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
