##### About the course

Focuses on Statistics that constitutes the backbone for Data Science along with Data Science modeling for Classification and Regression. Concepts will be taught in a handson manner with data sets.

**Munira Lokhandwala, CFA, FRM is PGDM, IIM – Calcutta and has cracked 100%ile in CAT multiple times. **

**She has developed educational content that is used by clients such as ISB, Axis Bank, Castrol, etc.**

**And has Twenty plus years of training experience.**

**The curriculum covers the topics of MBA
**

Live sessions over zoom everyday with immediate doubt clearing + Certificate for this course

##### Details:

**Duration:** 5 hours

**Start Date:** 1st June 2020, 7:00pm to 8:00pm

**End Date:** 5th June 2020, 7:00pm to 8:00pm

##### Curriculum:

#### *Lecture 1: Data Handling and Descriptive Statistics*

- Converting a .txt file into .xlsx
- Types of Data
- Central Tendency Measures
- Frequency Distribution
- Dispersion Measures
- Skewness and Mean
- Covariance and Correlation
- Scatterplots
- How to make sense of Data

#### *Lecture 2: Probability and Distributions*

- Importance of Probability for DS
- Revision of Probability Concepts
- Conditional Probability
- Dependent and Independent Events
- Bayes' Formula
- Uniform and Binomial Distribution
- Normal and Lognormal Distribution
- p values
- False Positive and False Negative

#### *Lecture 3: Sampling, Estimation and Hypothesis Testing*

- Sampling Distribution
- Central Limit Theorem
- Standard Error
- Confidence Intervals
- Sample Size Determination
- Hypothesis Testing Steps
- Type I and Type II Errors
- P-value Revisit
- Student's t- Distribution
- F Distribution
- Chi-Square Distribution
- All the above wrt Applicability and with Data Sets
- Sampling Biases

#### *Lecture 4: Linear Regression Fundamentals*

- Scatter Plot and Correlation
- Applicability of Linear Regression
- Dependent and Independent Variable
- Assumptions behind Linear Regression
- Linear Regression on Excel
- Interpret the slope and the intercept
- Calculations of predicted value
- Understand SEE, Coefficient of Determination, Confidence Intervals
- Significance of the Regression Model
- Anova Table Analysis
- Limitations of Regression Analysis

#### *Lecture 5: Linear Regression Advanced*

- Multiple Regression: Step-wise and Simultaneous Regression
- Adjusted Rsquared
- Anova Table Analysis
- Dummy Variables
- Heteroskedasticity
- Serial correlation
- Multi-collinearity
- Model Mis-specifications

**From **: 1st June 2020, 7:00pm to 8:00pm **to** 5th June 2020, 7:00pm to 8:00pm