Analytics For All

Course Curriculum

Chapter 1: Welcome

Lecture 1: Our course design

Lecture 2: Agenda

Lecture 3: Course Material For AFA

Chapter 2: Hypothesis Testing

Lecture 1: Telecom Churn- Case under Study

Lecture 2: The basics-High School Math you've probably forgotten

Lecture 3: Mean and Medians

Lecture 4: Probability: The reason you probably haven't won the lottery

Lecture 5: Confidence Intervals

Lecture 6: Hypothesis Testing

Lecture 7: ANOVA: Your car brand and your dinner bill

Lecture 8: T Stat- Your first new Statistic

Lecture 9: Example

Lecture 10: 1 sample t test- Checking Means

Lecture 11: 2-sample T test: Does TV make you buy things?

Lecture 12: Annova

Lecture 13: Goodness of Fit

Lecture 14: Chi Square TOI: One Category on another

Lecture 15: Cheat Sheet: So you don't have to remember all of it

Lecture 16: Questions you might have.

Chapter 3: Linear regression

Lecture 1: Pedagogy

Lecture 2: What is a predictive model?

Lecture 3: Building your first model using R

Lecture 4: Step 2: Use the lm function

Lecture 5: Step 3: Split your data

Lecture 6: Step 4: Model selection

Lecture 7: Step 5: Multicollinearity

Lecture 8: Predictions and quality checks

Lecture 9: FAQ

Chapter 4: Logistic Regression

Lecture 1: How to spot dissatisfied customers

Lecture 2: The math behind it

Lecture 3: Building a logistic regression using R

Lecture 4: Step 1: Import your data

Lecture 5: Step 2: Use the "glm" function to build a model

Lecture 6: Step 3: Split your data

Lecture 7: Step 4: Model selection

Lecture 8: Step 5: Make your predictions

Lecture 9: Step 6: Checking your model performance

Chapter 5: Cluster analysis

Lecture 1: Segmenting data with K-Means algorithm

Lecture 2: Import your data

Lecture 3: Specify number of clusters

Lecture 4: Interpret your cluster output

Lecture 5: FAQ

Lecture 6: Where do we use factor analysis

Lecture 7: FACTANAL READ ME

Lecture 8: Using R for factor analysis

Chapter 6: Factor analysis

Lecture 1: Computing factor loadings

Lecture 2: Scoring survey

Lecture 3: FAQ

Chapter 7: Project

Lecture 1: Elections data

Chapter 8: Advanced reading

Lecture 1: How cluster analysis is at the heart of Amazon's business model

Chapter 9: DATA CHALLENGE – Work That Data

Lecture 1: THE RULES

Lecture 2: Data Set 1: Meteorite Data

Lecture 3: How good are you with choosing the right flower?

Lecture 4: Data Set 2: Groundwater Depletion Rates

Lecture 5: Data Set 3: Exam Data

Lecture 6: Which car would you buy?

Chapter 10: DATA CHALLENGE

Lecture 1: RULES of the GAME

Lecture 2: Titanic Data Set

Chapter 11: Case Study (With Solution)

Lecture 1: This is for the retailers – You can never go wrong with this

Lecture 2: Don't Let Your Customers say Bye to You

Lecture 3: This could be the reason you never gain weight

Lecture 4: Snails- Yes, Those Slimy Little Creatures

Lecture 5: Are you being Targeted?

Lecture 6: How old was the last abalone you had?

Lecture 7: Why would you migrate to another state?

Lecture 8: Do you eat enough?

Lecture 9: Back to school

Lecture 10: Can you differentiate between a real and a fake note?

Lecture 11: How hard is it to keep warm?

Chapter 12: R: Introduction to R

Lecture 1: Lecture 70 : R Getting started

Lecture 2: Know your tool: Installing R and R studio

Lecture 3: Getting to know the Studio

Chapter 13: Under the Hood

Lecture 1: Working Directory – Understanding the concept

Lecture 2: Data Types: Your Raw Ingredients

Lecture 3: Basic Operations in R

Lecture 4: Basic Operations in R (Contd)

Lecture 5: Basic Operations in R (Contd)

Lecture 6: Introduction to Data Structures : Vector

Lecture 7: Lecture 75: Data Structures: Dataframe

Lecture 8: Set Operations with Dataframes