Data Science and Analytics in Business: An Overview

Course Description
The unprecedented volume and variety of available data, the growing capabilities for accessing and
analyzing that data, and a growing network of interconnectedness between data sources and end-users
all point to a sea change in how businesses operate, compete, and grow. It has become imperative for
organizations to adopt and embrace analytical methods in order to remain competitive, much less achieve
competitive advantage.

This seminar will provide an overview of the rapidly evolving data landscape which includes classic
analytical techniques descended from operations research and statistics as well as newer computing
methods and capabilities for processing vast amounts of data – big data – at real-time speeds. In addition
to an explanation of certain core analytical approaches, the seminar will touch on other key components
of successful analytics projects.

Time: 9:00 am – 4:00 pm
Address:
1773 Westborough Drive, Suite 209
Katy, TX 77449

Who Should Attend
This seminar is intended for people who:

• Are interested in gaining an understanding of the analytics landscape, but don’t necessarily have a background or training in the field;
• Have business problems that they feel could be addressed with analytical methods, but aren’t sure where to begin;
• Are stakeholders considering analytics or big data projects for their companies;
• Work directly or indirectly with data scientists and analysts and need to be able to communicate and collaborate effectively with them; and/or
• Are interested in pursuing a career in data science and would like to know what skills and knowledge are needed to enter the field.

Course Outline
Introduction to Data Science and Analytics for Business

• What is data science?
• Types of data scientists
• Types of analysis
• The mind of a (good) data analyst
• Becoming a data analyst or data scientist

Overview of Analytics Landscape

• The four basic pieces: Problem, Data, Analysis, Solution
• Common use cases in business
• Major players (software, components, languages)
• Where are we headed?

Big Data Overview

• Attributes of big data
• Distributed computing
• Hadoop and other components of the Apache ecosystem
• Use case examples

Analytics: Selected Approaches, Methods, Models and Techniques

• Descriptive
• Predictive
• Prescriptive
• Investigative
• Simulation
• Decision Trees

Making Sure Your Data is Useful, Usable, and Used

• Data management, governance, and quality
• Data flows and connections
• Data road mapping
• Creation-consumption distance
• Project and risk management