The world's best Basics of Data Science Course from a cutting-edge research group at Harvard

5 weekends • No entrance test • No prerequisites
Classes starting 25 June 2022
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5 weekends

Program Format

Live, Online

Program Fee

USD 50

Contact Hours


Meet the teaching team

You will receive LIVE mentorship from top professors and exceptional teaching assistants

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In case you need more information about the course, fill in the form and we will share further details on the email address provided by you.

Thank you for your interest. We will be sharing further information about AI0 shortly to help you kickstart your Data Science journey.
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Student Stories

Varshini Reddy

Incoming graduate student at Harvard University

“The Univ.AI Foundation course gave me a structured learning environment. They helped me understand why one algorithm worked better than another for a given application. The quality of the peer group and the personalized time devoted by the professors are the two things that surprised me”

Olga Graph

Phd Mathematics, Technical University, Munich

“The learning experience at Univ.AI is highly engaging, interactive and lively. It is most definitely on par with the best universities in the world. I enjoyed the teaching style of Dr. Protopapas and the high level of care and dedication displayed by the academic team.”

Padmaja Bhagwat

Data Scientist at Glance, Ex- VMware

“My growth has been tremendous! I see a huge difference in the quality of code I write now compared to what I used to write in VMware. My new role requires me to pick up new tools quickly, and I think it’s because of my training at Univ.AI that I can adapt with great ease. Univ.AI helped me refine my concepts, and I think their program is the perfect training for any data scientist role.”

Sakthisree Venkatesan

Machine Learning Lead at Metro Services

“The mentorship we got during the program was a perfect complement to learning from top faculty. The curriculum is challenging, but for the committed students the learning experience is exceptional.  I was surprised at the pace at which I was able to develop my expertise.”

Anah Veronica

Software Engineer, Larsen & Toubro Infotech

“One of the things that made a big difference for me was learning with a smart and highly accomplished peer group. My classmates would often ask questions that I hadn’t thought of. Peer learning added greatly to an already inspiring learning experience.” 

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Course Outcomes

This course starts you off from scratch, 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 linear regression. Along the way, we will introduce foundational ideas of statistics, linear algebra and calculus.

At the end of this course, you will have the tools and the concepts needed to successfully undertake a rigorous course in Data Science and AI.

Topics covered in AI-0: The Basics of Data Science (formerly called PyDS) are pre-requisites for our Master ML & AI program or its first course [AI-1] AI-Basics

Who should enroll

This part-time, weekend only course is for college or university students and working professionals who seek a career in Machine Learning and AI, but do not have the requisite knowledge of python programming, basic statistics and mathematics.

What you will learn

Go deeper into what you will learn in each session in this 5-weekend course

Introduction to Python Programming

  • Introduction to Python
  • Data Types, iterators, python operations
  • Order of operations, logical operators

Python Data Structures, Flow Control

  • Python Data Structures - Lists, Dictionaries, Tuples
  • List/dictionary comprehensions
  • Enumeration

Python Functions

  • Python Functions - Arguments, keyword arguments, etc.
  • Anonymous functions (lambda function)

Python Classes

  • Classes: Constructors vs Instantiations
  • Methods vs. Attributes

Reading & Writing Files

  • Working with strings
  • String formatting
  • Reading & writing file

Code Debugging, Third-party Modules

  • Debugging skills
  • Exception handling
  • Finding documentation
  • Process of elimination

Probability & Statistics

  • Random Variable
  • Probability Density Function
  • Some ‘standard’ distributions and their mean/stdev (Normal, Binomial). Properties of mean and variance

Statistics with NumPy

  • Indexing / slicing
  • Shape & reshape
  • Zeros, ones, arbitrary array declaration

Linear Algebra, Calculus and Linear Regression

  • Derivatives (including partial)
  • Matrix Operations
  • Matrix Multiplication
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