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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Course Overview

The Foundational Level in Programming and Data Science certification course is a foundation level course that will simplify the learning process of data science, and programming concepts. This programme is available and developed by the Centre for Continuing Education, IIT Madras. 

Apart from the theoretical concepts, the course will help in developing the skills of candidates through practical implementation too.

Foundational Level in Programming and Data Science training during the learning process will further delve into the concepts of maths, statistics, and python programming. This certificate course is ideal even for those students who have completed their 10+2 level of education, hence no prior knowledge is expected of the candidates. The topics covered under this course have been carefully chosen by the faculty at IIT Madras, so the candidates are not left out from learning the trendy concepts, and skills. The candidates with the end certificate can apply in various industries where they shall be offered roles like data scientists or programmers.

The Highlights

  • Foundation level course
  • Total 8 courses
  • Online degree programme
  • 1-3 years course
  • 10 hrs per course every week
  • IIT Madras certified course
  • Completion certificate

Important dates

Application Date

End Date : 26 May, 2024

Programme Offerings

  • Online assignment
  • Coursework
  • assessments
  • Quiz
  • 32 Credits
  • Video content

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesIIT Madras

The candidates registering for the Foundational Level in Programming and Data Science need to pay Rs. 32,000 where each of the 8 courses cost Rs. 4000 each. Also, an application fee of Rs. 3000 for regular students has to be submitted while applying for this programme.

Foundational Level in Programming and Data Science Fee Structure

Description

Amount in INR

Programme Fee 

Rs. 32,000

Application Fee, Regular Students

Rs. 3,000

Application Fee for PwD (>= 40% disability) / SC / ST category 

Rs. 1,500

Application Fee for PwD (>= 40% disability) and SC / ST category

Rs. 750


Eligibility Criteria

Educational qualification

Candidates who wish to apply for the course through regular entry must hold the following eligibility criteria:

For candidates passing class 12 or equivalent in 2019 or earlier:

  • Should have pursued English, and Mathematics in class 10.
  • Must have cleared class 12 in the year 2019 or earlier.

For candidates passing class 12 in 2020 or 2021:

  • Must have studied Mathematics and English in class 10.
  • Cleared class 12 or equivalent in 2020 or 2021.
  • The candidate should be:
  • Currently enrolled/previously enrolled in any Bachelor's Degree program in campus mode OR
  • Completed 1 year (campus mode only) diploma OR
  • Completed CA Inter

Certification Qualifying Details

  • For securing the Foundational Level in Programming and Data Science certificate by Centre for Continuing Education, IIT Madras must complete learning all the 8 different courses, its assignments, quizzes, and end exam in this foundational level.

What you will learn

Mathematical skillStatistical skillsKnowledge of AlgorithmsData science knowledgeKnowledge of Python

With the Foundational Level in Programming and Data Science certification syllabus, the students will be learning the foundational concepts related to data science, and programming languages like mathematicsstatisticspython, English, computational thinking, and more. These concepts will help the students to proceed towards diploma-level courses. 


Who it is for

The candidates who want to brush up their skills in data science, and programming, and want to learn to build applications using these concepts, can register for the Foundational Level in Programming and Data Science by IIT Madras.


Admission Details

To apply for the Foundational Level in Programming and Data Science classes, the candidates are requested to see the below-mentioned process.

Step 1: Firstly, they have to visit the official URL of the programme:

https://onlinedegree.iitm.ac.in/academics.html#AC11.

Step 2: The students are then requested to ‘Sign In’ with the required details. 

Step 3: New users need to create their new accounts.

Step 4: Candidates who have their User ID and password can directly sign in

Step 5: Candidates need to then fill in some details and upload the necessary documents. 

Step 6: The candidates will then need to pay the application fees, after which they will be offered admission.

Application Details

The application to this programme has to be done using only Google accounts. If the participants have other email addresses, then they will have to enable their emails with Google, and then sign up.

The Syllabus

Week 1
  • Set Theory
  • Number system
  • Sets and their operations
  • Video 1: Introduction
  • Video 2: Natural Numbers and Their Operations
  • Video 3: Rational Numbers
  • Video 4: Real and Complex Number
  • Video 5: Set Theory
  • Video 6: Construction of Subsets and Set Operations
  • Video 7: Sets: Examples
  • Video 8: Examples of Set Operations and Counting Problems
  • Video 9: Relations
  • Video 10: Functions
  • Video 11: Relations: Examples
  • Video 12: Function: Examples
  • Video 13: Prime Numbers
  • Video 14: Why is a Number Irrational?
  • Online Assignment
Week 2
  • Relations and functions
  • Relations and their types
  • Functions and their types
  • Rectangular coordinate system
Week 3
  • Straight Lines
  • The slope of a line
  • Parallel and perpendicular lines
  • Representations of a Line
  • General equations of a line
  • Straight-line fit
Week 4
  • Quadratic Functions
  • Quadratic functions
  • Minima, maxima
  • Vertex, and slope
  • Quadratic Equations
Week 5
  • Algebra of Polynomials
  • Addition
  • Subtraction
  • Multiplication
  • Division
  • Algorithms
Week 6
  • Graphs of Polynomials - X-intercepts,
  • Multiplicities
  • End behavior, and turning points
  • Graphing & polynomial creation
Week 7
  • Functions - Horizontal and vertical line tests
  • Exponential functions
  • Composite functions
  •  Inverse functions
Week 8
  • Logarithmic Functions - Properties
  • Graphs
  • Exponential equations
  • Logarithmic equations
Week 9
  • Graph Theory - Representation of graphs,
  • Breadth-first search
  • Depth-first search
  • Applications of BFS and DFS
Week 10
  • Directed Acyclic Graphs - Complexity of BFS and DFS
  • Topological sorting and longest path, 
  • Transitive closure
  • Matrix multiplication
Week 11
  • Graph theory Algorithms - Single-source shortest paths
  • Dijkstra's algorithm
  • Bellman-Ford algorithm
  • All-pairs shortest paths
  • Floyd–Warshall algorithm
  • Minimum cost spanning trees
  • Prim's algorithm
  • Kruskal's algorithm
Week 12
  • Revision

Week 1
  • Introduction and type of data
  • Types of data
  • Descriptive and Inferential statistics
  • Scales of measurement
  • Video 1: Introduction
  • Video 2: Course Overview and Week-wise Schedule
  • Video 3: Introduction and Types of Data - Basic Definitions
  • Video 4: Introduction and Types of Data - Understanding Data
  • Video 5: Introduction and Types of Data - Classification of Data
  • Video 6: Introduction and Types of Data - Scales of Measurement
  • Video 7: Tutorial 1 - Google Spreadsheets Introduction
  • Video 8: Tutorial 2 - Formatting Google Spreadsheets
  • Video 9: Tutorial 3 - Spreadsheet Formulae
  • Video 10: Tutorial 4 - Downloading and Uploading Spreadsheets
  • Online Assignment
Week 2
  • Describing categorical data
  • Frequency distribution of categorical data
  • Best practices for graphing categorical data
  • Mode and median for categorical variable
Week 3
  • 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
Week 4
  • 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
Week 5
  • Basic principles of counting and factorial concepts
  • Addition rule of counting
  • Multiplication rule of counting
  • Factorials
Week 6
  • Permutations and combinations

Week 7
  • Probability Basic definitions of probability
  • Events
  • Properties of probability
Week 8
  • Conditional probability
  • Multiplication rule
  • Independence
  • Law of total probability
  • Bayes’ theorem
Week 9
  • Random Variables - Random experiment
  • Sample space and random variable
  • Discrete and continuous random variable
  • Probability mass function
  • Cumulative density function
Week 10
  • Expectation and Variance
  • The expectation of a discrete random variable
  • Variance and standard deviation of a discrete random variable
Week 11
  • Binomial and poisson random variables
  • Bernoulli trials
  • Independent and identically distributed random variable
  • Binomial random variable
  • Expectation and variance of a binomial random variable
  • Poisson distribution
Week 12
  • Introduction to continuous random variables 
  • The area under the curve
  • Properties of pdf
  • Uniform distribution
  • Exponential distribution

Week 1
  • Variables
  • Initialization
  •  Iterators
  • Filtering
  • Data Types
  • Flowcharts
  • Sanity of data
  •  Video 1: Introduction to Course
  •  Video 2: Introduction to Datasets
  • Video 3: Concept of Variables, Iterators, and Filtering
  • Video 4: Tutorial on Concept of Variables, Iterators and Filtering
  • Video 5: Iterations using Combination of Filtering Conditions
  • Video 6: Tutorial on Iterations using Combination of Filtering Conditions
  • Video 7: Local Operations and Max in Single Iteration - Part 1
  • Video 8: Local Operations and Max in Single Iteration - Part 2
  • Video 9: Local Operations and Max in Single Iteration - Part 3
  • Video 10: Local Operations and Max in Single Iteration - Part 4
  • Online Assignment
Week 2
  • Iteration
  • Filtering
  • Selection
  • Pseudocode 
  • Finding max and min
  • AND operator
Week 3
  • Multiple iterations (non-nested)
  • Three prizes problem
  • Procedures
  • Parameters
  • Side effects
  • OR operator
Week 4
  • Nested iterations
  • Birthday paradox
  • Binning
Week 5
  • List
  • Insertion sort
Week 6
  • Table
  • Dictionary
Week 7
  • Graph
  • Matrix
Week 8
  • Adjacency matrix
  • Edge labeled graph
Week 9
  • Backtracking, Tree
  • Depth First Search (DFS)
  • Recursion
Week 10
  • Object oriented programming
  • Class
  • Object, Encapsulation
  • Abstraction
  •  Information hiding
  • Access specifiers
Week 11
  • Message passing
  • Remote Procedure Call (RPC)
  • Cache memory, Parallelism, Concurrency
  • Polling
  • Preemption
  • Multithreading
  • Producer-Consumer
  • Atomicity, Consistency
  • Race condition
  • Deadlock
  • Broadcasting
Week 12
  • Top-down approach
  • Bottom-up approach
  • Decision tree
  • Numerical prediction
  • Behavior analysis
  • Classification

Week 1

Sounds and Words

  • Video 1: Introduction
  • Video 2: Sounds and Writing Symbols in English
  • Video 3: Speech Sounds (Vowels) in English
  • Video 4: Consonant Sounds in English
  • Video 5: Language Learning and Use
  • Video 6: How to Improve Your Language Skills?
  • Video 7: Telephone English
  • Online Assignment
Week 2
  • Sounds and Words (Continued)

Week 3
  • Sounds and Words (Continued)

Week 4
  • Sentences

Week 5
  • Sentences (Continued)

Week 6
  • Listening Skills

Week 7
  • Listening Skills (Continued)

Week 8
  • Speaking Skills

Week 9
  • Speaking Skills (Continued)

Week 10
  • Reading Skills

Week 11
  • Writing Skills

Week 12
  • Writing Skills (Continued)

Week 1
  • Function of One variable
  • Some Topics from Maths 1
  • Function of one variable
  • Graphs and Tangents
  • Limits for sequence
  • Limits for the function of one variable
Week 2
  • Derivatives
  • Tangents and Critical points 
  • Limits and Continuity
  • Differentiability and the derivative
  • Computing derivatives and L’Hˆopital’s rule
  • Derivatives, tangents, and linear approximation
  • Critical points: local maxima and minima
Week 3
  • 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
Week 4
  • Vectors
  • Matrices and their applications
  • Vectors, Matrices
  • Systems of linear equations
  • Determinants
Week 5
  • Solving system of linear equations 
  • Cramer’s rule
  • Solutions to a system of linear equations with an invertible coefficient matrix
  • The echelon form
  • Row reduction
  • The Gaussian elimination method
Week 6
  • Vector space - Introduction to vector space
  • Linear dependence and independence
  • Basis for a vector space
Week 7
  • Rank, Nullity of a matrix
  • Introduction to a linear transformation
  • Dimension of a vector space
  • Rank and nullity using Gauss elimination\
  • Nullspace of a matrix
  • Linear mapping and linear transformation
Week 8
  • Matrix representation of Linear transformation
  • Affine subspaces and affine mappings Linear transformations
  • Ordered basis and matrices
  • Equivalence and similarity of matrices
  • Affine subspaces and affine mappings
Week 9
  • Inner products and norms on a vector space
  • Lengths and angles
  • Inner products and norms on a vector space
  • Orthogonal and Orthonormal basis
  • Projections using inner products
  • The Gram-Schmidt process
  • Orthogonal transformations and rotations
Week 10
  • Multivariate thinking - Visualization of multivariable functions

Week 11
  • Vector calculus
  • Vector calculus
  • Scalar-valued functions
  • Partial derivatives
  • Vector-valued functions
  • The gradient function
  • Continuity, differentiability
Week 12
  • Critical points: Maximum and minimum values
  • Gradients and linear approximation
  • Critical points: Maximum and minimum values
Week 8
  • Estimation and Inference II

Week 1
  • Events and probabilities - Basic concepts 
  • Probability simulations
  • Working with probability spaces
  • Conditional probability
  • Birthday problem
  • Monty Hall problem Bayes' theorem and independence - Polya's urn scheme 
  • Law of total probability
  • Bayes' theorem
  • Independence
  • Repeated trials
  • Gambler's ruin (random walk)
Week 2
  • Discrete random variables - Random variables
  • Common distributions
  • Functions of one random variable
Week 3
  • Multiple random variables - Two random variables
  • Multiple random variables and distributions
  • Independence
  • Functions of multiple random variables
Week 4
  • Expectations Casino math
  • The expected value of a random variable
  • Scatter plots and spread
  • Variance and standard deviation
  • Covariance and correlation
  • Inequalities
Week 5
  • Continuous random variables Discrete vs continuous
  • Weight data
  • Density functions
  • Expectations
Week 6
  • Multiple continuous random variables - Height and weight data, 
  • Two continuous random variables
  • Averages of random variables - Colab illustration, Limit theorems, IPL data - histograms and approximate distributions
  • Jointly Gaussian random variables Probability models for data - Simple models, Models based on other distributions
  • Models with multiple random variables, dependency 
  • Models for IPL powerplay
  • Models from data
Week 7
  • Estimation and Inference I

Week 9
  • Hypothesis testing I

Week 10
  • Hypothesis Testing II

Week 11
  • Linear Regression I

Week 12
  • Linear Regression II

Week 1
  • Introduction to algorithms

Week 2
  • Conditionals

Week 3
  • Conditionals (Continued)

Week 4
  • Iterations and Ranges

Week 5
  •  Iterations and Ranges (Continued)

Week 6
  • Basic Collections in Python
Week 7
  • Basic Collections in Python (Continued)

Week 8
  • Basic Collections in Python (Continued)

Week 9
  • File Operations
Week 10
  • File Operations (Continued)

Week 11
  • Module system in python
Week 12
  • Basic Pandas and Numpy processing of data

Week 1
  • Patterns in Sentences

Week 2
  • Patterns in Sentences (Continued)

Week 3
  • Patterns in Sentences (Continued)

Week 4
  • Listening Skills
Week 5
  • Listening Skills (Continued)

Week 6
  • Speaking Skills

Week 7
  • Speaking Skills (Continued)

Week 8
  • Reading Skills

Week 9
  • Writing Skills

Week 10
  • Writing Skills (Continued)

Week 11
  • Social Skills

Week 12
  • Social Skills (Continued)

Evaluation process

At the end of every course, end-term exams, and invigilated quizzes are planned to be conducted across a number of cities in India, Sri Lanka, and UAE. All the exams and the in-person quizzes are scheduled to be held on weekends. A candidate will be declared to have passed a course when the total course score exceeds 50 out of 100. 

Instructors

IIT Madras Frequently Asked Questions (FAQ's)

1: Which mode of payment is acceptable?

The candidates can pay the fees online only as no other offline methods are considered. 

2: Will live classes be conducted for the Foundational Level in Programming and Data Science online certification?

The participants are only given pre-recorded videos of the classes on a weekly basis.

3: Can different changes be made in the form once submitted?

For rectification in the form details, the candidates must mail the same to the help desk of IIT Madras.

4: Is there any way of marking the Foundational Level in Programming and Data Science online course attendance?

There is no stated way of marking or tracking the attendance of the candidates.

5: Can the students give the final exam on other dates than mentioned on the course website?

The training has exam dates that are not flexible, hence cannot be changed.

6: In which language are the instructions for the Foundational Level in Programming and Data Science online programme given?

The instructions are given in English as this language is the medium of instruction.

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