Data Analytics and Visualisation

“Doing data analysis requires quite a bit of thinking and we believe that when you’ve completed a good data analysis, you’ve spent more time thinking than doing.” — Roger Peng

The ever increasing computational capacity has enabled us to acquire, process and analyse larger data-sets and information. We increasingly want to take a data-driven lens to solve business problems. But business problems are inherently ‘wicked in nature’ - with multiple stakeholder, different problem definition, different solutions, interdependence, constraints, amplifying loops etc. There is no one trick to solve them. What is required is learning a structured approach to problem solving that can be applied to large set of these problems. One possible way is to use a Hypotheses Driven Approach - problems definition, scoping, issue identification and hypothesis generation - as a starting point for this.

In this workshop, we will learn how to apply this hypotheses driven approach through seven pragmatic steps - Frame, Acquire, Refine, Transform, Explore, Model and Insight - to any business problem. The focus will be to learn the principles of data analytics that can help us to understand the patterns, trends, outliers in our data and enable us to test our hypotheses. We will also learn how data visualization can enable us to compress and encode it in ways to aid perceptual, cognitive and emotional capacity required to comprehend the data. And then understand how we can use charts and narrative visualisation to communicate and to make decisions using this data. This workshop is designed to help you learn the art and science of problem solving using data analytics and visualisation.

Workshop Objectives

The aim of the workshop is to provide a thorough introduction to the art and science of Data Analytics and Visualisation. These are the main objectives:

• Understand the value of data analytics and the role it plays in business decision making
• Learn the approach to problem solving using a hypotheses driven approach - Frame, Acquire, Refine, Transform, Explore, Model and Insight
• Understand the principles of visual analytics and how it can be applied for comparisons, trends, patterns and relationship to solve business problems.
• Learn the theory of data visualisation including grammar, types, color, annotation, flow, animation, interaction etc.
• Learn through practice how to tell a compelling data-story to communicate the insights from the analysis and make change happen within the business.

Workshop Design

The workshop would be scheduled over two days and would be delivered with a mix of teaching, discussion and reflections, as well as individual and group exercises. It would aim to cover the following topics.

Session #1: Introduction

“Data is just a clue to the end truth” - Josh Smith

• Types of business analytics used for decision making - descriptive, inquisitive, predictive and prescriptive
• Understand the value of visualisation - Expression (record), Exploratory (analyse) and Explanatory (communicate)
• Learn the grammar of graphics: data, variable mapping, data transformation, geometric shape, position and aesthetics, a coordinate system, and scale transformation
• Exercise: Paper and Pen Visualisation

Session #2: Frame a Problem

“An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.” — John Tukey

• How to frame a data analytics problem?
• Learn the hypothesis-driven approach: problem definition, scoping, issue diagrams and generating hypotheses
• How do you start - question driven, dataset driven or both?
• Understand the iterative nature of data analysis
• Focus will be on descriptive and inquisitive analysis to answer the “so what” questions
• Exercise: Building a Hypotheses set

Session #3: Acquire, Refine & Transform the Data

“Data is messy - 80% perspiration, 10% great output, 10% idea” - Simon Roger

• Acquire the data - downloads, 3rd party, APIs, scraping, extraction, gathering / collection
• Refine the data - remove, derive, parse, missing values, quality checks
• Transform the data - convert, calculate, merge, aggregate, group-by, pivot, filter, sample, summary
• Exercise: Data Preparation Challenge

Session #4: Explore the Data

“Visualisation gives you answers to questions you didn’t know you had.” - Ben Schneiderman

• Find the visual abstraction - the trend, the patterns, the deviation, and the outliers - in the data
• Create single dimension exploration - distributions, part-to-whole
• Create dual dimension exploration - comparisons, time-series
• Conduct multi-dimensional exploration - deductions, correlations, patterns
• Exercise: Exploration on multi-dimensional dataset

Session #5: Visual Analysis Techniques

“Visualisation addresses the second order of ignorance: I don’t know, what I don’t know” - Shailesh Kumar

• Broad visual analytical techniques: comparing & sorting, add variables, optimal scales, reference lines, trellis & facets, concurrent view, brushing, highlighting, focus & context together, over-plotting reduction
• Deep dive into patterns & relationships - exceptions, boundaries, correlation, association, clusters, overlaps
• Deep dive into comparisons: distribution, range, categorical, measurements, context, hierarchical
• Deep dive into trends: direction, optima, rate of change, fluctuation, significance, intersection
• Exercise: Looking for patterns and trends in Exploration

Session #6: Deep-dive in to Data Visualisation

“We are often bad at the why questions in visualisation, which are often the most interesting ones” - Amanda Cox

• Principles of graphical perception and integrity
• Understand how visual perception works and is applied to choose the appropriate visualisations
• Explore a guide to choosing appropriate type of graphical representations
• Learn about graphical representations that are best avoided or minimised
• See the principles of data-ink and chart-junk
• Exercise: Chart Redesign and Visualisation Critique

Session #7: Modeling the Solution

“All models are wrong, But some models are useful” - George Box

• Approach to model building, selection and evaluation
• Simple pragmatic steps for modeling a business problem in a spreadsheet tool
• Parameterised models and scenario generation
• Evaluating, testing and auditing the model
• Exercise: Modeling the Problem

Session #8: Communicating the Insight with Stories

“Numbers have an important story to tell. They rely on you to give them a voice.” – Stephen Few

• Explore the principles of storytelling and narrative
• Lessons on how stories work from other mediums - oral storytelling, journalism, movies, comics and consulting
• Selection of messaging - text annotation and verbal
• Build a data-visual-story - understand framing, transitions and flow
• Exercise: Building a Storyboard

Session #9: Engagement through Narrative

“The audience does not need to tune themselves to you, you need to tune your message to them.” - Nancy Duarte

• Engagement in context - personal, presentation & participation
• Designing an engaging monthly reporting deck
• Crafting an engaging narrative deck for telling a data-story
• Exercise: Creating a three slide narrative deck

Session #10: Presentations and Recap

“The data is just part of the story” - Jonathan Harris

• Presentation and review of the narrative deck Exercise
• Way forward: tools and learning resources
• Recap of the overall course

Workshop Details

Participant Profile — The workshop is ideal for anyone who is interested in learning the art and science of data analytics and visualising data and communicating persuasively through it. You could be working with large and complex data-sets as an analyst or using a small datasets to create simple charts and slides in your presentations as a manager.

Pre-requisite skills - There are no hard pre-requisite skills, knowledge or roles necessary for attending the workshop. Only an open approach to learning through listening, observation, and participation in the workshop activities and discussion is required. However, basic level of comfort with handling tabular datasets in a spreadsheet tool would be beneficial. The participant should be comfortable with doing basic data manipulations like lookups, pivots, calculations, filtering etc. in the spreadsheet tool.

Tools Used — The workshop principles are tool-agnostic and can be applied using any visual analytics platform or graphical programming tool. However, for the ease of doing the exercises, we would be using Excel for visual analytics and PowerPoint for narrative visualisation during the workshop. Please download, install and build basic familiarity with the tool prior to the session.

Number of Participants — The maximum number of participants for the workshop would be capped at 30. The small class size would enable a more participative environment with group interaction and presentations possible as well as opportunities to have one-to-one learning interactions.

Duration — The workshop would be conducted over 2 days from 0900 to 1700. There will be short breaks during the morning and afternoon session and a longer lunch break of around 45 minutes in the middle.

Venue Logistics — A training venue for the workshop, with availability of a projector, sound system and whiteboard would be needed for conducting the session.

Workshop Cost

The workshop would be charged at Rs. 150,000 per day (for Indian locations) or USD 5,000 per day (for International locations). Service tax and other government charges as applicable will be additional. Also, for sessions conducted outside of Bangalore, the facilitator’s travel and accommodation cost would be charged on actuals.

Facilitator’s Profile

Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. He is the founder partner at narrativeVIZ Consulting, where he teaches data-science, data-visualisation and data-stories as tools for improving communication, persuasion, and leadership and conducts workshops on these topics for businesses, nonprofits, and academic institutes. He also teaches visualisation as a guest faculty in design context at NID, Bangalore and in management context at IIM Bangalore & IIM Ahmedabad

His background is in strategy consulting in using data-driven stories to drive change across organizations and businesses. He has more than 15 years of management consulting experience, first with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps.