CD3301 Data Analytics and Visualization Syllabus:

CD3301 Data Analytics and Visualization Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 To understand the need of data analytics
 To understand the different methods of analytics
 To learn the applications of predictive analytics
 To understand the impact of data visualization in data analytics
 To provide hands on experience in Data Analytics

UNIT I INTRODUCTION TO DATA ANALYTICS

Data Analytics – Steps in Data Analytics – Data Gathering – Data Scrubbing – Data Analysis – Descriptive Analytics – What – Use – Measures – Inferential Statistics

UNIT II PREDICTIVE ANALYTICS

Definition – Different Kinds – Predictive Models – Descriptive Modeling – Decision Modeling – Machine Learning Techniques – Regression – Linear Regression – Logistic Regression – Neural Network – Support Vector Machines – Naïve Bayes – The players – Privacy and disclosure – Terminology – Respondent and Holder privacy – Data driven methods – Computation driven methods – Result driven methods

UNIT III APPLICATION OF PREDICTIVE ANALYTICS

Analytical Customer Relationship Management – Use of Predictive Analytics in Healthcare – Financial Sector – Predictive Analytics & Business – Marketing Strategies – Fraud Detection

UNIT IV DATA VISUALIZATION

Stacked Bar Chart – Histogram – Butterfly Chart – Donut Chart – Scatter Plot – Bubble Chart – Box Plot – Pareto Chart – Bump Chart – Maps – Gantt Chart

UNIT V DASHBOARD

Dashboard – What, Types – Dashboard Design Approach – Healthcare Quality Dashboard – Airline Quality Dashboard – Manufacturing Quality Dashboard – Warehouse Quality Dashboard.

LIST OF EXPERIMENTS

1. Working with Python Pandas Data Science Library
2. Working with Python Numpy and Lambdas Library
3. Data cleaning and manipulation
4. Data Wrangling
5. Plots in Python
6. Creation, manipulation of list, dictionaries, Tuples, Series, DataFrames
7. Linear Regression with Python
8. Logistic Regression with Python
9. Clustering with Python

PRACTICALS 30 PERIODS
THEORY 45 PERIODS
TOTAL : 75 PERIODS

COURSE OUTCOMES:

CO1 : Able to develop data analytic programs for different use cases
CO2 : Able to perform data visualization for data exploration
CO3 : Develop data analytic applications
CO4 : Get practice to analyze and interpret data for business solutions
CO5 : To predict usefulness of data to the respective domain

TEXT BOOKS

1. Arshdeep Bahga, Vijay Madisetti , “Big Data Science and Analytics A Hands-On Approach”, Arshdeep Bahga, Vijay Madisetti, 2016
2. Jaejin Hwang Youngjin Yoon, “Data Analytics and Visualization in Quality Analysis using Tableau”, CRC, 2022

REFERENCES

1. Bart Baesens,”Analytics in a Big Data World, The essential guide to data science and its applications”, Wiley, 2014.
2. S Christian Albright, Wayne L Winston, “Business Analytics, Data analysis and Decision Making”, Cengage Learning, 2014 ,Sixth edition .
3. Phuong Vo.T.H, Martin Czygan, Ashish Kumar, “Python: Data Analytics and Visualization”, Packt Publishing Ltd. 2017.
4. Purna Chander Rao. Kathula”, “Hands-on Data Analysis and Visualization with Pandas”, Published by BPB Publications, 2020.
5. Christian Tominski, Heidrun Schumann,” Interactive Visual Data Analysis”, CRC Press. 2020.