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Importing & Cleaning Data in Python

Understanding how to prep your data is an essential skill for working in Python. It’s what you have to do before you can reveal the insights that matter. In this track, you’ll learn how to import your data from a variety of sources, including .csv, .xls, text files, and more. After learning how to import your data it’s time to prepare your data for analysis. You’ll work with real-world data such as restaurant reviews as you learn how to handle improper data types, deal with missing data, and perform record linkage. You’ll then learn how you can leverage the Tweepy package to access Twitter’s API to scrape the web for data. Start this track and gain the data prepping skills you need to clean your dirty data.

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1
Introduction to Importing Data in Python

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

3 hours

Hugo Bowne-Anderson Headshot

Hugo Bowne-Anderson

Data Scientist

2
Intermediate Importing Data in Python

Improve your Python data importing skills and learn to work with web and API data.

2 hours

Hugo Bowne-Anderson Headshot

Hugo Bowne-Anderson

Data Scientist

3
Cleaning Data in Python

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

4 hours

Adel Nehme Headshot

Adel Nehme

VP of Media, DataCamp

4
Reshaping Data with pandas

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

4 hours

Maria Eugenia Inzaugarat Headshot

Maria Eugenia Inzaugarat

Data Scientist and Artificial Intelligence Consultant

5
Exploring Airbnb Market Trends

Apply your data importing, cleaning and manipulation skills to explore New York City Airbnb data.

2 hours

Maham Khan Headshot

Maham Khan

Senior Data Scientist, YouView TV

6
Importing & Cleaning Data with Python
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