How to Tackle the Exercises

This course assumes you have experience manipulating data with the Pandas library, which is covered in the data analyst nanodegree. Some of these transformation exercises are challenging. The most challenging exercises are marked (challenging). If an exercise is marked as a challenge, it means you’ll get something out of solving it, but it’s not essential for understanding the lesson material or for getting through the final project at the end of this data engineering course.

Throughout the exercises, you might have to read the pandas documentation or search outside the classroom for how to do a certain processing technique. That is not just expected but also encouraged. As a data scientist professional, you will oftentimes have to research how to do something on your own much like what software engineers do. See this answer on Quora about how often do people use stackoverflow when working on data science projects?.

Use Google and other search engines when you're not sure how to do something!

What You Will do in the Next Section

In the next section of the lesson, you'll learn about the extract portion of an ETL pipeline. You’ll get practice with a series of exercises. These exercises are relatively brief and focus on extracting, or in other words, reading in data from different sources. The goal is to familiarize yourself with different types of files and see how the same data can be formatted in different ways.

For a review of pandas, click on the "Extracurricular" section of the classroom. Open the Prerequisite: Python for Data Analysis course, and go to Lesson 7: Pandas.