Foundation of Data Science
offered by IIM Bangalore

5.0 /5

(1 Review)

Public/Government Institute
Estd. 1973

Foundation of Data Science
at
IIM Bangalore
Overview

Gain a comprehensive overview of the data science principles and concepts

Duration	9 weeks
Mode of learning	Online
Schedule type	Self paced
Credential	Certificate

Foundation of Data Science
at
IIM Bangalore
Course details

Skills you will learn

Excel Regression Anova Software Testing Data Science Statistics Hypothesis Testing Data visualization

Who should do this course?

For students/practitioners interested in improving their knowledge in the fundamental concepts of Data Science

What are the course deliverables?

Describe the role of probability theory, optimization and linear algebra in the field of Artificial Intelligence
Define probability distributions such as binomial and normal and its applications in ML model development
Conduct hypothesis tests such as Z test and t-test and how it is used in ML Model development
Explain optimization and linear algebra concepts and their applications in ML and AI
Conduct hypothesis testing, optimization and linear algebra using Excel

More about this course

This course will start with basic concepts in probability such as joint and conditional probabilities
Discuss the implementation of these concepts in ML algorithms for Market Basket Analysis and Recommender Systems
After covering basic probability concepts, we move on to random variables, discrete and continuous probability distributions, sampling, estimation and central limit theorem
ML models such as regression and logistic regression use hypothesis testing to select features
We will discuss various hypothesis tests and how they are used in feature selection.

Foundation of Data Science
at
IIM Bangalore
Curriculum

Descriptive Statistics and Data Visualization

Introduction

Data Types and Scales

Population and Sample

Measures of Central Tendency

Measures of Variation

Measures of Shape

Data Visualization

Demo Using Excel and Tableau

Network mobilization

Introduction

Probability Theory-Terminology

Axioms of Probability

Bayes-Theorem

Random Variables

PDF& CDF of Continuous

Random Variable

Binomial Distribution

Poisson Distribution

Geometric Distribution

Uniform Distribution

Exponential Distribution

Normal Distribution

Chi-Square Distribution

Student's t-Distribution

F-Distribution

Tutorials

Sampling and Estimation

Introduction

Population Parameter & Sample Statistic

Sampling

Probabilistic Sampling

Non-Probability Sampling

Sampling Distribution

Central Limit Theorem

Sample Size Estimation for Mean of the Population

Estimation of Population Parameters

Method of Moments

Estimation of Parameters Using Maximum Likelihood Estimation

Confidence Intervals

Introduction

CI for Population Mean

CI for Population Proportion

CI for Population Mean when Standard Deviation is unknown

CI for Population Variance

Hypothesis Testing

Introduction

Setting up a Hypothesis Test

One-Tailed and Two-Tailed Test

Type I Error, Type II Error, and Power of the Hypothesis Test

Hypothesis testing for Population Mean with Known Variance: Z-Test

Hypothesis testing for Population Proportion: Z-Test

Hypothesis test for Population Mean under Unknown Population Variance: t-test

Paired Sample t-test

Two-Sample Z and t-test

Two-Sample Z-Test for Proportions

Effect Size: Cohen's D

Hypothesis Test for Equality of Population Variances

Non-Parametric Tests: Chi-Square Tests

Tutorials

Analysis of Variance

Introduction

Multiple t-Tests for Comparing Several Means

One-way ANOVA

Two-way ANOVA

Tutorials

Correlation Analysis

Introduction

Pearson Correlation Coefficient

Spearman Rank Correlation

Point Bi-Serial Correlation

The Phi-Coefficient

Applied Linear Algebra

Why do we need Linear Algebra?

Matrix Algebra and Operations

Eigen Values and Eigen Vectors

Linear Algebra in Dimensionality Reduction

Linear Algebra in Natural Language Processing

Linear Algebra in Machine Learning

Foundation of Data Science
at
IIM Bangalore
Faculty details

U Dinesh Kumar

U Dinesh Kumar’s research interest includes Business Analytics and Big Data, Artificial Intelligence, Machine Learning, Deep Learning Algorithms, Stochastic models (Reinforcement Learning Algorithms), Reliability, Optimization, Six Sigma and Performance Based Logistics.