Python for Data Science Weekend

Chicago, 29 E Madison St.
Python for Data Science Weekend
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  • Thalo Menninga
  • Beginner
    Skill Level


Data Science Immersive is a four session comprehensive course with an emphasis on the practical application of Python to data analysis. In the first two Saturdays we cover Python's built-in data types, explaining differences in the behavior of data structures, laying a foundation for the more complex NumPy and Pandas structures.


A deep understanding of data types prepares users to solve real-life challenges with the right tools in the most efficient manner. Also, it is important to understand why some of the types are faster and some are just an extension of others. In this course we are not covering in-depth mathematical and statistical concepts, rather we master practical usage of the Python programming language and its extensions NumPy and Pandas.


When you sign up for this course, you should also be prepared to work hard. While we do explain all of the concepts thoroughly and have notes on each lesson that you can refer back to, we follow up each lesson with practice problems to reinforce the material. We code almost seven hours a day with breaks for lunch and coffee.


  • Discover best practices for data analysis and start on the path to becoming a data scientist
  • Discover best practices for data analysis and start on the path to becoming a data scientist.
  • Learn and practice essential tools for data analytics: NumPy, Pandas and Matplotlib
  • Learn to find solutions to problems by analyzing data using appropriate tools
  • Master your analytical skills by working on real life projects
  • Implement the core Data Science techniques of Linear Algebra, Probability, Gradient Descent, and Linear Regression
  • By the end of this course, you will have a Data Analytics Project to present to potential employers

Things To Remember

  • Please bring your own laptop to class.
  • We will help you to install all the programs you need in class.
  • If you withdraw one day before the course start date, any deposit paid will be refunded in full.
  • If you cannot attend classes for which you were charged, you could join next cohort and make up the missed classes.