect, clean, sort, evaluate, and visualize data Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights Use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context Skills you'll gain SQL Pandas Generative AI in Data Analytics Data Analysis Python Programming Prepare for a career in the high-growth field of data analytics. In this program, you'll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed. You'll also have the option to learn how generative AI tools and techniques are used in data analytics. Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization. In this program, you'll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You'll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data. You'll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results. When you complete this full program, you'll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise. Applied Learning Project Throughout the program, you'll get to practice your new data analysis skills through hands-on projects including: Identifying data sources Using spreadsheets to clean and filter data Using Python to sort and explore data Using Tableau to visualize results Using statistical analyses By the end, you'll have a professional portfolio that you can show to prospective employers or utilize for your own business. Homepage https://www.coursera.org/professional-certificates/meta-data-analyst TakeFile https://takefile.link/obrpjort9haw/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part1.rar.html https://takefile.link/0o0vytqpya37/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part2.rar.html https://takefile.link/34bskatzz17w/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part3.rar.html https://takefile.link/qrpltfcjyyd8/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part4.rar.html https://takefile.link/8xs2j14g1aa4/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part5.rar.html https://takefile.link/bvorrbabt7a8/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part6.rar.html Rapidgator http://peeplink.in/4f6440b65328 Fikper Free Download https://fikper.com/lOEYaICLuR/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part1.rar.html https://fikper.com/amv7NDKYix/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part2.rar.html https://fikper.com/1TC8Mwgk6X/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part3.rar.html https://fikper.com/tOSwzILmAC/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part4.rar.html https://fikper.com/rK9dn7Jbkk/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part5.rar.html https://fikper.com/ipTh2AAlzc/rkldp.Coursera..Meta.Data.Analyst.Professional.Certificate.part6.rar.html No Password - Links are Interchangeable

Free Download 10 Key Functions to Analyze Data in Python for Beginners Published 9/2024 Duration: 36m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 310 MB Genre: eLearning | Language: English Learn and Apply Data Analysis with Python on Real-World Datasets What you'll learn Understand and apply 10 essential Python functions for data analysis. Master techniques to transform raw data into meaningful insights. Analyze datasets efficiently using Python's powerful tools. Learn how to clean and prepare data for analysis with Python. Requirements No prior programming experience needed; the course is designed for beginners. A computer with internet access to install Python and necessary tools. Description Welcome to "10 Awesome Functions in Python to Analyze Data"! Who this Course is for This course is tailored for anyone eager to step into the world of data analysis using Python , whether you have coding experience or not. There's no need for prior knowledge-just a computer, an internet connection, and a willingness to learn. What You Need To start analyzing data with Python, you'll need to set up a Python environment on your computer. But don't worry - I'm here to help every step of the way. We'll be using tools like Anaconda (which includes Jupyter Notebooks) or Visual Studio Code, both of which are free and widely used for data analysis. What You'll Learn In this class, you'll dive into 10 of the most powerful and practical functions in Python that are essential for data analysis. Each lesson focuses on a specific function, explaining its purpose and demonstrating how to use it with real-world datasets. By the end of the course, you'll have a solid toolkit of Python skills that you can apply directly to your own data projects. Here's what you'll cover How to load and view data with read_csv() and head() Summarizing your data with info() and describe() Cleaning and handling missing data using dropna() and fillna() Grouping and sorting data with groupby() and sort_values() Filtering data with query() Combining datasets using merge() By the end of this class you will not only understand the methods presented but also be able to apply the 10 functions on your own datasets and have gained great skills regarding data analysis. Who this course is for This course is ideal for business professionals, analysts, and beginners who want to harness the power of Python for data analysis. Whether you are new to programming or looking to enhance your data skills, this course will guide you step by step in transforming raw data into valuable insights. Perfect for those looking to apply data analysis in a business setting. Homepage https://www.udemy.com/course/10-key-functions-to-analyze-data-in-python-for-beginners Rapidgator https://rg.to/file/7e95a9be7047b012cde708b7e3c23da5/qtxeh.10.Key.Functions.to.Analyze.Data.in.Python.for.Beginners.rar.html Fikper Free Download https://fikper.com/EWZavwLRgS/qtxeh.10.Key.Functions.to.Analyze.Data.in.Python.for.Beginners.rar.html No Password - Links are Interchangeable