Syllabus Data Science - (BE05016021) Total Credits = TH / 30 Assessment Pattern and Marks Total Marks Theory Tutorial / Practical ESE (E) PA (M) PA (I) PBL (I) ESE (V) 03 70 30 20 30 50 200 Sr. No. Content 1. Introduction to Data Science & Engineering : Concept of Data Science; Data engineering vs. Analytics; Reporting vs. Analytics; Introduction to analytics process; Types of Analytical Techniques (Descriptive, Diagnostic, Predictive, Perspective); Traits of Big Data; Web Scrapping and Social Media Analytics. (Chapter - 1) 2. Python Essentials for Data Science : Introduction to Python programming; Data structures (Lists, Dictionaries, Sets); Vectorized operations with NumPy; Data manipulation and cleaning with Pandas (DataFrames, Series); Overview of the Matplotlib and Seaborn libraries for visualization. (Chapter - 2) 3. Descriptive Analytics & Visualization : Data Types and Scales (Measurement Scales); Population vs. Sample; Percentile, Decile, and Quartile; Measures of Variation and Shape (Skewness and Kurtosis); Visualization techniques for different data relations. (Chapter - 3) 4. Probability Recap : Brief review of Probability Theory Axioms and Random Variables; Summary of PDF and CDF; Overview of key distributions (Binomial, Poisson, Normal, Chi-Square). (Chapter - 4) 5. Sampling : Introduction to Sampling, Population Parameters and Sample Statistic, Sampling, 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. (Chapter - 5) 6. Simple Linear Regression and Correlation : 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, Prediction, Simple Linear Regression Case Study. (Chapter - 6) 7. 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. (Chapter - 7) 8. Classification : 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. (Chapter - 8) 9. Applying Domain Expertise to Solve Real-World Problems Using Data Science : 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. (Chapter - 9)
