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Learn Data Cleaning with Python
Rating: 4.1 out of 5(340 ratings)
11,813 students

Learn Data Cleaning with Python

Perform Data Cleaning Techniques with the Python Programming Language. Practice and Solution Notebooks included.
Last updated 3/2020
English
English [Auto],

What you'll learn

  • I can standardize a dataset by fixing inconsistent column names.
  • I can perform data type conversion to fix inaccurate data.
  • I can find and fix syntax errors in a dataset.
  • I can find and fix typos in a dataset.
  • I can deal with irrelevant data in a dataset.
  • I can remove any duplicate records in a dataset.
  • I can find and deal with missing data in a dataset.
  • I can find and deal with outliers in a dataset.

Course content

7 sections • 7 lectures • 50m total length
  • Introduction3:11

    Master fundamental data cleaning techniques in Python, including loading data, exploring structure, standardizing names, converting types, fixing errors, and handling missing data, outliers, duplicates, and irrelevant data for reliable analyses.

Requirements

  • You will need to have basic python programming proficiency.
  • You will need a modern browser i.e. Google Chrome or Mozilla Firefox.

Description

By the end of this course, you will be able to:

  • I can standardize a dataset by fixing inconsistent column names.

  • I can perform data type conversion to fix inaccurate data.

  • I can find and fix syntax errors in a dataset.

  • I can find and fix typos in a dataset.

  • I can deal with irrelevant data in a dataset.

  • I can remove any duplicate records in a dataset.

  • I can find and deal with missing data in a dataset.

  • I can find and deal with outliers in a dataset.


Who this course is for:

  • This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.