SYLLABUS – SNS Courseware

21UAI506-Principles of Data Science and Analytics

UNIT 1 Introduction

Introduction to Data Science ᎓ Evolution of Data Science ᎓ Data Science Roles ᎓ Stages in a Data Science Project ᎓ Applications of Data Science in various fields ᎓ Data Security Issues.

UNIT 2 Data Collection and Data Pre-Processing

Data Collection and Data Pre-Processing Data Collection Strategies ᎓ Data Pre-Processing Overview ᎓ Data Cleaning ᎓ Data Integration and Transformation ᎓ Data Reduction ᎓ Data Discretization.

UNIT 3 Exploratory Data Analytics

Exploratory Data Analytics Descriptive Statistics ᎓ Mean, Standard Deviation, Skewness and Kurtosis ᎓ Box Plots ᎓ Pivot Table ᎓ Heat Map ᎓ Correlation Statistics ᎓ ANOVA.

UNIT 4 Model Development Simple and Multiple Regression

Model Development Simple and Multiple Regression ᎓ Model Evaluation using Visualization ᎓ Residual Plot ᎓ Distribution Plot ᎓ Polynomial Regression and Pipelines ᎓ Measures for In-sample Evaluation ᎓ Prediction and Decision Making.

UNIT 5 Model Evaluation Generalization Error

Model Evaluation Generalization Error ᎓ Out-of-Sample Evaluation Metrics ᎓ Cross Validation ᎓ Overfitting ᎓ Under Fitting and Model Selection ᎓ Prediction by using Ridge Regression ᎓ Testing Multiple Parameters by using Grid Search.

Reference Book:

3. David Dietrich, Barry Heller, Beibei Yang, ᎜Data Science and Big data Analytics᎝, EMC 2013 4. Raj, Pethuru, ᎜Handbook of Research on Cloud Infrastructures for Big Data Analytics᎝, IGI Global.

Text Book:

.Jojo Moolayil, ᎜Smarter Decisions : The Intersection of IoT and Data Science᎝, PACKT, 2016. 2. Cathy O᎙Neil and Rachel Schutt , ᎜Doing Data Science᎝, O’Reilly, 2015. 3. David Dietrich, Barry Heller, Beibei Yang, ᎜Data Science and Big data Analytics᎝, EMC 2013 4. Raj, Pethuru, ᎜Handbook of Research on Cloud Infrastructures for Big Data Analytics᎝, IGI Global.