Data Engineer Live Session

General Instructions

• Read this page carefully
• Ask all questions you can think of
• Ask for help when you get stock
• Search for guide and solutions online
• Speak out load what you are planning to do

Preparation

• If you need to code, store the code in a github repository
• If you want to use a cloud provider, use GCP, use a Google account not used before in GCP
• You may choose to work locally and not use a Cloud
• You may need an IDE
• You may use Jupyter Lab to run Python
• For a local DB you may use SQLite

Data Analysis Exercise

1) Data Analysis on Big Query

• Use a public Big Query data set for analysis
• Login into GCP and enable Big Query
• Once logged in, click on this data-to-insights
• The definitions of the data-in-insights is here
• Use the dataset data-to-insights.ecommerce.web_analytics

Answer the following:

• How many unique visitors do we have?
• How many of the visitors made a purchase?
• Out of the total visitors who visited our website, what % made a purchase?

2) Data Analysis locally

• Use the cereal.csv dataset provided for analysis
• Use Python or SQL

Answer the following:

• Show the first 10 rows
• Show all the columns for python only
• Does an increase in sugar lead to a higher rating?
• Visualize for python only
• Show the top 10 cereals with the major combination of sugars and carbo.
• Show the average of sodium among cereals with a rating higher than 38.