INDIAN INSTITUTE OF MANAGEMENT

AMRITSAR

Visual Analytics
TERM – IV
MBA08 Batch

Faculty:
Prof. Siddharth Gaurav Majhi
 INDIAN INSTITUTE OF MANAGEMENT AMRITSAR
Master of Business Administration
Course Outline

Course Code and Course Title Visual Analytics

Course type Elective
Pre-requisites (if any) -
Course Units 0.75 unit
Total no. of sessions 15
Session Duration 75 minutes
Term 4
Year and Batch 2023-24 / MBA08
Sections (if any)

Instructor(s) Siddharth Gaurav Majhi

Contact Details siddharthm@iimamritsar.ac.in ; +91-
8130718988
Office #20, 3rd Floor, Administrative Block, Transit
Building
Consultation Hours By appointment

Introduction
In the present data-driven world, data visualization has become an essential skill allowing
managers and professionals to better understand their data, and present the findings in a way
that is most suitable for the intended audience, with the ultimate objective of narrating engaging
data stories that matter. Combining aspects of varied areas such as statistics, psychology, and
computer science, data visualization essentially allows one to explore and present the data in
an effective manner.

Learning Outcomes/Course Objectives

By the end of this course, the students are expected to be able to:
• Understand the key concepts related to data visualization
• Create reports, dashboards, and data visualizations using Tableau
• Create reports, dashboards, and data visualizations using PowerBI
• Understand how interactive dashboards help in managerial decision-making

Textbooks and Learning Materials

Text Book(s)
1. “Fundamentals of Data Visualization” – Claus Wilke (https://clauswilke.com/dataviz/)
2. “Visual Data Storytelling with Tableau” – Lindy Ryan

Reference Book(s)
1. “Data Visualization: Exploring and Explaining with Data (1st Ed)” – Camm, Cochran,
Fry, and Ohlmann
2. “Tableau for Dummies” – Molly Monsey and Paul Sochan
3. “Microsoft Power BI for Dummies” – Jack A. Hyman
4. “Tableau Your Data! Fast and Easy Visual Analysis with Tableau Software”, 2nd
Edition – Dan Murray
5. “Now You See It: Simple Visualization Techniques for Quantitative Analysis” –
Stephen Few
 6. “Accidental Analyst: Show Your Data Who's Boss” - Stephen McDaniel
7. “Storytelling with Data: A Data Visualization Guide for Business Professionals” – Cole
Nussbaumer Knaflic
8. “Information Dashboard Design: Displaying Data for At-a-glance Monitoring” –
Stephen Few
9. “Beautiful Visualization, Looking at Data Through the eyes of Experts” – Julie Steele,
Noah Iliinsky
10. “Visual Thinking for Design” – Colin Ware
11. “The Visual Display of Quantitative Information” – Edward Tufte

Additional Reading(s)
1. “Visualizing data and effective communication” (HBS, Datar & Bowler) [9-118-114]
2. “Visualizations that really work” (HBR)
3. “Is that chart saying what you think it’s saying” (HBR)
4. “The Science of Visual Data Communication: What Works” – Steven L. Franconeri
and others (Psychological Science in the Public Interest, Vol 22(3), pp. 110-161,
2021)

Technology and Software (if any)

Tableau, Microsoft PowerBI

Other Resources (Journals, Internet Websites) (if any)

1. Tableau and PowerBI online resources and training

a. http://onlinehelp.tableau.com/current/pro/online/windows/en-us/help.htm
b. http://www.tableau.com/support/manuals/quickstart
c. http://www.tableau.com/learn/training
d. https://docs.microsoft.com/en-au/power-bi/
e. https://docs.microsoft.com/en-us/learn/powerplatform/power-
bi?WT.mc_id=powerbi_landingpage-marketing-page
f. https://powerbi.microsoft.com/en-au/learning/

2. Other resources will be provided in the class

Pedagogy Used/Learning Process

This course will utilize various pedagogical tools such as lectures, case discussions, in-class
exercises, take-home assignments, project work, etc. The students are expected to come
prepared with the assigned pre-class readings and participate actively in the class discussions
and hands-on exercises. To gain hands-on experience with the Tableau and Power BI tools, the
students must work on both the in-class exercises and the take-home assignments.

Evaluation Components/Assessment of Student Learning

Assessment Tool Percentage Description

End term Exam (Individual) 35 % This component will evaluate the
students’ understanding of the key
concepts covered in the course, and
the ability to apply those concepts
 In-class exercises and Take-home 20 % Students are expected to participate
assignments (Individual) actively in classroom discussions
and in-class exercises

Regular take-home assignments will

be given so that students get hands-
on experience with the Tableau and
PowerBI tools
Project (Group) 20 % The students will have to work in
groups to analyze data and visualize
the same to glean insights
Quizzes (Individual) 25 % Intended to test the basic
understanding of the students in data
visualization, PowerBI and Tableau

Session Plan

Session Topic Chapter No.

/ Reading
material /
Cases
1 Introduction
• What is data visualization?
• Need for data visualization
• Introduction to visual analytics
• Visualizing basic charts using Microsoft Excel
• Introduction to the tools (Tableau and Power BI)
Installation of Tableau Desktop and Power BI Desktop
Creation of Tableau Public account
Creation of a Microsoft account to access Power BI Service
2 Basics of visualization using Tableau
• The Tableau user interface
• Welcome screen
• Importing data into Tableau
• Live versus Extract
• Worksheet and dashboard interface
• Dimensions and measures
• Marks card
• Basic sorting and filtering
• Basic charts – bar, line, table
• Creating a basic dashboard
• Adding interactions to the dashboard
• Publishing workbook to Tableau Public
3 Choosing the right visual
• Importance of context
• Good Charts matrix
• Abela’s chart type hierarchy
 Creating various charts in Tableau
• Bar and line charts
• Dual line charts
• Pie and donut charts
• Scatter plots
• Bubble and area charts
• Tree map
• Heat map
4 Working with dates in Tableau

Maps and geo-visualizations

Visual perception
• Cognitive load and clutter
• Gestalt principles of visual perception
• Pre-attentive attributes
5 Visual design principles
• Human brain and visualization
• Cognitive and perceptual designs
Formatting
• Colors, shapes, and sizes
• Color effects
• Formatting, shading, and banding
• Trend lines and forecasting
• Worksheet and dashboard formatting
6-7 Some useful features in Tableau
• Aliases
• Grouping
• Sorting
• Filters
• Parameters
• Calculated fields
Table calculations
• Ad-hoc calculations
• Quick table calculations
• Customizing table calculations
• Logical calculations
8-9 Advanced charts in Tableau
• Gantt chart
• Timeline
• Bar-in-bar chart
• Control charts
• Market basket analysis
• Pareto chart
• Waterfall chart
 • Social media analytics
10 Dashboards and stories in Tableau
• Assembling a dashboard
• Best practices of dashboard design
• Hierarchies and actions
• Creating data stories in Tableau
11 Introduction to PowerBI
• Introduction to Power BI
• Data sources
• Power Query
• Data Model
• Report Editor – creating a report
• Measures
• Changing reports
• Filters
• Slicers
12 PowerBI – II
• Sync slicers
• Scatter charts
• Formatting
• Analytics
• Merge queries
• Pivot and Unpivot
• Granularity and relationships
• Data types, split columns and merge columns
13-14 PowerBI-III and IV
• Grouping
• Card visuals
• Conditional formatting
• Measures and visuals from multiple tables
• Third party visuals
• DAX calculations
• Hierarchies and drilling up and down
• Calculated columns
• Measure tables
• Formatting reports
• Chart analytics
• Power BI service
• Dashboards
15 Revision

Identifying and fixing ineffective visuals

Ethics of data visualization

Additional Instructions (if any)

Plagiarism is the practice of using/submitting someone else’s words/ideas/works and
pretending that those are your own. This includes – (i) copying exact words/ideas of another
person without proper citation, (ii) failing to use quotation marks to indicate that the
words/ideas are exactly reproduced, (iii) copying/borrowing from published/unpublished
sources (including the internet) without citing the sources, (iv) taking help from another person
in completing the assignment or taking quizzes/examinations, (v) paraphrasing (i.e., changing
the sequence of words/using synonymous words of a source without giving credit) another
person without mentioning the source, and (vi) copying so many words or ideas from a source
(or multiple sources) so that the copied portion makes up the majority of your work, with
without citation(s).
Plagiarism or academic misconduct in any form would be considered seriously and disciplinary
actions, including an F grade in the course, could be taken against the offender. No request for
leniency would be entertained. It is recommended that the students use the APA style of
referencing for all submissions. Please visit the following link for details –
https://apastyle.apa.org/style-grammar- guidelines/references/examples.