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Importing & Cleaning Data in Python

Gain the real-world data prepping skills you need to reveal the insights that matter! Discover how to import, clean, and work with APIs and web data.
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PythonImporting & Cleaning Data13 hours17,357

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Track Description

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.

Prerequisites

There are no prerequisites for this track
  • Course

    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.

  • Course

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

  • Course

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

  • Project

    bonus

    Exploring Airbnb Market Trends

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

Importing & Cleaning Data in Python
4 courses
Track
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