Data Storytelling for Business Course

Data storytelling is predicted to be the top business skill of the next 5 years.

Well told data stories are change drivers within the modern organization. But how do we find the most important insights in our business data and communicate them in a compelling way? How do we connect the data that we have to the key underlying business issue?

This course takes students from the fundamentals (what should we be measuring and why?) through to the elements of good visualization design (what does a good chart look like?) through to proficiency in data story telling. By the end of the course, students will know how to produce engaging, cohesive and memorable data stories using Excel and PowerPoint. The course also teaches attendees the importance of producing statistically robust visualizations and insights.

Who Should Attend?

  • Graduate hires who are on track toward a career in analytics or data science
  • Executives and managers who want to create more engaging and impressive presentations with Excel and PowerPoint
  • Candidates already working in analyst positions, including (but not limited to) positions involved with data preparation, data analytics, digital and marketing analytics, customer and market analysis
  • Any other business professional who would like to tell better stories with data


None, although it is recommended that students have completed at least an introductory undergraduate statistics unit as part of a degree program.

Laptop Required Specs

Intel i3 processor, 2GB RAM. Either Mac or Windows operating system

Software Requirements

PowerPoint and a data vizualization package of your choice

Course Objectives

  • Provide participants with visualization best practices for the most common charts used in business
  • Provide participants with industry best practices for the production of insights
  • Provide participants with guidelines on best practices for linking charts together into a cohesive story that concludes with actionable recommendations
  • Provide participants with applied examples regarding trend analysis
  • Provide participants with a grounding in sampling, confounding variables and the  statistical aspects behind data story telling

Upon successful completion of this course, participants will be able to:

  • Create complete data stories and know how to present these stories in an engaging way to upper management
  • Generate statistically robust data driven insights that inform decision making in the business setting
  • Detect, illustrate and highlight trends, outliers and patterns in business data
  • Build stakeholder support for project initiatives using data
  • Know how to select appropriate chart types for a given dataset (e.g. When to use a bar chart versus a pie chart, a line graph versus a bubble chart)
  • Know how to design aesthetically pleasing data visualizations that adhere to the principals of good design
  • Understand how to use a ‘data dictionary’ and metadata to facilitate data analysis

Day 1


I. Introductions, Ice Breaker (9:00am – 9:15am)

II. Overview of the Four Keys to Data Storytelling (9:15am – 9:30am)

  • Knowing your audience
  • Preparing your data
  • Choosing the right visual and designing it well
  • Telling the story

III. Preparing your data: Exploratory Data Analysis in the Business Setting (9:30am – 10:15am)

  • Step 1 – Know the story behind your data
  • Step 2 – Variable classifcation
  • Step 3 – Handle missingness
  • Step 4 – Sanity check
  • Step 5 – Univariate EDA
  • Step 6 – Bivariate EDA

IV. Q&A / Break (10:15am – 10:30am)

V. Tables Versus Charts Versus Single Metrics – What to Use and When? (10:30am – 11:15am)

  • Choosing between tables, charts and single headline metrics – guidelines
  • Visualisation is the fastest bandwidth channel for transferring high dimensional information into
  • the human brain
  • Visualisation separates data structure from data noise
  • Visualisation uncovers hidden patterns
  • Visualisation grabs attention
  • Visualisation uncovers cause and effect relationships
  • When to not use graphs – Recognizing situations where a table is most appropriate
  • When to not use graphs – Recognizing situations where a single headline metric is appropriate

VI. Q&A / Break (11:15am – 11:30am)

VII. The Visualisation Arsenal (11:15am – 12:00pm)

  • The Histogram – The most underutilized visualization in business
  • The Bar Chart – The king of flexibility, guidelines on vertical and horizontal variations
  • The Case for and Against Stacked Bar Charts
  • The Pie Chart – Theory and controversy, smack down with bar charts
  • The Scatter Plot – Theory and guidelines for large datasets
  • The Line Chart – Theory, comparison with clustered bar charts, discussion on dual axis line charts
  • Bubble, Waterfall and Area Charts – Quick opinions

VIII. LUNCH (12:00pm – 1:00pm)

IX. Recent Developments in Data Visualization Media (1:15pm – 1:45pm)

  • Virtual Reality Data Visualization Demo
  • Interactivity and animation, d3.js
  • Macros for more efficient and consistent designs
  • Histograms in Excel 2016 – An Applied Walkthrough

IX. Workshop: Team Activity (1:45pm – 4:15pm)

X. Group Work Submission Deadline (4:15pm)

XI. Group Presentations, Feedback and Day 1 Wrap Up (4:15pm – 5:00pm)

Day 2


I. Ice Breaker Exercise – Let’s Tell Stories as a Group (9:00am – 9:15am)

II. The Elements of Data Visualisation Design (9:15am – 10:00am)

  • Above all else, show the data
  • Tufte’s war on chart-junk
  • Tufte’s data-ink ratio
  • Using color to focus attention
  • Dimension, perspective and 3D
  • The Gestalt principles of visual perception
  • Proximity
  • Similarity
  • Closure
  • Continuity
  • Connectedness
  • Enclosure

III. Q&A / Break (10:00am – 10:15am)

IV. The Elements of Data Storytelling (10:15am – 11:00am)

  • Knowing your audience
  • Designing your visuals and narrative around ‘The Big Takeaway’
  • Delivering insights
  • Creating memorable soundbites
  • Structuring your data story – What is an appropriate story flow?
  • From reporting to strategy – Is your data story actionable?

V. Q&A / Break (11:00am – 11:15am)

VI. Examples of good data stories (11:15am – 12:00pm)

  • ‘The Apathy Gap’ – Real life replay of Isaac’s TEDx talk
  • ‘200 Countries, 200 Years, 4 Minutes’ – Hans Rosling’s Animated Take on Global Health
  • Examples of data stories from the top management consulting firms

VII. LUNCH (12:00pm – 1:00pm)

VIII. The Statistics Behind Good Data Storytelling (1:00pm – 1:30pm)

  • Sample size and inference – Why it’s important
  • Correlation and causation – Applied examples

X. Workshop: Team Activity and Presentation (1:30pm – 4:15pm)

XI. Group Work Submission Deadline (4:15pm)

XII. Group Feedback, Course Wrap Up (4:15pm – 5:00pm)