Lesson Overview

Outline of the Lesson

Extract data from different sources such as:
- csv files
- json files
- APIs
Transform data
- combining data from different sources
- data cleaning
- data types
- parsing dates
- file encodings
- missing data
- duplicate data
- dummy variables
- remove outliers
- scaling features
- engineering features
Load
- send the transformed data to a database
ETL Pipeline
- code an ETL pipeline

This lesson contains many Jupyter notebook exercises where you can practice the different parts of an ETL pipeline. Some of the exercises are challenging, but they also contain hints to help you get through them. You'll notice that the "transformation" section is relatively long. You'll oftentimes hear data scientists say that cleaning and transforming data is how they spend a majority of their time. This lesson reflects that reality.

Big Data Courses at Udacity

"Big Data" gets a lot of buzz these days, and it is definitely an important part of a data engineer's and, sometimes, a data scientists's work. With "Big Data", you need special tools that can work on distributed computer systems.

This ETL course focuses on the practical fundamentals of ETL. Hence, you'll be working with a local data set so that you do not need to worry about learning a new tool. Udacity has other courses where the primary focus is on tools used for distributed data sets.

Here are links to other big data courses at Udacity:

data pipelines

SOLUTION:

False