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Coursera

Data Ecosystem

Tableau via Coursera

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Overview

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The Data Ecosystem course will give you a foundational understanding of the entire data ecosystem, including data management. Specifically, this course shows how a business intelligence analyst would organize, access, and use data. You will learn about a variety of data sources along with the use and purpose of each type. Additionally, you’ll learn about the importance of data quality and data governance in relation to effective data management. You’ll also learn about the goals of data management and the principles that form the foundation of modern data architecture. Having a solid understanding of the data ecosystem is important for entry-level roles in business analytics. This course is for anyone who is curious about entry-level roles that demand fundamental Tableau skills, such as business intelligence analyst or data reporting analyst roles. It is recommended (but not required) that you have some experience with Tableau Public, but even if you're new to Tableau Public, you can still be successful in this program. By the end of the course, you will be able to: -Describe the purpose and applications of tabular data, databases, data warehouses, data lakes, and ETLs. -Describe the importance of data quality and data governance in relation to data management. -Identify the goals of data management. -Identify the principles that form the foundation of modern data architecture.

Syllabus

  • Data Sources
    • Welcome to the Data Ecosystem course, the third course in the Tableau Business Intelligence Analyst Professional Certificate. By enrolling in this course, you are continuing on your journey to a career in business analytics. In the first week of this course, you will learn about the most basic data sources you will encounter as an analyst — spreadsheets! You will also learn about more complex data sources like databases and how organizations use different systems to manage them.
  • Data Warehousing
    • Welcome to the second week of Data Ecosystem! As organizations collect more and more data, they require more complex systems to store and manage it. Many organizations use a combination of data warehouses and data lakes for this exact purpose. So, that's what this week is all about! By the end of this week, you will be able to describe the purpose of a data warehouse for analytics and identify the most common ways they are structured. You will also be able to describe the application of data lakes for analytics and recognize the key differences between a data lake and a data warehouse.
  • Data Management
    • Welcome to the third week of the Data Ecosystem! With the amount of data that organizations are collecting, you might be wondering how they are able to ensure that it is accessible throughout the organization while maintaining security. In order to ensure that their data is useable and protected, organizations have to have strong data management practices throughout the data life cycle. Data management can be thought of as the logistics of data — how you collect, store, validate, protect, and maintain your data. This week, you'll dive into a few of the key components of good data management including data quality management, data governance, and data architecture. By the end of this module, you'll have an understanding of the practices organizations employ to make sure their data can be trusted.

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4.7 rating at Coursera based on 50 ratings

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