AI-0

The Basics of Data Science

The objective of this module is to provide fundamental understanding of the python programming language needed to follow an introductory course in 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.

duration
5 weekends
Format
Live Online
course fee
₹3,750
$50
Next Cohort
To be decided
Classroom based

You learn with a group - your cohort. Build lifelong relationships and learn from one another.

LIVE, from the very best

We seek to recreate the learning environment of elite learning institutions, specifically - the Harvard M.S. in Data Science & AI

Intensive mentoring

Help is always on hand, always live, and more of it than anywhere else in the world

Meet your teacher

Our teachers hail from some of the world’s leading institutions and research labs in AI & Data Science.
In numbers
35

+

Contact Hours
12

+

Hrs of classes
10

+

Hrs of guided labs
10

+

faculty office hours
1

+

Group projects

Modules

Week 
1
Session 
1
Introduction of Pyton Programming
Session 
2
Pythin Data Structures, Flow control
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Week 
2
Session 
3
Python Functions
Session 
4
Python Syntax
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Week 
3
Session 
5
Python Classes
Session 
6
Files, Strings & Regular Expression
Week 
4
Session 
7
Numerical Python(Numpy)
Session 
8
Introduction to Probability and Statistics
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Week 
5
Session 
9
Panel Data(Pandas)
Session 
10
Linear regression
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Eligibility

Who is it for?

This program is for those who seek to specialise in Data Science & ML.

Prerequisites

Your are expected to have a working knowledge of python, along with these three libraries:

  • *Numpy
  • Pandas
  • Matplotlib

All exercises in this course will be done in jupyter notebooks.

In addition to this, you are expected to know the material covered in our AI0 course. Before you begin the course, we have prepared for you a simple exercise to ensure your proficieny of the above libraries. This will help you assess your preparedness for the course, and will also help you familiarize yourself with the platform.

*Note: No prior knowledge of machine learning libraries is necessary for this module

Textbooks

You are advised to follow the following two e-books for reference during the course:

  1. Speech and Language Processing, Dan Jurafsky and James H. Martin
  2. Natural Language Processing, Jacob Eisenstein

Before you begin the course, we have prepared for you a simple exercise to ensure your proficieny of the above libraries.This will help you assess your preparedness for the course, and will also help you familiarize yourself with the platform.

How it works

Lectures

We have very carefully designed the coursework to give you, the student, a wholesome learning experience. We will hold two weekend sessions per week for a total of five weeks.

01
Pre-Session

Before the session begins, students are expected to complete a pre-class reading assignment and attempt a quiz.

02
During Session

During the session, we will have live instruction interspaced with collaborative coding in small groups assisted by our teaching assistants. This will help you develop intuition for the core concepts and provide guidance on technical details.

03
Post-Session

After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class.

session structure

Labs

A lab is a TA driven 1.5 hour session that is divided into 3 major parts

Each lab begins by revisiting the quizzes and exercises done in the previous lecture session.After discussing exercises, we will have a semi-formal Q/A session.

All doubts pertaining, but not limited, to the previous session, and homeworks are welcome. The last part of the labs deals predominantly with the upcoming homeworks. It is directed towards giving a brief overview of the homework problem. We will discuss some code to help you get started.

Lab structure

Outcomes

Data Scientists use math and coding to solve complex business problems and predict future outcomes

Job Skills

On completion of the course you would gain the following skills:

  • Knowledge of Python data structures and flow control
  • Understanding of Python functions and classes
  • Familiarity with code debugging and third-party modules
  • Knowledge of probability and statistics
  • Understanding of linear algebra, calculus, and linear regression

Student Projects

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Sample class

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We believe in the idea of active learning and our course is designed with the expectation of active participation from the students. Please find a demo of our course style and pedagogy.

Meet our Students & Alumni

Meet our Students & Alumni

Our early Alumni are going places

Frequently asked questions

Do I get a certificate for completing each course?
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For each course you complete successfully, you get a course completion certificate and a grade. All certificates are granted by Univ.AI, and signed by our founding faculty, who are Harvard and UCLA professors.

Do I get college credits for taking Univ.AI courses?
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No, we do not offer college credits for our courses. However, if you choose to pursue further education, based on knowledge acquired at Univ.AI, you will be able to test out of required courses at many universities around the world.

How are students graded and evaluated?
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We conduct both group and individual evaluations. Evaluations are based on your class performance (exercises and quizzes), labs, homeworks, and projects. We ask that you always participate and attempt exercises even if you get them wrong: participation counts towards your grade. 


Detailed evaluation criteria may vary from course to course, and are published prior to each course. Below is a representative grading structure:

  • Quiz & Exercises: 25%
  • Homework assignments: 35%
  • Participation: 10%
  • ~Forums: 5%
  • ~Lectures: 5%
  • Course-end Project: 20%


How do you mentor students at Univ.AI?
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The frequency of live sessions ensures that help is always on hand. 


Highly trained Teaching Assistants (TAs), serve as course mentors. RIght from the in-class exercise during professors’ lectures, to labs, office hours and discussion forums. TAs mentor each step of your learning journey - LIVE. Typically, they will be alongside you, live, with other members on a shared screen, discussing questions, helping clarify concepts and when necessary, typing in lines of code. TAs also grade your work. 


This real-time, live mentoring by TAs makes a decisive and unparalleled difference to the learning experience at Univ.AI.

How much time will I need to commit each week to do well at Univ.AI?
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Programs at Univ.AI are run one-course-at-a-time, and require about 12-15 hours of your time each week, including live classes, labs, office hours, homeworks, and projects. 


Every week, you have two classes of about 90 minutes each


Each session lasts about 90 minutes. 


If you can commit that kind of time every week to learn a new field, then we welcome you to apply for Univ.AI programs in Data Science, and AI irrespective of your stage of career.

What happens if I miss a session?
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We make session recordings available on video shortly after they are completed. Most participants tend to view the lecture video and then follow that up with a live Lab and office hours to catch-up. 


TAs often stay after labs and office hours to answer questions. There are lots of opportunities to catch up with missed sessions.

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