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Python for Data Science

You will start with the basics of python programming, including python data structures, functions and classes. We follow this up by an introduction to Numerical Python (NumPy) and finally, the course will provide a basic introduction to linear regression from scratch.

Along the way, we will introduce foundational ideas of statistics, linear algebra and calculus.

At the end of this module, you will have the tools and the concepts needed to successfully undertake a rigorous course in machine learning.

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Applications opening soon
Registration Closed


August 15, 2021
To be decided


5 weeks (classes only on weekends)

Time Investment

3-5 hours a week


Live Online


Introductory Price: $100
List Price: $250


Registration closes

August 14, 2021
To be decided

Course Outcome

Program Overview

Program Overview

At the end of this sprint, you will have the tools and the concepts needed to successfully undertake a rigorous course in machine learning.

Who should apply


Prior knowledge of programming is not necessary for this sprint

Class Timings

Course Structure

Live online classes
1. Live Online Classes

Bi-weekly live, cohort-based lessons with labs and quizzes

2. Mentor-Supported

Ten or more hours/week to interact with an experienced & accomplished mentor

Module end Project
3. End-of-Module Project

Complex problems that challenge you to apply what you learn

4. Capstone

10–12 week long Capstone Project with one of our partner companies or faculty

Program Faculty

Dr Pavlos Protopapas

Founding faculty Univ.AI

Pavlos is the Scientific Program Director, Institute for Applied Computational Science (IACS) at Harvard University, and leads the data science masters program at Harvard.

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Topics Covered


  • Introduction to Python
  • Data Types, iterators, python operations,
  • order of operations, logical operators
  • Python Data Structures - Lists, Dictionaries, Tuples
  • List/dictionary comprehensions
  • Enumeration
  • Python Functions - Arguments, keyword arguments, etc.
  • Anonymous functions (lambda function)
  • Classes: Constructors vs Instantiations
  • Methods vs. Attributes
  • Working with strings
  • String formatting
  • Reading & writing file
  • Debugging skills
  • Exception handling
  • Finding documentation
  • Process of elimination
  • Random Variable
  • Probability Density Function
  • Some ‘standard’ distributions and their mean/stdev (Normal, Binomial). Properties of mean and variance
  • Indexing / slicing
  • Shape & reshape
  • Zeros, ones, arbitrary array declaration
  • Derivatives (including partial)
  • Matrix Operations
  • Matrix Multiplication

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