Unit 1 Introduction to Data Science

• Concept of Data Science
• What is data engineering.?
• Analytics vs
• Introduction to analytics process
• Types of Analytical Techniques in BI – Descriptive
• Social Media Analytics
• Iris Datasets
• Traits of Big Data
• Web and Social Media Analytics
• Web Scrapping
• Machine Learning Algorithms Framework for 05 Program Name: Bachelor of Engineering Level: UG Subject Code: BE04000281 Subject Name: Data Science for Humanity Data-Driven Decision Making Analytics Capability Building Roadmap for Analytics Capability Building Challenges in Data-Driven Decision Making and Future

Unit 2 Descriptive Analytics

5 lecture hours

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

Unit 3 Introduction to Probability

7 lecture hours

• Introduction to Probability Theory
• Probability Theory – Terminology
• Fundamental Concepts in Probability – Axioms of Probability
• 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 Distribution

Unit 4 Sampling

8 lecture hours

• Introduction to Sampling
• Population Parameters and Sample Statistic
• Probabilistic Sampling
• Non-Probability Sampling
• Sampling Distribution
• Central Limit Theorem (CLT)
• Sample Size Estimation: Classical Methods of Estimation
• Estimating the Mean
• Standard Error of a Point Estimate
• Prediction Intervals
• Tolerance Limits
• Estimating the Variance
• Estimating a Proportion for single mean
• Difference between Two Means
• between Two Proportions for Two Samples and Maximum Likelihood Estimation

Unit 5 Simple Linear Regression and Correlation

4 lecture hours

• Introduction to Linear Regression
• The Simple Linear Regression Model
• Least Squares and the Fitted Model
• Properties of the Least Squares Estimators
• Inferences Concerning the Regression Coefficients
• Simple Linear Regression Case Study

Unit 6 Logistic Regression

5 lecture hours

• 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

Unit 7 Classification

6 lecture hours

• Overview of a Decision Tree
• Introduction Chi-Square Automatic Interaction Detection (CHAID)
• The General Algorithm
• Decision Cost-Based Splitting Criteria
• Tree Algorithms – ID3, Naïve Bayes
• Bayes‘ Theorem
• Naïve Bayes Classifier
• Evaluating a Decision Tree
• Classification and Regression Tree Ensemble Method Random Forest

Unit 8 Applying Domain Expertise to Solve Real-World Problems Using Data Science

5 lecture hours

• Case Study 1: Data Science in healthcare Case Study 2: Delving into customer personality analysis Case Study 3: Data Science for Driving Growth in E-Commerce Case Study 4: Data Science in sentiment analysis