Data Analytics and Data Visualization

Paper Code: 
MCA 421A
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Course Objectives:

This course enables the students to

  1. Define the concepts of data analytics.
  2. Understand the concepts of inferential statistics and descriptive analytics
  3. Demonstrate the concept of predictive analytics and data visualization.
  4. Differentiate between business intelligence and data analytics.
  5. Evaluate using different predictive and prescriptive methods
  6. Construct cases and new ideas where the knowledge of data analytics and visualization can be implemented.

 

 

Course Outcomes(COs):

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

CO195. Define the concepts of data analytics.

 

CO196. Describe the concepts of inferential statistics and descriptive analytics.

 

CO197. Execute different predictive and prescriptive methods used in data analytics

 

CO198. Demonstrate the concept of data visualization.

 

CO199. Evaluate using different directory of visualizations. 

 

CO200. Differentiate between data analytics, data science and business intelligence.

 

CO201. Construct cases and new ideas where the knowledge of data analytics and visualization can be implemented.

 

Approach in teaching:

Interactive Lectures,

Modeling, Discussions, implementing enquiry based learning, Student centered approach, Through audio-visual aids

 

Learning activities for the students:

Experiential Learning, Presentations, Discussions, Quizzes and Assignments

 

Assignments

Written test in classroom

Classroom activity

Continues Assessment

Semester End Examination

 

12.00
Unit I: 

Introduction

Introduction to Big Data Platform – Challenges of conventional systems - Web data , Evolution of Analytic scalability, Analytic approaches, Business Approaches, Analytic Innovation, Traditional approaches- iterative, Analysis vs. reporting, Pattern Recognition, Types of data analytics, Introduction to data visualization.

8.00
Unit II: 

Inferential Statistics And Descriptive Analysis

Statistical concepts, Descriptive Analysis, Sampling distributions, resembling, statistical inference, prediction error. Regression modeling, Multivariate analysis, Bayesian modeling, inference and Bayesian networks, Support vector and kernel methods, Analysis of time series, linear systems analysis, nonlinear dynamics, Rule induction.

14.00
Unit III: 

Predictive & Prescriptive Analytics

Predictive Analytics, Supervised, Unsupervised learning , Neural networks,  Kohonen models, Normal,  Deviations from normal patterns, Normal behaviors,  Expert options,  Variable entry, Mining Frequent item sets,  Market based model,  Apriori Algorithm,  Handling large data sets in Main memory,  Limited Pass algorithm, Clustering Techniques,  Hierarchical , K- Means, Clustering high dimensional data Prescriptive Analytics: Basics of Prescriptive Analytics, Optimization models, Decision Trees

14.00
Unit IV: 

 

Introduction to Data Visualization

Data visualization, Need for Visualization, Introduction to tools need for data visualization, Mapping Data onto Aesthetic, Aesthetics and types of data, Scales maps data values onto Aesthetics, Coordinate Systems and Axes, Cartesian Coordinates, Nonlinear Axes, Coordinate Systems with Curved Axes, Color Scales, Color as a tool to distinguish, Color to represent data values, Color as a tool to Highlight

12.00
Unit V: 

Directory of Visualizations

Directory of Visualizations: Amounts, Distributions, Proportions, x-y relationships, Geospatial Data, Visualizing Amounts: Bar Plots, Grouped and Stacked Bars, Dot Plots and Heat Maps. Visualizing Distributions: Histograms and Density Plots, Visualizing a Single Distribution, Visualizing Multiple Distributions at the same time

ESSENTIAL READINGS: 

 

·   Seema Acharya, Subhashini Chellappan, “Big Data and Analytics (WIND)”, Wiley, ISBN: 8126554789, 2015.

·   Claus O. Wilke, ”Fundamentals of Data Visualization” , O’ Reilly, , 2019

·   Boris Lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 2015.

·   Chris Eaton, Dirk deroos et al., “Understanding Big Data”, McGraw Hill, 2012.

·   Alberto Cordoba, “Understanding the Predictive Analytics Lifecycle”, Wiley, 2014.

REFERENCES: 
  • Tom White, “HADOOP: The definitive Guide”, O Reilly IT 2015 SRM (E&T), 2012.
  • Vignesh Prajapati, “Big Data Analytics with R and Haoop”, Packet Publishing, 2013.
  • Tom Plunkett, Brian Macdonald et al, “Oracle Big Data Handbook”, Oracle Press, 2014.
  • Jay Liebowitz, “Big Data and Business Analytics”, CRC press, 2013.
Academic Year: 
2020-21
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