Foundation level in Programming and Data science
offered by IIT Madras

4.7 /5

(3 Reviews)

Public/Government Institute
Estd. 1959

Foundation level in Programming and Data science
at
IIT Madras
Overview

Gain a comprehensive overview of the Binomial Distribution and Normal distribution principles and concepts

Duration	8 months
Total fee	₹32,000
Mode of learning	Online
Credential	Certificate

Foundation level in Programming and Data science
at
IIT Madras
Highlights

Earn a certificate of completion from IIT Madras
One or more weekly online assignments
Three quizzes will be conducted at the end of Weeks

Foundation level in Programming and Data science
at
IIT Madras
Course details

Skills you will learn

Python Operations Statistics Data visualization Data Science

What are the course deliverables?

Create, download, manipulate, and analyse data sets
Describe data using numerical summaries and visual representations
Estimate chance by applying laws of probability
Calculating expectation and variance of a random variable
Frame questions that can be answered from data in terms of variables and cases

More about this course

The students will be introduced to large datasets. Using this data, the students will be introduced to various insights one can glean from the data
Basic concepts of probability also will be introduced during the course leading to a discussion on Random variables
The students will be introduced to a number of programming concepts using illustrative examples which will be solved almost entirely manually
This course aims at achieving fluency and confidence in spoken and written English
This course aims to introduce the basic concepts of linear algebra, calculus and optimization with a focus towards the application area of machine learning and data science

Foundation level in Programming and Data science
at
IIT Madras
Curriculum

Mathematics for Data Science I

Rectangular coordinate system, Straight Lines - Slope of a line, Parallel and perpendicular lines, Representations of a Line, General equations of a line, Straight-line fit

Quadratic Functions - Quadratic functions, Minima, maxima, vertex, and slope, Quadratic Equations

Algebra of Polynomials - Addition, subtraction, multiplication, and division, Algorithms, Graphs of Polynomials - X-intercepts, multiplicities, end behavior, and turning points, Graphing & polynomial creation

Functions - Horizontal and vertical line tests, Exponential functions, Composite functions, Inverse functions

Statistics for Data Science I

Describing categorical data Frequency distribution of categorical data, Best practices for graphing categorical data, Mode and median for categorical variable

Describing numerical data Frequency tables for numerical data, Measures of central tendency - Mean, median and mode, Quartiles and percentiles, Measures of dispersion - Range, variance, standard deviation and IQR, Five number summary

Association between two variables - Association between two categorical variables - Using relative frequencies in contingency tables, Association between two numerical variables - Scatterplot, covariance, Pearson correlation coefficient, Point bi-serial correlation coefficient

Basic principles of counting and factorial concepts - Addition rule of counting, Multiplication rule of counting, Factorials

Permutations and combinations

Computational Thinking

Variables, Initialization, Iterators, Filtering, Datatypes, Flowcharts, Sanity of data

Iteration, Filtering, Selection, Pseudocode, Finding max and min, AND operator

Multiple iterations (non-nested), Three prizes problem, Procedures, Parameters, Side effects, OR operator

Nested iterations, Birthday paradox, Binning

English I

Sounds and Words

Sentences

Listening Skills

Speaking Skills

Reading Skills

Writing Skills

Mathematics for Data Science II

Function of One variable - -Some Topics from Maths 1 -Function of one variable -Graphs and Tangents -Limits for sequences -Limits for function of one variable

Derivatives, Tangents and Critical points - -Limits and Continuity -Differentiability and the derivative -Computing derivatives and L'H'opital's rule -Derivatives, tangents and linear approximation -Critical points: local maxima and minima

Integral of a function of one variable - -Computing areas, Computing areas under a curve, The integral of a function of one variable -Derivatives and integrals for functions of one variable

Vectors, matrices and their applications - Vectors, Matrices, Systems of linear equations, Determinants

Statistics for Data Science II

Multiple random variables - Two random variables, Multiple random variables and distributions

Multiple random variables - Independence, Functions of random variables - Visualization, functions of multiple random variables

Expectations Casino math, Expected value of a random variable, Scatter plots and spread, Variance and standard deviation, Covariance and correlation, Inequalities

Continuous random variables Discrete vs continuous, Weight data, Density functions, Expectations

Programming in Python

Introduction to algorithms

Conditionals

Iterations and Ranges

Basic Collections in Python

File Operations

Module system in python

Basic Pandas and Numpy processing of data

English II

Patterns in Sentences

Listening Skills

Speaking Skills

Reading Skills

Writing Skills

Social Skills

Foundation level in Programming and Data science
at
IIT Madras
Faculty details

Neelesh Upadhye

Experienced Associate Professor with a demonstrated history of working in the higher education industry. Skilled in Mathematical Modeling, R, Stochastic Modeling, and Statistical Modeling. Strong education professional with a Doctor of Philosophy (Ph.D.) focused in Mathematical Statistics and Probability from Indian Institute of Technology, Bombay.

Madhavan Mukund

Madhavan Mukund studied at IIT Bombay (BTech) and Aarhus University (PhD). He has been a faculty member at Chennai Mathematical Institute since 1992, where he is presently Deputy Director and Dean of Studies. His main research area is formal verification.

Usha Mohan

Usha Mohan holds a Ph.D. from Indian Statistical Institute. She has worked as a researcher in ISB Hyderabad and Lecturer at University of Hyderabad prior to joining IIT Madras. She offers courses in Data analytics, Operations research, and Supply chain management to under graduate, post graduate and doctoral students.

Andrew Thangaraj

Andrew Thangaraj received his B. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Madras in 1998 and Ph.D. in Electrical Engineering from the Georgia Institute of Technology, Atlanta, USA in 2003.