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About this course

In this advanced data cleaning course, you’ll learn how to supercharge your workflow with complex data cleaning techniques that will make you a data analysis superstar. To get the most out of these lessons, you must have intermediate knowledge of Python for data science and basic knowledge of data cleaning.

This course focuses on the following:

  • Introducing new Python concepts like character classes, quantifiers, and capture groups
  • Building proficiency in regular expressions, list comprehensions, and lambda functions
  • Teaching advanced techniques for data cleaning and working with missing data.

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How Our Advanced Data Cleaning in Python Course Works

Data scientists spend over 60% of their time cleaning and preparing data for analysis. While it’s not the “sexiest” part of the job, data cleaning is undoubtedly one of the most important. That’s why we’ve created this data cleaning course, to give you one of the most valuable skills you’ll ever need as a data scientist or data analyst. We’ve structured this course to build on the skills and knowledge contained in our previous Data Cleaning and Analysis course.

At Dataquest, it’s our mission to help you get hired as quickly as possible. That’s why we only use the best teaching methods. That way, you’ll acquire in-demand data skills quickly and retain them effectively. Unlike most online courses, we believe in learning by doing. With us, you’ll never sit through tedious and ineffective training videos or useless “fill-in-the-blank” problems. Instead, with Dataquest, you’ll advance on your career path by solving real problems with real code, starting on day one!

This Advanced Data Cleaning in Python course (like most courses here at Dataquest) features carefully crafted, hands-on, and interactive lessons that will allow you to not only learn data cleaning but also practice it using real-world datasets. You’ll even complete a project that requires you to dig into data gathered by the NYPD, helping you synthesize and apply your new data cleaning skills.

Most students love Dataquest because we help you build a compelling and robust project portfolio, which will help you get hired faster. That’s why we offer guided practice and a culminating portfolio project to complete at the end of every course. We want to help you build a portfolio that hiring managers and potential employers can’t ignore!

Additionally, with Dataquest, you’ll never learn alone! The moment you sign-up, you’ll officially become a part of our massive community of data learners and professionals who are all willing to help if you need it. If you ever need a little extra help, our powerful and responsive support tools are just a click away.

Here’s a glance at our Data Cleaning in Python: Advanced course:

  • This course is the eighth in the Data Analyst in Python Career Path. It consists of the four lessons listed below, which cover in-depth programming concepts in Python.
  • You’ll write real code with dozens of practice problems so you can apply your skills.
  • At the end of each course, you’ll complete a guided project to reinforce your new knowledge and expand your portfolio.
  • When you complete this course, you’ll receive a certificate that you can share with your professional network.
  • Once you complete this course, you’ll be ready for more advanced Python courses.
  • Engage with our friendly community of data professionals, get feedback on your projects, and keep building your skills.

Who Is This Data Cleaning in Python: Advanced Course For?

This is an intermediate Python course. We’ve designed it for intermediate-advanced Python programmers who already have a firm grasp of the basics of Python and data cleaning.

We created this course to benefit as many people as possible, here are just a few of them:

  • Data science beginners looking for intermediate data cleaning knowledge
  • Python users seeking a deeper understanding of the inner workings of Python
  • People who want a career as a data analyst or data scientist
  • People seeking remote work
  • Anyone who works with data in telecommunication, finance, education, and healthcare
  • Junior data scientists or data analysts who want to advance in their current positions
  • Anyone who wants to be able to capture, process, and interpret data
  • Data students who want to develop a competitive portfolio
  • Experienced Python users who want to review material they may have forgotten

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