# Statistical Methods for Data Science

## Time & Location

## About the Event

**Instructors:** Dr. Tirthajyoti Sarkar

*What you will learn / Topics that will be covered:*

**1. Descriptive statistics and probability for data analysis**

1. Why statistics is foundation of data science

2. Central tendency and dispersion measures

3. Bivariate statistics, scatterplot, and correlation coefficient

4. The concept of probability

5. Discrete and continuous probability distributions

6. Bayes’ rule and how it is used in data science

7. Exploratory data analysis (EDA) and how it powers data science

**2. Inferential and Bayesian statistics for data science**

1. What is estimation in statistics

2. Concept of p-values

3. t-test, ANOVA, Chi-square test

4. Bayes’ rule and how to use it for probability computation

5. Application example of Bayesian inference using Python

**3. Statistical methods as used in practical data science and ML**

1. Linear regression with practical example

2. Linear regression as a statistical inference problem, advanced linear regression topics

3. Logistic regression as a classification algorithm, case study with the US income data

4. Naïve Bayes concept and practical application – spam filtering

5. MLE and k-means clustering using market segmentation example

**Target Audiences: **

Engineers, researchers, practitioners and students who are interested in statistics, data science, machine learning, and artificial intelligence. This workshop will particularly benefit people who intend to develop statistical techniques for data science and/or want to pursue a data scientist career.

**Prerequisites: **

Basic knowledge of probability, and familiarity with basic programming fundamentals.

**Upon completion of this course, you’ll be able to start analyzing data using Statistical Methods.**