Overview

Businesses are increasingly looking to take advantage of Big Data to be competitive. In addition to Data Scientists, organizations need data-savvy business leaders who can identify opportunities to solve business problems using advanced analytics and who have the expertise to lead an analytical team. This course gives business leaders the skills and knowledge to better manage such analytical efforts. It describes how to get started and what is required to effectively run projects which leverage Big Data analytics. Specifically, it addresses: deriving business value from Big Data, leading Data Science projects using a data analytics lifecycle, developing Data Science teams, and driving innovation using analytics.

Duration:  03 days (24 hours).

Training method: Instructor Led.

Course Objective:

-       Understand the overview of Big Data: Definition, Characteristics, Components, Benefits

-       Articulate the business value of Big Data and the opportunities it presents to drive growth and innovation

-       Discuss key Data Science analytic methods and identify opportunities for applying these methods

-       Understand techniques using in Big Data: how to store, manipulate, process and analyze data.

-       Lead analytics projects using a structured lifecycle approach

-       Develop Data Science teams to leverage the required skill sets and appropriate organizational models

Audience:

-       Leaders of functional areas wanting to enhance analytics-driven decision making

-       Business leaders looking to build a new analytics or Data Science capability

-       Leaders of Business Intelligence or Operations teams looking to raise the level of analytics

Course outline

Module 1: Introduction to Data Science

-       History of Big data.

-       Big Data overview

-       How big data is.

-       Differences between Business Intelligence and Big Data.

Module 2: Data as an asset

-       Types of data, source of data

-       5V in Big Data: Volume, Velocity, Variability, Veracity, Value.

-       Strategies using data in business

Module 3: Deriving Business Value from Big Data

-       Business value of Big Data technology.

-       Case study of using Big Data to drive business.

-       Big Data’s case study in banking, retail banking.

Module 4: Techniques using in Big Data technology

-       Big Data storage overview

-       Big Data processing overview

-       Big Data storage technique (tools, how to store data…)

-       Big Data processing (tools, how to process, analyze data…)

Module 5: Leading Big Data project

-       Overview of data analytics lifecycle

-       Frame a business problem as an analytics problem

-       Deliverables in an analytics project

Module 6: Developing Data Science/Big Data Teams

-       Develop an analytic team, roles and skill sets

-       Approaches to develop Data Science capabilities

-       Organizational models for Data Science teams

Module 7: Work in group

-       Discuss and understand the Business Problems, 

-       Define the Business Initiatives (Goals & Objectives)

-       Plan the key tasks

-       Identify the data sources during Brainstorming/Discussion sessions.

Module 8: Data related infrastructure at a Retail Bank

-       In this module you will understand the various types of data needed at a retail bank, Infrastructure required to manage data and learn about challenges and best practices in managing data.

Module 9: Data-Driven Customer Acquisition at Retail Banks.

-       In this module you will understand how to run data-driven acquisition programs, Best practices around analytics in the acquisition space, understand the differences between prospecting and onboarding and also learn about best practices around digital onboarding. Carry out a case study of an Indonesian bank.

Module 10: Data Driven Upselling and Cross Selling at Retail Banks

-       In this module you will understand how to run data driven upsell and cross sell programs. Learn about best practices of analytics in the upsell and cross sell space, tactics to increase customer penetration, approaches to Bancassurance perform a case study of an Indian bank and Chinese bank.