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Data Visualization for CSE Students

The document outlines a course on data visualization. The course aims to help students understand different data types and visualization techniques, and apply visualization to analyze large datasets and support decision making. It consists of 8 modules covering topics like visualization techniques, visual analytics, visualization tools, and integrating visualization with Hadoop. Students will learn outcomes like identifying appropriate visualizations, relating visualizations to problems and datasets, creating dashboards for large datasets, and analyzing trends. The course includes both lectures and hands-on experiments in laboratories.

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Dada Analtix
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156 views2 pages

Data Visualization for CSE Students

The document outlines a course on data visualization. The course aims to help students understand different data types and visualization techniques, and apply visualization to analyze large datasets and support decision making. It consists of 8 modules covering topics like visualization techniques, visual analytics, visualization tools, and integrating visualization with Hadoop. Students will learn outcomes like identifying appropriate visualizations, relating visualizations to problems and datasets, creating dashboards for large datasets, and analyzing trends. The course includes both lectures and hands-on experiments in laboratories.

Uploaded by

Dada Analtix
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Course code DATA VISUALIZATION L T P J C

CSE3020 2 0 2 4 4
Pre-requisite Data Mining Syllabus version
v. 1.1
Course Objectives:
1.To Understand various type of data, apply and evaluate the principles of data visualization
2.Accquire skills to apply visualization techniques to a problem and its associated dataset
3.To learn how to bring valuable insight from a massive dataset using visualization

Expected Course Outcome:


1.Identtify different data types, visualization types and bring out the insight
2.Relate the visualization towards the problem based on the dataset to analyze
3.Data Visualization dashboard to support decision making on largescale dataset
4. Demonstrate and analyze large scale dataset using visualization tools and techniques.

Student Learning Outcomes (SLO): 2,3,4,5,6,7,

Module:1 Introduction to Data Visualization 4 hours CO: 2


Overview of Data Visualization – Data Abstraction – Task Abstraction – Analysis – four Level of
Validation

Module:2 Visualization Techniques 4 hours CO: 3,4


Scalar and Point Techniques – Vector visualization Techniques – Multidimensional Techniques –
Visualizing Cluster Analysis- Matrix Visualization

Module:3 Visual Analytics 5 hours CO: 5


Network and Trees –Heat Map –Map color and other channels – Manipulate view –Visual Attributes

Module:4 Visualization Tool and Techniques 4 hours CO: 6


Various Visualization Tools – Case Study

Module:5 Diverse types of Visual Analytics 5 hours CO: 7


Time series Data Visualization- Text Data Visualization –Multivariate Data Visualization-Case
Study

Module:6 Integration of Data Visualization with 6 hours CO: 7


Hadoop
Visualization tools with Hadoop –Dashboard Creation- Finance, Marketing, Healthcare,
Insurance etc..

Module:7 Recent Trends 2 hours CO - 8

Module:8 Contemporary issues: 5 hours CO: 8


Total Lecture hours: 35 hours

Text Book(s)
1. Tamara Munzer, Visualization Analysis and Design – CRC press 2014
2. Stephen Few, Now You see it –Analytics press 2009
Reference Books
1. Dr.Chun-haun chen, W.K.Hardley, A.Unwin, Handbook of Data Visualization, Springer
Publications-2008
2. Ben Fry Data Visualization O’Reilly Media,2008

Mode of Evaluation: CAT / Assignment / Quiz / FAT / Project / Seminar


List of Challenging Experiments (Indicative) CO: 9
1. Analysis of Social media data using visualization 4 hours
2. Market Basket Analysis 4 hours
3. Text Visualization 4 hours
4. Dashboard Creation 4 hours
5. Visualization on Streaming Dataset 4 hours
Total Laboratory Hours 24 hours
Mode of evaluation:
Recommended by Board of Studies DD-MM-YYYY
Approved by Academic Council No. xx Date DD-MM-YYYY

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