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Introduction to Data Science

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The payment is 100% refundable if you cancel before the first session. The remaining tuition is pro-rated based on date of withdrawal. To request a refund, please contact [email protected]


Students should have some experience with Python and have some familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. In Python, it will be helpful to know basic data structures such as lists, tuples, and dictionaries, and what distinguishes them (that is when they should be used). Students should skip the pre-work if they can accomplish all of the following:

  • Write a program in Python that finds the most frequently occurring word in a given sentence.
  • Explain the difference between correlation and covariance, and why the difference between the two terms matters.
  • Multiply two small matrices together (e.g. 3X2 and 2X4 matrices).

Otherwise, students should complete the following pre-work (approximately 8 hours) before the first day of class:

  1. Exercises 1-7, 13, 18-21, 27-35, 38,39 of Learn Python The Hard Way. (If the link is outdated, you can access the main website here.)
  2. Videos 1-6 of Linear Algebra review from Andrew Ng’s Machine Learning course (labeled as: III. Linear Algebra Review (Week 1, Optional).
  3. The exercises in Chapters 2 and 3 of OpenIntro Statistics. (This book is free, but there is a suggested donation. Feel free to donate an amount or set it to zero.)

Students must have a Github account to get access to the content. Sign-up for an account on their site is free, fast and easy.

In order to confirm a spot in the course, on the next page you must sign the subsequent enrollment agreement and then submit payment.