Statistics for AI Data Science and Business Analysis – 2024

Course Curriculum

Chapter 1: Foundation of Statistics

Lecture 1: Introduction to Statistics

Lecture 2: Types of Statistical Analysis – Descriptive Statistics

Lecture 3: Types of Statistical Analysis – Inferential Statistics

Lecture 4: How Statistics and Machine Learning are Related

Lecture 5: Understanding the Types of Data

Lecture 6: Sampling Techniques

Lecture 7: Descriptive Statistics – Measure of Central Tendency

Lecture 8: Descriptive Statistics – Measures of Dispersion – Range & Interquartile Range

Lecture 9: Descriptive Statistics – Measures of Dispersion – Variance & Standard Deviation

Lecture 10: Hands On – Exercise with Python

Lecture 11: Descriptive Statistics – Measures of Shape

Lecture 12: Descriptive Statistics – Measures of Position

Lecture 13: Descriptive Statistics – Standard Scores

Lecture 14: Descriptive Statistics – Hands On

Lecture 15: Problem Statement – Wine Reviews Data Set Analysis

Lecture 16: Solution for Project 1

Lecture 17: Project 2 – Customer Income Data Analysis

Lecture 18: Solution for Project 2

Lecture 19: Project 3 – US Arrests Dataset

Lecture 20: Solution for Project 3 – US Arrests Dataset

Lecture 21: Project 4 – BigMart Sales data analysis

Lecture 22: Solution for Big Mart Data Analysis

Lecture 23: Quick Summary of Descriptive Statistics

Chapter 2: Exploratory Data Analysis

Lecture 1: Introduction to Exploratory Data Analysis

Lecture 2: Types of Data Analysis

Lecture 3: Univariate Non Graphical EDA & Outlier Analysis

Lecture 4: Univariate Graphical EDA & Hands On

Lecture 5: Multivariate Non Graphical EDA

Lecture 6: Multi variate Graphical EDA

Lecture 7: Steps in EDA

Lecture 8: Summary of Graphical EDA Techniques

Lecture 9: Hands On EDA on Titanic Data Set

Lecture 10: Project 5 – Crimes in Boston City

Lecture 11: Project 5 – Solution

Lecture 12: Project 6 – PUBG Game Analysis

Lecture 13: Project 6 – PUBG Game Analysis – Solution

Lecture 14: Project 7 – FIFA Game Analysis

Lecture 15: Project 7 – Solution

Lecture 16: Project 8 – Covid19 Data Analysis

Lecture 17: Project 8 Solution

Chapter 3: Probability

Lecture 1: Introduction to Probability

Lecture 2: Key Terminology of Probability

Lecture 3: Rules of Probability

Lecture 4: Marginal Probability , Joint Probability

Lecture 5: Disjoint Events and Non Disjoint events

Lecture 6: Independent and Dependent events

Lecture 7: Product Rule of Dependent & Independent Events

Lecture 8: Task with Manifold Bank and compute probability

Lecture 9: Bayes Theorem

Lecture 10: Bayes Theorem in Data Science

Lecture 11: Hands On : Bayes Algorithm in Python

Lecture 12: Random Variables

Lecture 13: Various Distribution functions

Lecture 14: Hands ON : Generate the Discrete & Continuous Random numbers

Lecture 15: Central Limit Theorem and Hands On

Lecture 16: Applications of Probability Distributions

Lecture 17: Hands On : Transform the data to get Normal Distribution curve

Lecture 18: Example Problems for Probability

Lecture 19: Project 9 – Cars Dataset & Solution

Lecture 20: Hands On – Bayes Theorem

Lecture 21: Project 10 – Hands On – Normal Distribution & CDF

Chapter 4: Inferential Statistics

Lecture 1: Introduction to Inferential Statistics

Lecture 2: Key Terminology of Inferential Statistics

Lecture 3: Hands On – Population & Sample

Lecture 4: Types of Statistical Inference

Lecture 5: Confidence Interval – Margin of Error – Confidence Interval Estimation

Lecture 6: Demo – Margin of Error and Confidence Interval

Lecture 7: Hypothesis Testing & Steps of Hypothesis testing

Lecture 8: ZTest and Example Problem

Lecture 9: ZTest Solution Hands On

Lecture 10: 1 Sample t-test

Lecture 11: 1 sample t-test Hands On

Lecture 12: 2 Sample t-test

Lecture 13: 2 sample t-test Hands On

Lecture 14: Paired Sample t-test

Lecture 15: Hands On – Paired Sample t-test

Lecture 16: Chi-Square Goodness of Fit

Lecture 17: Hands On – Chi Square test

Lecture 18: Anova

Lecture 19: Hands On – Anova

Lecture 20: Project 11 – Inferential Statistics – cars

Lecture 21: Project 11 – Solution

Lecture 22: Project 12 – Blood Pressure health dataset

Lecture 23: Project 12 – Solution

Lecture 24: Project 13 – Students admissions dataset

Lecture 25: Project 13 – Solution

Chapter 5: Section 5 – Linear Regression

Lecture 1: Introduction to Regression , What , Why and Types of Problem we can solve

Lecture 2: Assumptions of Linear Regression

Lecture 3: Intuition of Linear Regression

Lecture 4: Linear Regression with Normal Equation

Lecture 5: Apply Linear Regression using Sklearn – Hands On

Lecture 6: Checking Assumption of Linear Regression – Hands On

Lecture 7: How Good is your fit ?

Lecture 8: How Minimisation of Error is performed – Gradient Descent

Lecture 9: Gradient Descent Hands On Part 1