Learn the fundamentals of Data Analytics and gain an understanding of the data ecosystem, the process and lifecycle of data analytics, career opportunities, and the different learning paths you can take to be a Data Analyst. In this course, you will learn about the various components of a modern data ecosystem and the role Data Analysts, Data Scientists, and Data Engineers play in this ecosystem.
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You will gain an understanding of data structures, file formats, sources of data, and data repositories. You will understand what Big Data is and the features and uses of some of the Big Data processing tools.
This course will introduce you to the key tasks a Data Analyst performs in a typical day. This includes how they identify, gather, wrangle, mine and analyze data, and finally communicate their findings to different stakeholders impactfully. You will be introduced to some of the tools Data Analysts use for each of these tasks.
You will learn about the features and use of relational and non-relational databases, data warehouses, data marts, and data lakes. You will understand how ETL, or Extract-Transform-Load, process converts raw data into analysis-ready data. And what are some of the specific languages used by data analytics to extract, prepare, and analyze data.
By the end of this course you will know about the various career opportunities available in the field of Data Analytics, and the different learning paths you can consider to gain entry into this field.
The course ends with some exercises and a hands-on lab to test your understanding of some of the basic data gathering, wrangling, mining, analysis, and visualization tasks.
This course is part of the Data Analyst Professional Certificate.
What you'll learn
- Explain what Data Analytics is and the key steps in the Data Analytics process
- Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
- Describe the different types of data structures, file formats, and sources of data
- Explain the use for different types of data repositories, the ETL process, and Big Data platforms
- Describe the process and tools for gathering data, wrangling data, mining and analyzing data, and visualizing data
- List the different career opportunities in Data Analysis and resources for getting skilled in this domain
- Demonstrate your understanding of gathering, wrangling, mining, analyzing, and visualizing data
