Engage live with a leading expert and cohort of experienced professionals
Format: 5 x 2 hr live workshops (+ recordings of each)
Dates: 1pm PT; Oct 24, 26, 31, Nov 2 and 7
Price: $800 per seat (expense through L&D)
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
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.
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
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
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
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
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
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
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.
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.!
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!
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.
Please reach out to us via our Contact Form with any questions. We’re here to help!