Visualization is an integral part of the data science process and includes exploratory data analysis to understand the shape of the data, model visualization to unbox the model algorithm, and dashboard visualization to communicate the insight. This task of visualization is increasingly shifting from a static and narrative setup to an interactive and reactive setup, which presents a new set of challenges for those designing interactive visualization applications.

Creating visualizations for data science requires an interactive setup that works at scale. Bargava Subramanian and Amit Kapoor explore the key architectural design considerations for such a system and discuss the four key trade-offs in this design space: rendering for data scale, computation for interaction speed, adapting to data complexity, and being responsive to data velocity.

• Rendering for data scale: Envisioning how the visualization can be displayed when data size is small is not hard. But how do you render interactive visualization when you have millions or billions of data points? Technologies and techniques include bin-summarise-smooth (e.g., Datashader and bigvis) and WebGL-based rendering (e.g., deck.gl).
• Computation for interaction speed: Making the visualization reactive requires the user to have the ability to interact, drill down, brush, and link multiple visual views to gain insight. But how do you reduce the latency of the query at the interaction layer so that the user can interact with the visualization? Technologies and techniques include aggregation and in-memory cubes (e.g., hashcubes, InMEMS, and nanocubes), approximate query processing and sampling (e.g., VerdictDB), and GPU-based databases (e.g., MapD).
• Adapting to data complexity: Choosing a good visualization design for a singular dataset is possible after a few experiments and iterations, but how do you ensure that the visualization will adapt to the variety, volume, and edge cases in the real data? Technologies and techniques include responsive visualization to space and data, handling high cardinality (e.g., Facet Dive), and multidimensional reduction (e.g., Embedding Projector).
• Being responsive to data velocity: Designing for periodic query-based visualization refreshes is one thing, but streaming data adds a whole new level of challenge to interactive visualization. So how do you work decide between the trade-offs of real-time and near real-time data and their impact on refreshing visualization? Technologies and techniques include optimizing for near real-time visual refreshes and handling event- and time-based streams.

Building and scaling data science capability is an imperative for enterprises and startups aiming to adopt a data-driven lens for their business. However, crafting a successful data-science strategy is not straightforward and requires answering the following questions:

• Strategy & Tactics: What part of the business should I target first for adoption? Should I take a jump-start approach or a bootstrap approach?
• Process & Systems: How should I set up an initial process for data science? How to integrate data-driven processes with existing business systems?
• Structure & Roles: Should I adopt a functional or a business-focused data science structure? What specialized roles should I be hiring for Data engineering, ML expert, Visualisation expert, and /or Data Analyst?
• Tools & Stack: Should I build a vertical or horizontal data science stack? How do I integrate data science models with existing applications?
• Engineering & Technical: What are the pitfalls to watch out for? How to avoid pre-mature over-engineering of data science? How to manage the ongoing technical debt for data science?
• Skills & Competencies: How do I up-skill and build differentiated data-science competency across the organization?

The speakers draw upon their experiences in setting up and advising data science teams at enterprises and startups to share best practices on how to craft a successful data strategy and then go on to execute it. The will use case-studies to discuss what worked and failure points to watch out for.