Bargava Subramanian

The ever-increasing computational capacity has enabled us to acquire, process and analyze larger data-sets and information. We increasingly want to take a data-driven lens to solve business problems. But business problems are inherently 'wicked in nature' - with multiple stakeholders, different problem definition, different solutions interdependence, constraints, amplifying loops etc. There is no one trick to solve them. What is required is learning a structured approach to problem-solving that can be applied to a large set of these problems. One possible way is to use a Hypotheses Driven Approach - problems definition, scoping, issue identification and hypothesis generation - as a starting point for this. In this workshop, you will learn how to apply a hypothesis-driven approach to any business problem through seven pragmatic steps:

1. Frame * Acquire * Refine * Transform * Explore * Model * InsightThe focus will be to learn the principles through an applied case study and using an iterative and agile methodology.

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.