Data Science 3151608 Syllabus is a term that refers to Information Technology Department covers this subject This year, this Subject is covered in the 5th Semester.

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Why Analytics

Business Analytics: The Science of Data-Driven Decision Making
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Descriptive, Predictive and Prescriptive Analytics Techniques
Big Data Analytics
Web and Social Media Analytics
Machine Learning Algorithms
Framework for Data-Driven Decision Making
Analytics Capability Building
Challenges in Data-Driven Decision Making and Future

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## Descriptive Analytics

Introduction to Descriptive Analytics
Data Types and Scales
Types of Data Measurement Scales
Population and Sample
Percentile, Decile and Quartile
Measures of Variation
Measures of Shape − Skewness and Kurtosis

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3

## Introduction to Probability

Introduction to Probability Theory
Probability Theory – Terminology
Fundamental Concepts in Probability – Axioms of Probability
Application of Simple Probability Rules – Association Rule Learning
Bayes’ Theorem
Random Variables
Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of a
Continuous Random Variable
Binomial Distribution
Poisson Distribution
Geometric Distribution
Parameters of Continuous Distributions
Uniform Distribution
Exponential Distribution
Chi-Square Distribution
Student’s t-Distribution
F-Distribution

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4

## Sampling and Estimation

Introduction to Sampling
Population Parameters and Sample Statistic
Sampling
Probabilistic Sampling
Non-Probability Sampling
Sampling Distribution
Central Limit Theorem (CLT)
Sample Size Estimation for Mean of the Population
Estimation of Population Parameters
Method of Moments
Estimation of Parameters Using Method of Moments
Estimation of Parameters Using Maximum Likelihood Estimation

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5

## Simple Linear Regression

Introduction to Simple Linear Regression
History of Regression–Francis Galton’s Regression Model
Simple Linear Regression Model Building
Estimation of Parameters Using Ordinary Least Squares
Interpretation of Simple Linear Regression Coefficients
Validation of the Simple Linear Regression Model
Outlier Analysis
Confidence Interval for Regression Coefficients b0 and b
Confidence Interval for the Expected Value of Y for a Given X
Prediction Interval for the Value of Y for a Given X

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## Logistic Regression

Introduction – Classification Problems

Introduction to Binary Logistic Regression
Estimation of Parameters in Logistic Regression
Interpretation of Logistic Regression Parameters
Logistic Regression Model Diagnostics
Classification Table, Sensitivity, and Specificity
Optimal Cut-Off Probability
Variable Selection in Logistic Regression
Application of Logistic Regression in Credit Rating
Gain Chart and Lift Chart

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## Decision Trees

Decision Trees: Introduction
Chi-Square Automatic Interaction Detection (CHAID)
Classification and Regression Tree
Cost-Based Splitting Criteria
Ensemble Method
Random Forest

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