Did you know that data scientists spend most of their time cleaning data? Yes, that’s right. Before you can even get to the analysis and the insights, you need to make sure you find, clean, and organize the data.

In our data cleaning with Python path, we’ll teach you how to identify and remove inaccurate records from a dataset. You’ll gain the fundamental skills to begin cleaning data, using the powerful tools offered by Python. We’ll also teach you how to manipulate, analyze, and visualize data using premier Python libraries like NumPy and pandas.

With these courses, you’ll have everything you need—and more—to perform data cleaning from start to finish.

  • Learn how to save values using variables; build for loops; work with lists; and more!
  • Discover how to analyze, combine, transform, and reshape data.
  • Renewal trade strategies for working with missing data.

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What You’ll Learn

Data cleaning is a crucial part of data science. Clean data will allow you to develop accurate models and insights. In fact, the majority of a data scientist’s time goes to data cleaning. That’s why we’ve developed this path — to show you how to maximize your time and effort when cleaning data.

In our Data Cleaning with Python path, you’ll learn how to remove incorrect and corrupted data. You’ll also learn how to resolve missing and inconsistent data, as well as duplicate values and outliers — and so much more.

This Data Cleaning with Python path covers basic Python programming, beginner to advanced lessons in data visualization, presentation, manipulation, and beginner to advanced data cleaning. Not only will you learn these skills, but throughout this path, you will put these skills to use with our guided projects and practice problems.

Enroll in this skill path if you want to learn how to do the following:

  • Implement the fundamentals of programming in Python
  • Build for loops, work with lists, and use conditional statements in Python
  • Clean and analyze text data
  • Use the Jupyter Notebook
  • Leverage powerful tools like Booleans, pandas, and Numpy to index and analyze data
  • Use vectorized operations to optimize working with data
  • Visualize data in a variety of ways, from line plots to scatter plots, bar plots, and histograms.
  • Optimize your data processing skills with best practices and software
  • Perform data cleaning from start to finish
Data Scientist in Python Salary Increase

Jobs that require data cleaning skills can average $168,738 /yr salary according to Salary.com

Data Scientist in Python Job Openings

The U.S. Bureau of Labor Statistics projects 28% growth in data science job skills by 2026

Data Scientist In Python Job Growth

Python won the TIOBE’s programming language of the year award four years in a row

How Our Data Cleaning with Python Skill Path Works

Most surveys indicate that data professionals spend 70-80% of their time preparing data for analysis. To thrive in this data-driven world, you’ll need valuable insights from quality data.

The best way to learn data cleaning with Python is to practice it firsthand. Different types of data will require different data cleaning methods. At Dataquest, we offer an entire skill path on data cleaning using Python to position you for maximum career growth. Starting today, you can acquire these valuable skills to jumpstart your current or future career as a data professional.

With Dataquest, you’ll never learn alone. From our thriving community to our robust support tools, the support tools you need are right at your fingertips.
Here’s a quick glance at this skill path:

  • This skill path consists of the eight courses listed below, which cover the basics of Python and beginner to advanced data cleaning courses.
  • You’ll perform hands-on data cleaning using Python and answer practice problems to and apply your new skills.
  • Toward the end of the skill path, you’ll complete a guided data cleaning project from start to finish to enhance your resume and further reinforce your new skills.
  • When you complete the courses, you’ll earn a certificate you can share with your professional network.
  • After you receive your certificate, you’ll be ready to meet the demand for data cleaning with Python.Additional data science courses are available if you want to learn even more!
  • Engage with the community, get feedback on your project, and keep building.

Enroll in this career path to learn how to clean data using Python!

Data Cleaning with Python Skill Path Course List

Python for Data Science: Fundamentals Part I
Learn the basics of Python programming and data science.

Python for Data Science: Fundamentals Part II
Learn how to use Jupyter Notebook and how to build a portfolio project.

Python for Data Science: Intermediate
Learn important tools for your Python data science toolbox.

Pandas and NumPy Fundamentals
Learn how to analyze data using the pandas and NumPy libraries.

Data Visualization Fundamentals
Learn the fundamentals of data visualization in Python by striking a good balance between graph interpretation (statistics) and tooling (Matplotlib and seaborn).

Data Cleaning and Analysis
Learn data cleaning and analysis with pandas, how to combine data sets, and how to clean strings and handle missing data.

Data Cleaning in Python: Advanced
Learn how to use regular expressions to clean and manipulate text data, how to use lambda functions and lists comprehension with pandas, and strategies for working with missing data.

Data Cleaning Project Walkthrough
Learn to hone your data cleaning skills with a data cleaning project from start to finish.

Who Is This Data Cleaning Skill Path For?

Data cleaning is valuable in many different roles. Whether you’re a marketing professional who works with large datasets, a data scientist who wants to refresh their data cleaning skills, or a complete beginner interested in the data field as a whole, this path offers the training you need.

  • People who are interested in becoming a data scientist or data analyst
  • Anyone who frequently works with large datasets and wants to know the proper way to clean their data
  • Anyone who wants to prepare vast amounts of data for analysis
  • Students who want to develop a portfolio to become a competitive job applicant
  • Data scientists or data analysts who want to refresh certain skills
  • Python users who want to add variety to their skills
  • People seeking remote work

Qualify for In-demand Jobs in Data Cleaning

  • Data analyst
  • Financial data analyst
  • Business data analyst
  • Data scientist
  • Data quality analyst
  • Data conversion analyst
  • Data governance analyst
  • Data management analyst
  • Healthcare data analyst
  • BI analyst
  • Information scientists
  • Data engineer