Unit 1 Introduction to Data Science & Engineering

• Concept of Data Science
• Data engineering vs
• Reporting vs
• Introduction to analytics process
• Types of Analytical Techniques (Descriptive
• Traits of Big Data
• Web Scrapping and Social Media Analytics 04 Program Name: Bachelor of Engineering Level: UG Subject Code: BE05016021 Subject Name: Data Science

Unit 2 Python Essentials for Data Science

7 lecture hours

• Introduction to Python programming
• Data structures (Lists
• Vectorized operations with NumPy
• Data manipulation and cleaning with Pandas (DataFrames
• Overview of the Matplotlib and Seaborn libraries for visualization

Unit 3 Descriptive Analytics & Visualization

4 lecture hours

• Data Types and Scales (Measurement Scales)
• Population vs
• Decile, and Quartile
• Measures of Variation and Shape (Skewness and Kurtosis)
• Visualization techniques for different data relations

Unit 4 Probability Recap

3 lecture hours

• Brief review of Probability Theory Axioms and Random Variables
• Summary of PDF and CDF
• Overview of key distributions (Binomial

Unit 5 Sampling

7 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 6 Simple Linear Regression and Correlation

5 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 7 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 8 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 9 Applying Domain Expertise to Solve Real-World Problems Using Data Science

4 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