** DA 202-O Introduction to Data Science 3:1 (August 2021) **

**Course Instructor:** Sashikumaar Ganesan, CDS and Deepak Subramani, CDS

**Course description:** This course will provide an introduction to Data Science. This four-credit course will be offered every year
in the August-December term as a core course to MTech (online) programme. This course is aimed to be an introductory graduate-level (200-series) course.

** Syllabus**

Data Science Fundamentals: Identifying and framing a data science problem in different fields; Data - Types, Pre-processing; Different
types of Analytics; Introduction to Machine Learning, Artificial Intelligence.

Basic Programming: Data structures, if-else, loops; Visualization; Handling structured data

Probability: Probability axioms, Conditional Probability, Bayes' Theorem, Independence, Counting Problems, Discrete and Continuous Random Variables,
Expectation, Iterated Expectation, Total Law of Probability, Covariance, Correlation, Entropy, Mutual Information.

Computational Methods: Calculus for Data Science: Functions, Derivative, Partial derivative, Gradient of vector-valued functions and matrices and
automatic differentiation, Second derivative Hessian matrix.

Linear Algebra: Vectors, Basis, Linear Dependence and Independence, Tensors, Scalars, Inner Products, Outer product, Norms, Basis, Orthogonal and
Orthonormal Vectors, Orthogonalization and Normalization.

Matrix Linear transformation: Frobenius Norm, Matrix Multiplication, Solutions of system of algebraic equations; Matrix Decomposition: QR Factorization,
Singular Value Decomposition; Cholesky Decomposition, Eigen Value Decomposition.

** Textbooks / References**

- Shah, Chirag. A Hands-On Introduction to Data Science. Cambridge University Press, 2020.
- Bertsekas, Dimitri P., and John N. Tsitsiklis. Introduction to Probability. Vol. 1. Belmont, MA: Athena Scientific, 2002.
- Shaw, Zed A. Learn python 3 the hard way: A very simple introduction to the terrifyingly beautiful world of computers and code. Addison-Wesley Professional, 2017.
- G4. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020. (https://mml-book.github.io)
- Gibert Strang. Linear Algebra and Learning from Data. Wellesley-Cambridge Press, 2019
- Gibert Strang. Linear Algebra for Everyone, Wellesley-Cambridge Press, 2020

**Prerequisites: ** Basic knowledge of mathematics

** Grading: **

- Homework assignments (four) 40%
- Mid-term exam 30%
- Final exam 30%.