IDC estimates that the market for big data and business analytics will grow from $150Bn in 2017 to more than 203Bn in 2020. Despite significant market potential, there is a dearth of analytical talent – data scientists (akin to the Wall Street quants of the 1990s), analysts and managers – to leverage the economic value of big data.
Big data analysis is likely to fuel the next wave of growth in productivity, innovation, and competition in the market place. The organisations ability to unlock the potential of big data and lead in the market place will be largely determined by its ability to tackle major hurdles in effectively managing big data – identifying the business use case, hiring, nurturing and retaining the right analytical talent for conducting big data analytics, and embracing data driven culture for making business decisions.
This program aims to help the participants to build a solid foundation on big data. It will enable the participants to learn, design and build big data analytic solutions to solve business problems and improve decision making. The program will also help participants to understand various issues, challenges and best practices in implementing big data analytic solutions in organizations.
The program aims to cover three key modules:
Data management infrastructure
Fundamentals of data management
Data modeling and management for big data
Technology infrastructure for big data: DFS, Map reduce & Hadoop
Fundamentals of analytics: Classification, clustering and rule mining
Personalization in e-commerce (recommender systems)
Mining unstructured data (text, image and social network)
Visual analytics: Basic and advanced constructs
Exploratory visual analytics for understanding large data sets
Design and development of dashboards
Strategies for analytics
Building and managing analytic organization
Communicating the analytic results and storytelling with data
Analytic professionals / Data scientists / Functional managers with 3-10 years of industry experience
Participants with some prior exposure to analytics are preferred
Mix of pedagogies including case discussions, lectures, exercises, presentations, and lab sessions.