Data Analyst distance learning

In this first module, you will discover the KPIs (Key Performance Indicators) specific to each pole of an organization. You will learn how to use the data produced using no-code tools that are very common in companies:

Python is the most common programming language in Data Analysis. As you become more familiar with Data, you will need to learn how to code. That's why we'll look at how to write efficient code, and how to use libraries like Pandas, Numpy and Plotly.

Once you know how to write Python code, you'll be able to extract data and bring it into the company's internal infrastructure. The most common way to do this is to use ETL(Extract Transform Loads) tools, APIs and Data Warehouses. Therefore, you will learn how to use ETL tools like Airbyte to bring data to Big Query and automate your processes using Zapier.

As a Data Analyst, you will spend a lot of time querying data from various databases and sources. It is therefore essential that you master SQL: we will deepen your level of SQL programming. You will review joins, functions, and understand table schemas in order to be able to extract all types of data and visualize them with the BI tools seen in the next module.

Now that you know how to collect, extract and transform your data, you can visualize it and start making decisions. You'll learn how to use PowerBI and Tableau, the two most popular Business Intelligence (BI) tools on the market. Then, we'll teach you best practices for creating charts and presentations.