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Introduction to Data Science

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Refund policy

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]

Prerequisites

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 (If the link is outdated, you can access the main website here.)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.