1. OMIS 694, Big Data Analytics
Dr. Chuck Downing
Spring semester, 2016
Course meets Th 6:30-9:10
Get to the web site and familiarize yourself with the course.
Register , code, group, background.

2. High-level rationale: Why now?
We’re awash in data. Storage is cheap. Data is becoming “Big” whether we like it or not.
Organizations need help with hindsight, insight, and foresight.

3. Data explosion

4. Need for talent Data Scientists
Projected U.S. talent gap: 140,000 to 190,000
Role
Role Description
Deep Analytical Talent
People with advanced training in quantitative disciplines, such as mathematics, statistics, and machine learning.
Data Savvy Professionals
People with a basic knowledge of statistics and/or machine learning, who can define key questions that can be answered using advanced analytics
Technology & Data Enablers
People providing technical expertise to support analytical projects. Skills sets including computer programming and database administration
Analysts & Data Savvy Managers
Projected U.S. talent gap: 1.5 million
The new data ecosystem driven by the arrival of big data will require 3 archetypical roles to provide services.
Here are some professions that represent illustrative examples of each of the 3 main categories.
Deep Analytical Talent
Technically savvy, with strong analytical skills
Combination of skills to handle raw data, unstructured data and complex analytical techniques at massive scales
Needs access to magnetic, analytic sandbox
Examples of professions: Data Scientists, Statisticians, Economists, Mathematicians
Data Savvy Professionals
Examples of professions: Financial Analysts, Market Research Analysts, Life Scientists, Operations Managers, Business and Functional Managers
Technology & Data Enablers
Examples of professions: Computer programmers, database administrators, computer system analysts
Note: Figures above reflect a projected talent gap in US in 2018, as shown in McKinsey May 2011 article
Big Data: The next frontier for innovation, competition, and productivity
Module 1: Introduction to BDA

5. OMIS 694 – Big Data Analytics
This course provides an in-depth study of the concepts, methods, and tools for Data Science and Big Data Analytics.
Topics include…
The Data Analytics Lifecycle
Basic Data Analytics Methods using the open-source RStudio
Advanced Analytics Theories and Methods including Clustering, Association Rules, Linear and Logistic Regression, Classification, Text Analysis and Time Series Analysis.
Advanced Analytics Technology and Tools including the open-source software MapReduce and Hadoop.

6. OMIS 694 objectives
Deploy a structured lifecycle approach to data science and big data analytics projects
Reframe a business challenge as an analytics challenge
Apply analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
Select optimal visualization techniques to clearly communicate analytic insights to business sponsors and others
Use tools such as R and RStudio, MapReduce/Hadoop, and in-database analytics
Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst

7. Goal of OMIS and this class:
Senior
Management
Technical
Specialists
YOU

8. The Masters Student IT Knowledge Line
Less sophisticated
from an IT standpoint.
Good problem solver with strong logic,
but needs a solid IT understanding
to function as a high-level
manager.
Loves technology, and
has great experience
using it.

9. Other Administrative Stuff
Who am I?
Who are you?
Groups
Logins

11. Questions??