Visualization for Data Science using Python.

Course Curriculum

Chapter 1: Basics of Statistics

Lecture 1: Quick Introduction

Lecture 2: What is a random variable

Lecture 3: Nominal and Ordinal Data

Lecture 4: Central tendency – Introduction

Lecture 5: Central tendency – Examples

Lecture 6: Data Visualization

Lecture 7: Types of Quartile, Inter Quartile Range

Lecture 8: Types of Quartile, Inter Quartile Range – Example

Lecture 9: Standard Deviation & Variance

Lecture 10: Sample Standard Deviation

Lecture 11: Co Variance

Lecture 12: Normal Distribution

Lecture 13: Chi Square Distribution

Lecture 14: Chi Square Goodness of Fit

Lecture 15: Association between Categorical variables

Lecture 16: Correlation

Chapter 2: Visualization of Iris Dataset using Seaborn and Matplotlib

Lecture 1: Introduction to EDA

Lecture 2: Iris Dataset

Lecture 3: Scatter Plot

Lecture 4: Two dimensional Scatter plot

Lecture 5: Three dimensional scatter plot

Lecture 6: Pair plots

Lecture 7: One dimensional scatter plot

Lecture 8: Histogram, PDF, CDF

Lecture 9: Kde plots

Lecture 10: Kde plot – Intuition

Lecture 11: PDF and its properties

Lecture 12: CDF – Code snippet

Lecture 13: Mean, Median, Standard deviation, MAD – Code snippet

Lecture 14: Box plots

Lecture 15: Violin plot

Chapter 3: Visualization of Haberman dataset

Lecture 1: Haeberman Data – Introduction

Lecture 2: Data Overview

Lecture 3: Univariate Analysis

Lecture 4: Bivariate Analysis

Chapter 4: Linear Algebra

Lecture 1: Introduction to Linear Equations

Lecture 2: Application of Linear Algebra

Lecture 3: What is a scaler

Lecture 4: What is a point and distance between 2 points

Lecture 5: What is a vector

Lecture 6: Row and Column Vector

Lecture 7: Transpose of a Matrix

Lecture 8: Unit Vector

Lecture 9: Vector Addition and Subtraction

Lecture 10: Inverse of a vector

Lecture 11: Dot Product between two vectors

Lecture 12: Multiplication of a vector with a scaler

Lecture 13: Angle between 2 vectors – Part 1

Lecture 14: Angle between 2 vectors – Part 2

Lecture 15: Orthogonal Vectors

Lecture 16: Orthonormal vectors

Lecture 17: Equation of a line – Part 1

Lecture 18: Equation of a line – Part 2

Lecture 19: Equation of a line – Part 3

Lecture 20: Equation of a line – Part 4

Lecture 21: Projection of a point on a line

Lecture 22: Distance of a point from a line

Lecture 23: How to determine point on the negative and positive side of a line

Lecture 24: Matrix Introduction

Lecture 25: Matrix Operations

Lecture 26: Symmetric, Square, Identity and Diagonal Matrix

Lecture 27: Orthogonal Matrix

Lecture 28: Minor, Cofactor and Determinant of a Matrix (Optional)

Lecture 29: Inverse of a matrix (Optional)

Chapter 5: Principal Component Analysis

Lecture 1: Preface for Dimensionality Reduction – Part 1

Lecture 2: Preface for Dimensionality Reduction – Part 2

Lecture 3: Preface for Dimensionality Reduction – Part 3

Lecture 4: Preface for Dimensionality Reduction – Part 4

Lecture 5: Preface for Dimensionality Reduction – Part 5

Lecture 6: Gometric Intuition of PCA

Lecture 7: Mathematical formulation of PCA – Part 1

Lecture 8: Mathematical formulation of PCA – Part 2

Lecture 9: Mathematical formulation of PCA – Part 3

Lecture 10: Failure cases of PCA

Lecture 11: Connecting Colab to Gdrive

Lecture 12: Understanding MNIST dataset

Lecture 13: Visualizing MNIST single digit

Lecture 14: MNIST Visualization – Method 1

Lecture 15: MNIST Visualization – Method 2