IDVTUndergraduate courses
Interactive Data Visualization
Interactive exploration of quantitative business data — perception, distributions, multivariate views and treemaps — worked hands-on in Tableau.
- Introductory
- 4 weeks
- Approx. 15 hours per week
- 2 semester hours
About this course
This course is about the interactive exploration of data, and how it is achieved with current data visualization software. You work with a range of data types and structures, and with the interactive techniques for manipulating and examining them, so that a display answers a question rather than decorating one. The learning is hands-on: you are guided through an analysis of quantitative business data to pick out the meaningful patterns, trends, relationships and exceptions that reveal business performance, potential problems and opportunities.
Who this course is for
Statistical analysts and data miners who need to explore and graph multivariate data, either to form impressions of the data or as a preliminary step to performing statistical tests or building models.
What you will learn
6 outcomes
By the end of this course, you will be able to:
- Apply principles of perception to data visualization
- Use software tools to interactively visualize relationships among variables
- Analyze distributions of data visually
- Use a range of displays to explore data
- Use parallel coordinate plots, scatterplots and trellising to analyze multivariate data
- Visualize hierarchical data with treemaps
Week by week
4-Week curriculum overview
This curriculum is identical across all start dates. Expand a week to explore the topics covered.
- Weeks
- 4
- Your week
- Approx. 15 hours
- Level
- Introductory
Characterizing information visualization; perception and data preparationWeek 1
- Information visualization — characterization and history
- Elements of visual perception
- Introduction to the software, and preparing data — merging data, getting started, export
Interaction and distributionsWeek 2
- Interaction techniques
- Distribution analysis
- Hands-on visual exploration of business data
Time series, multivariate views and hierarchiesWeek 3
- Time series
- Multivariate views — scatterplots, parallel coordinate plots and trellising
- Treemaps for hierarchical data
Specialized displays and visual analyticsWeek 4
- Specialized visualizations
- Video demonstrations of novel techniques
- From visualization to visual analytics
Instructors
Expert-Led Guidance
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Ms. Madhuri Maddipatla
Madhuri Maddipatla is an analytics specialist and problem solver with 10+ years of experience in analytics consulting across multiple domains, including Retail, Consumer Packaged Goods, Healthcare, Finance, Manufacturing, and E-commerce. Currently a Specialist with McKinsey and Company, she has been an instructor and mentor in the data analytics, data visualization and business consulting space for 6+ years now. She completed her M.S. in Data Science and Business Analytics at the University of North Carolina at Charlotte and worked on several analytics efforts with the industry and in the academic setup. She won several online crowd sourcing analytics contests and is a passionate problem solver and data science mentor.
Before you start
What you need
Everything to have to hand before the first week. Open a row for the detail.
Course Format & Schedule
This is a 4-week, 100% online, asynchronous course.
- No mandatory live sessions: Log in and complete your work at times that fit your schedule.
- Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
- Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework
Guided exercises using the visualization software, alongside assigned readings. There is also an end-of-course data modeling project, and example software files to work from.
Texts
A recommended text for this course is Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few. Note: This text is not available in digital format. For those residing outside the US and not able to purchase this text, you may use The Truthful Art by Albert Cairo instead.
Software
The use of Tableau software is illustrated and access to Tableau Public will be provided in the first lesson. Prior experience with Tableau is not expected or required.
FAQ
Do I need to have used Tableau before?This course
No. Prior experience is not expected. The first week covers getting the software going and preparing data in it, and the exercises build from there.
Is this a course about Tableau, or about visualization?This course
About visualization, taught in Tableau. The principles of perception, the choice of display and the reading of a multivariate view are the subject; Tableau is how you carry them out, and what you learn transfers to other interactive tools.
Is there a money-back or satisfaction guarantee?Every course
We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.
Can I transfer to a later start date or withdraw after the course begins?Every course
Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.
Who teaches the course?Every course
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Something not answered here? Ask us before you book — and the terms set out what a purchase covers.
Credit, and where it counts
This course carries a verified figure of 2 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.
Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.