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