Course

Advanced Data Cleaning in Python

Data scientists spend over 60% of their time cleaning and preparing data for analysis. While it’s not the most exciting part of the job, data cleaning is undoubtedly one of the most important skills you need. In this advanced data cleaning course, you’ll learn complex data cleaning techniques that will help you to stand from the crowd as a data analyst or data scientist.

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Part of the Data Analyst (Python), and Data Scientist (Python) paths.

4.8 (359 reviews)

17,813 learners enrolled in this course.

Intermediate friendly
9 hours
Self paced
4 lessons

“Dataquest was an enormously helpful tool for me to develop my data analyst skills and I credit it with getting me the job I have today.”

Christian L’Heureux

Analytics Manager @Epic Games Store at Epic Games

Advanced Data Cleaning in Python

Enroll For Free

Course overview

This course builds on basic data cleaning knowledge and requires intermediate familiarity with Python for data science. You’ll learn how to clean and manipulate text data using basic and advanced regular expressions, how to resolve missing data, and how to employ lambda functions and list comprehension with pandas. Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.

Key skills

Cleaning and manipulating text data using regular expressions
Resolving missing data and duplicate data
Employing lambda functions and list comprehension with pandas
Advanced techniques for data cleaning

Course outline

Advanced Data Cleaning in Python [4 lessons]

Regular Expression Basics 2h

Lesson Objectives

Employ regular expressions
Define regex components like character classes and quantifiers
Employ regular expressions with the 're' module and pandas

Advanced Regular Expressions 3h

Lesson Objectives

Clean data by substituting regular expression matches
Employ lookarounds to include and exclude text before and after your regex match
Extract new columns from text data using capture groups

List Comprehensions and Lambda Functions 2h

Lesson Objectives

Read and employ JSON files
Create and transform lists using List Comprehensions
Create and employ lambda functions

Working with Missing Data 2h

Lesson Objectives

Identify missing data using both code and visualizations
Resolve missing data using imputation
Fill missing values using external data

The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

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Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”