NPTEL : NOC:Python for Data Science (Computer Science and Engineering)

Co-ordinators : Prof. Ragunathan Rengasamy


Lecture 1 - Introduction to Python for Data Science

Lecture 2 - Introduction to Python

Lecture 3 - Introduction to Spyder - Part 1

Lecture 4 - Introduction to Spyder - Part 2

Lecture 5 - Variables and Datatypes

Lecture 6 - Operators

Lecture 7 - Jupyter setup

Lecture 8 - Sequence data - Part 1

Lecture 9 - Sequence data - Part 2

Lecture 10 - Sequence data - Part 3

Lecture 11 - Sequence data - Part 4

Lecture 12 - Numpy

Lecture 13 - Reading data

Lecture 14 - Pandas Dataframes - I

Lecture 15 - Pandas Dataframes - II

Lecture 16 - Pandas Dataframes - III

Lecture 17 - Control structures and Functions

Lecture 18 - Exploratory data analysis

Lecture 19 - Data Visualization - Part I

Lecture 20 - Data Visualization - Part II

Lecture 21 - Dealing with missing data

Lecture 22 - Introduction to Classification Case Study

Lecture 23 - Case Study on Classification - Part I

Lecture 24 - Case Study on Classification - Part II

Lecture 25 - Introduction to Regression Case Study

Lecture 26 - Case Study on Regression - Part I

Lecture 27 - Case Study on Regression - Part II

Lecture 28 - Case Study on Regression - Part III

Lecture 29 - Module : Predictive Modelling

Lecture 30 - Linear Regression

Lecture 31 - Model Assessment

Lecture 32 - Diagnostics to Improve Linear Model Fit

Lecture 33 - Cross Validation

Lecture 34 - Classification

Lecture 35 - Logistic Regression

Lecture 36 - K-Nearest Neighbors (kNN)

Lecture 37 - K-means Clustering

Lecture 38 - Logistic Regression (Continued...)

Lecture 39 - Decision Trees

Lecture 40 - Multiple Linear Regression