This course is designed for

Data Analysts & Data Scientists

Data Analysts & Scientists

who want to better prepare and analyze data sets or resulting models

Key Learning Objectives:

Apply visualization methods to effectively make sense of data and models

Avoid common data exploration pitfalls

Gain increased confidence and proficiency in their analytic work

Watch Jeff's recent live chat on

What makes a visualization good?

Recommended by

The Industry’s Best

Mike Bostock

Founder @ Observable,
Creator of D3.js

“Jeff is a fantastic lecturer and mentor, with a true passion and gift for teaching, explaining, and inspiring. He is also an accomplished researcher with an encyclopedic knowledge of (and many contributions to!) research in data visualization and analysis. You couldn’t ask for a better guide to both the theory and the practice of data exploration.”

Joe Hellerstein

Professor @ UC Berkeley,
Co-Founder @ Trifacta and Aqueduct

“Jeff’s open-source software is everywhere, thanks to the success of his work on famous projects like d3.js, Vega, and Vega Lite. He is the highest-impact Data Visualization Professor in the world today, bar none. He also has real-world experience not only from open-source users, but also from his time as co-founder and CEO of Trifacta. There he helped pioneer visual dataprep technologies that power Google Cloud Dataprep and Alteryx Designer Cloud, and which inspired numerous copycats.  In my 30+ years in computing, Jeff Heer is one of the leaders I continue to listen to most closely -- you should too!”

Carlos Guestrin

Professor @ Stanford,
Former Senior Director of AI & Machine
Learning @ Apple

“Jeff is one of the world’s leading experts in developing tools for helping people understand data. His data visualization methods are used everyday across industry and academia, enabling the discovery of patterns in data and supporting data-driven communication of these insights. Jeff is also an engaging and instructive speaker, who conveys complex concepts through intuitive and applicable examples.”

Meet Your

Instructor Jeff Heer

Jeffrey Heer is a Professor of Computer Science at the University of Washington (and previously at Stanford), where he leads the Interactive Data Lab. He is a world-recognized expert with over 20 years of experience in data visualization, data science, and human-computer interaction.

The visualization tools developed by Jeff and his collaborators -- including Vega, Vega-Lite, and D3.js -- are used by companies, researchers, and data enthusiasts around the world. Jeff also co-founded Trifacta, a provider of interactive tools for scalable data exploration and transformation (acquired by Alteryx in 2022), and has worked with companies including Tableau, Microsoft, IBM, Xerox PARC, and a host of start-ups.

Jeff is an invited speaker across industry (Strata, Economist Ideas) and research (NeurIPS, VLDB, KDD) and his work has been honored by multiple best paper awards, MIT Technology Review's TR35 (2009), and the ACM Grace Murray Hopper Award (2016). He holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley.

Jeff Heer

About Jeff's

Live Cohort

The increasing scale and accessibility of digital data provides an unprecedented resource for business strategy, governance, public policy, and more. Yet real-world data and the models derived from them may be messy, biased, and violate our assumptions. Productive analytic work requires integrating analysis algorithms with human judgments of the meaning and significance of observed patterns.

This course will supercharge your ability to visualize data and models to ensure they are properly transformed and fit for purpose. Learn to engage in targeted data exploration at scale, automate parts of the process where you can, employ appropriate techniques for a variety of data types (including tabular, high-dimensional, network, and text), and avoid costly missteps or false discoveries.

How do I apply visualization methods to make sense of data and models?

How do I interact at scale with large volumes of data?

How do I visualize uncertainty and avoid issues of false discovery?

How do I explore complex data types, including high-dimensional, network, and text data?

Session 1 - Open your eyes: An introduction to visual data exploration

Monday, October 24
1-3 PM (PST)

This session will focus on  the purpose of data exploration and visualization composition. Upon completion, learners will be able to:

Formulate the goals and tasks of data exploration

Compose visualizations and reason about their effectiveness using fundamentals of visual encoding design

Use common visualization tools and technologies

Session 2 - Know your data: Assessing shape, structure, and quality

Wednesday, October 26
1-3 PM (PST)

Triage datasets, test  assumptions, identify data quality  issues – and turn these into repeatable processes! When finished, learners will  be able to:

Assess data distributions and associated uncertainty, setup processes for rapid data characterization

Discover data quality issues that may undermine analysis, and monitor that quality going forward

Scrutinize the results of dimensionality reduction methods to examine high-dimensional data

Session 3 - Go big: Exploration at scale

Monday, October 31
1-3 PM (PST)

Grapple with issues of scale to visualize and interact with large volumes of data. Then, the session knowing how to:

Construct plots that scale to arbitrary data volumes

Synthesize indexing and prefetching techniques to perform scalable visual querying

Select and apply sampling and approximation methods

Session 4 - Try not to fool yourself: Avoiding analytic own goals

Wednesday, November 2
1-3 PM (PST)

Identify common inferential failures and learn methods to better assess the reliability of visual patterns. By the end of the session, learners will be able to:

Identify problems of false discovery: how can visual analysis mislead?

Use visualization methods for decision-making under uncertainty

Perform model checks or graphical inference to assess patterns

Session 5 - Think outside the box: Beyond tabular data

Monday, November 7
1-3 PM (PST)

Visualize complex data types, including networks, text, and machine learning models. Leave the session knowing how to:

Analyze network data using appropriate visualization techniques

Transform and visualize unstructured text

Survey visualization techniques for set-typed, sequence, rank-order, genomic, and other data types

Still have questions?

We’re here to help!

Do I have to attend all of the sessions live in real-time?

You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.

Will I receive a certificate upon completion?

Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, Sphere is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.!

Is there homework?

Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!

Can I get the course fee reimbursed by my company?

While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.

I have more questions, how can I get in touch?

Please reach out to us via our Contact Form with any questions. We’re here to help!

Book a time to talk with the Sphere team