MASTER OF SCIENCE IN BUSINESS ANALYTICSCreate Business Value “[…] must start with a valid and high-value business question or idea. Even when the team is making a decision regarding

M A S T E R O F S C I E N C E I N

B U S I N E S S A N A LY T I C S

A A R O N K O E H L , P H D

F A C U L T Y D I R E C T O R , B U S I N E S S A N A LY T I C S

C L I N I C A L A S S O C I A T E P R O F E S S O R

Why are you here?

What are some qualities of a successful

business person?

Create Business Value

“[…] must start with a valid and high-value business

question or idea. Even when the team is making a decision

regarding IT, data, analytics models, and executions, it

should not be made in isolation and apart from business

considerations. Even though the original business premise

might sometimes need to be modified, it should always

focus on business outcomes.”

Create value through your analyses

Learn Analytics Techniques

- Extract useful business insights, patterns, and knowledge

from data using models

- How to communicate model results

- How to approach large, ambiguous (messy) problems

How can you be useful?

Supply Chain /

Operations

Purchasing

Inventory

Production

Sales and Distribution

Maintenance

Financial Accounting

Payables

Treasury

Collections and Receivables

Financial Reporting

Managerial Accounting

Human Resources

Payroll

Talent Management

Retention and Recruiting

Career Development

Customer Service

Contract Management

Repairs and Warranties

Routing and Support

Marketing Automation

Pricing

Product Mix

Promotions

Advertising

Wallet Estimation

Customer Relationship

Sales Pipeline Mgmt

Self-Service Order Mgmt

Marketing Campaign / Comm Flow

Partner Relationships

Product Lifecycle

Product Data Management

Product Development

Product Managment

Overview

Analysts Create Insights

The Curriculum

Your Career

Words of Wisdom

Be at the Forefront

• Global marketplace built on an advancing landscape of technology

• At the center is a nearly bottomless repository of data

• Industry lacks the ability and talent to analyze it

• This has spawned a revolution – business analytics

New breed of business expert that can leverage data analysis,

statistics, and computation to create business value through insight.

What has changed?

What has changed?

Business analytics, in some form*, has been around for 50 years, so what is new?

• Computation availability

• Advances in algorithms (ML + AI)

• Proprietary data availability—and enormous amounts of it

• Data is now a strategy

• (critical mass reached for desire to compete with data)

• Finance, Operations, Marketing and Sales

4 Analytics Enablers

Business Analysts are successful in 4 dimensions

Prob & Stats, Machine Learning, Optimization, AI, Neural Networks, Algorithms

Visualization (Tableau, Excel, Python, R), Presentation Practice, Capstone Project

SQL, ETL, Dashboards, Big Data, Programming Solutions (R, Python), Web Scraping

Metrics, How Managers Think, What Decisions are Made, Industry Familiarity, Organizational Dynamics, Implementation, Terminology

The Faculty

11

James Bradley, ProfessorPhD, Industrial Engineering,

Stanford UniversityMBA, Tuck School, Dartmouth College

David Murray, ProfessorPhD, Computer Information Systems,

University of Michigan; MBA Concordia University, Montreal

Paul Blossom, ProfessorPhD, Operations Management,

Michigan State University MS, Operations Research,

Michigan State University

Monica Tremblay, ProfessorPhD, Business Administration,

University of South FloridaMS Information Systems,

University of South Florida

Seth Li, ProfessorPhD, Management Information

Systems, University of Georgia

Aaron Koehl, Professor and Faculty Director of the MSBAPhD, Computer Science,

College of William & MaryMEng, Systems Engineering,

University of VirginiaJoe Wilck, ProfessorFaculty Director, OMSBAPhD, Industrial Engineering and Operations

Management, Penn State UniversityMS, Systems Engineering, Virginia

Polytechnic Institute and State University

New Faculty

12

J. Alejandro Gelves, ProfessorPhD, Economics

University of Wisconsin-Milwaukee MS, Business Analytics

College of William & Mary

Rachel Chung, ProfessorPhD, Business Admin/MISPhD, Psychology MS, Information Science

University of Pittsburgh

A bit about the Faculty

• Our Passion

• Our backgrounds are all in fields that spawned the analytics movement..

Information technology, Operations Research, Statistics, Computer Science, Artificial Intelligence, Mathematics, Engineering…

…BUT with practical business experience

• Automotive, defense, transportation, software development, computer infrastructure, cybersecurity, marketing analytics, consumer engagement, telecommunications

We value relevant, practical techniques that deliver business value

Words of WisdomMason School of Business

Faculty

~20 years of Industry Experience

Already the best—a prerequisite for working here.

Give them your respect, and they will provide far more than the curriculum.

Take charge of your learning, give deference to the faculty and

you will soar to new heights.

T h e C u r r i c u l u m

M S B A P R O G R A M D E S I G N

Context

What reasoning abilities or intellectual habits do

analysts need to possess to develop or answer

questions raised in business?

Recognize that conclusions/decisions must be made

in the absence of complete information.

Context

What reasoning abilities or intellectual habits do

analysts need to possess to develop or answer

questions raised in business?

Recognize that conclusions/decisions must be made

in the absence of complete information

..a few words on ambiguity

Theme of business decision making

Problems are never well-defined

Decisions always made under uncertainty

Analysts are at the forefront of this ambiguity.

Ambiguity Clarity

Ambiguity

Comfort and tolerance with NOT knowing the problem

Analysts: Cut through the ambiguity, define the

problem, talk it through, work within the team, refine

the problem definition.

A problem well-defined is a problem well-solved.

Analytics TaxonomyDescriptive

Statistics, Visualization, Databases

Predictive

Statistics, AI, ML, Big Data

Prescriptive

Optimization, Heuristics, AI, ML, Big Data

Always persuasive communication of the results!

Always an understanding of the greater business context!

Mission

“To prepare graduates to excel in the field of

analytics, which requires mathematical, information

technology, and communications skills, as well as

knowledge of industry processes and practices.

Further, the MSBA fosters curiosity and a

commitment to lifelong learning.”

Acu

men

/ C

on

text

Ap

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Your 10 Month Journey

Cutting Edge Curriculum

Pre-requisites Fall (15 credits) Spring (15 credits)

Capstone Orientation

• Probability &

Statistics

• Linear algebra

• R and Python

Programming

• Business

Foundations

Competing

through

Business

Analytics

(3 credits)

2 weeks

Database Management3 credits, 13 weeks

Big Data

3 credits, 12 weeks

Capstone

(3 credits)

3 weeks

Intermediate Probability and Statistics

3 credits, 13 weeks

Heuristic

Algorithms

1.5 credits, 6

weeks

Data

Visualization

1.5 credits, 6

weeks

Machine Learning 13 credits, 13 weeks

Machine Learning 2

3 Credits, 12 weeks

Optimization3 credits, 13 weeks

Artificial Intelligence – Neural

Networks

3 Credits, 12 weeks

MSBA Program Design• Courses develop Analytics, Math, Programming, and Databases

• Business Acumen

Competing Through Business Analytics

Guest Speakers (especially in CTBA)

Real or realistic data sets and problems

“Story time” for context of the analysis

Capstone projects

• Communications

Practice, practice, practice

Many opportunities in the MSBA

Checkpoint

Are we teaching business students how to program,

or are we teaching technical students how to

“business”?

Checkpoint

Are we teaching business students how to program,

or are we teaching technical students how to

“business”?

Neither. Both of those approaches fail.

Analysts must do both well.

We must make a $1M ambiguous decision tomorrow. Our analysts have consumed the data (ambiguity) and made a

data-based recommendation (clarity).

Which do you trust more?

1. The analyst whose model and mathematics are shaky and untrustworthy..? 2. The programmer intern whose model did not consider the business context and

solved the wrong problem..?

If either one of these were acceptable, business analytics would not be a “thing”, and you would not be here.

MSBA Classroom Experience

We will teach you more than you need to know.

Surface learning is a tacit acceptance of the material—a “transactional” understanding. Is that a bad thing?

How far can you get with transactional understanding?

How will YOU respond when something goes WRONG? How will you defend your technical model to other

analysts? How will you defend your business recommendation to management?

Curriculum: SummaryStrong quantitative program achieved through admission and pre-requisites. (Congratulations again!)

Communication and business acumen are reinforced strategically throughout the program through selection of assignments, team work, presentations, applied material

Context Analytic Methodologies Application

Graduates are well-prepared for analytic roles…

Yo u r M S B A C a r e e r

B E G I N S T O D AY

Career Outlook

Data has been called the backbone for 21st century business decisions, which underscores the value of well-trained data practictioners. But why else should you consider this role?

• #1 Best Job in America1

• Top 10 Highest Paying Jobs in the U.S.2

• $110,000 median base salary3

• 28% expected rise in demand4

• 2.7 million openings for U.S. data professionals, including data scientists, developers and engineers expected by 20205

1. Retrieved on February 28, 2018, from glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm2. Retrieved on March 21, 2018, from cnbc.com/2017/09/18/10-highest-paying-jobs-in-the-us-right-now.html3. Retrieved on February 28, 2018, from glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm4. Retrieved on February 28, 2018, from forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/5. Retrieved on March 21, 2018, from https://blog.linkedin.com/2016/10/20/top-skills-2016-week-of-learning-linkedin

Your MSBA Journey

• Right now…

1. Work hard in the MSBA to acquire skills

2. Determine your target positions and industries

3. Become aware of your competencies (and deficiencies)

4. Develop target positions and industries for the future

• Set a career path, trajectory

• (Punch through)

Your careerConsultant or Consulting Analyst

Accenture, IBM, Booz Allen Hamilton, Alix Partners, Marathon

Advisory Services AnalystEY, Deloitte, PwC, KPMG

Company HQ/Division Analytics DepartmentCapitalOne, Disney, Ferguson, Florida Blue, MetLife, Merkle, MITRE, Verizon

Analytics-Based CompaniescomScore, WealthEngine

FinanceStevens Capital Management

Public PolicyIMF, World Bank

32

Your careerAccount Analyst

Analytics and Tech Consultant

Applied Analytics Consultant

Associate Data Analyst

Business Analyst

Consultant

Data Analyst

Data Scientist

Data Scientist Consultant

eBusiness Analyst

Information Technology Consultant

Jr. Data Engineer

Machine Learning Fellow

Sr. Business Analyst

Sr. Consultant

Solutions Analyst

Staff Computer Scientist

Technology Analyst

A Moving TargetBusiness Analytics is rapidly evolving.

Software, methodologies, skills, and positions

R vs SAS, Python vs Java, CPU vs GPU

Limited visibility of future opportunities

You’ll have opportunities that don’t now exist!

Your preferences will evolve with experience.

How do you cope?

Keep learning, create flexibility for yourself

Great news: There’s a huge talent shortage.

:: You have a lot of latitude in shaping your career!

Your MSBA Journey

• Beware, you have signed up for a

Lifelong Learning Journey

• But, since you’re here, you probably view this as an

exciting challenge, a beneficial aspect of your career:

• Always something new to learn!

• Those who learn will have the advantage!

Wo r d s o f W i s d o m

Hello?

Read, have an opinion, and share it in class.

It lets us know you're not dead.

(It also keeps the faculty from feeling dead inside.)

Study?

500-Level Courses

Less work? More work?

Percent learned in classroom:

100 - (L / 10)

Time Management

It's perfectly fine to party until 3:00 in the morning,

so long as you have the stamina, and that you studied

for 6 hours early in the afternoon.

Make Good Decisions

Just remember that—EVERY DAY—you will make

decisions that determine whether you DO or DO NOT

make progress toward your degree.

Teamwork

The first 14 days here is the best time to get to know

people. You're going to need people as a sounding

board to echo what you're learning. THIS is what will

make your time here unique, and special.

Classroom

Dazzling concepts will be presented every day, but

without any dazzling special effects.

(Find your inner motivation for those times.)

Romance

Write down the key ideas you hear in our courses.

They make great conversation starters at the Corner

Pocket or Green Leafe.

Memorization

There's a difference between memorizing and critical

thinking--we'll work on the latter but you'll need

both. You can memorize a girl or guy's phone number,

but you'll need critical analysis to determine if he or

she will be seen with you.

Analytics Assignments

Remember that assignments in analytics are not

designed to be completed in one sitting. So, when

you see deadlines coming up, that's not a signal to

start working on an assignment--- (That works well in

undergrad for some, but not grad school.)

Deliverables

Rather, we faculty try to coordinate and stagger your

deliverables when possible. Fall faculty meet

regularly.

But, due dates are not work dates.

Don’t Worry

On another note, beware that you might spend 20

hours on an assignment, and another team member

spends only 5. It's OKAY. Yes, it's fair.

(Your career rewards outputs, not inputs. You,

however, will reap the benefits of your own inputs.)

Deadlines

Deadlines are closer than they appear.

You might be able to get an extension on an assignment,

but it will help if you are wearing a sling, or bandages.

Emailing questions to the faculty about an assignment

right before it’s due doesn’t get an extension..

It didn’t work in 1995 for (some of) us either.

Finally

Relax, and have fun. You have lots of support here.