NOC:Python for Data Science

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 - Lists Part - 1

Lecture 8 - Lists Part - 2

Lecture 9 - Tuples

Lecture 10 - Dictionary

Lecture 11 - Sets

Lecture 12 - Numpy Part - 1

Lecture 13 - Numpy Part - 2

Lecture 14 - Matrix

Lecture 15 - Linear algebra Part - 1

Lecture 16 - Linear algebra Part - 2

Lecture 17 - Reading data

Lecture 18 - Pandas Dataframes - I

Lecture 19 - Pandas Dataframes - II

Lecture 20 - Pandas Dataframes - III

Lecture 21 - Control structures and Functions

Lecture 22 - Exploratory data analysis

Lecture 23 - Data Visualization - Part I

Lecture 24 - Data Visualization - Part II

Lecture 25 - Dealing with missing data

Lecture 26 - Module : Predictive Modelling

Lecture 27 - Linear Regression

Lecture 28 - Model Assessment

Lecture 29 - Diagnostics to Improve Linear Model Fit

Lecture 30 - Multiple Linear Regression

Lecture 31 - Cross Validation

Lecture 32 - Classification

Lecture 33 - Logistic Regression

Lecture 34 - Logistic Regression (Continued...)

Lecture 35 - Performance Measures

Lecture 36 - K - Nearest Neighbors (kNN)

Lecture 37 - K - means Clustering

Lecture 38 - Decision Trees

Lecture 39 - Introduction to Classification Case Study

Lecture 40 - Case Study on Classification - Part I

Lecture 41 - Case Study on Classification - Part II

Lecture 42 - Introduction to Regression Case Study

Lecture 43 - Case Study on Regression - Part I

Lecture 44 - Case Study on Regression - Part II

Lecture 45 - Case Study on Regression - Part III