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FAQ > 
FAQ 2012
Last revised: July 15, 2012

Reference Room

DePaul University offers an M.S. degree in predictive analytics.

DePaul's M.S. in Predictive Analytics)

DePaul University advertises: "The program aims to prepare students with the required qualifications to become 'data mining analysts/engineers' or 'predictive modelers'."

North Carolina State University N.C.S.U.) offers an M.S. degree in analytics (M.S.A.)

N.C.S.U. M.S.A.

N.C.S.U. advertises: "What if there was a graduate education that could produce future leaders who understood how to extract value from vast quantities of data? Now there is."

Northwestern University offers an M.S. degree in predictive analytics (MAPS)

(Northwestern's M.S.P.A.)

Northwestern's School of Continuing Studies advertises that its online M.S.P.A. program "enhances students' capability for data-driven decision making."

Northwestern University offers a Master of Science in Analytics

(Northwestern's M.S.A.)

Northwestern University's McCormick School of Engineering offers a full-time, on-campus Master of Science in Analytics (M.S.A.) program.

Its website states: "All three areas of data analysis are studied: predictive (forecasting), descriptive (business intelligence and data mining) and prescriptive (optimization and simulation)." 

What is RewardandRisk AnalyticsTM?

"RewardandRiskAnalyticsTM". It's a mouthful of syllables, so we'll break it down into smaller pieces that you can chew, swallow, and digest.

Analytics (I)

"Analytics" is the study of analysis and/or the body of knowledge about analysis, just as statistics and physics are specific fields of study and bodies of knowledge.


"RewardandRisk" is our name for a bundle that consists of both Reward and Risk. In the real world one seldom has an opportunity to reap a Reward without the possibility of Risk. In the diagram for the Markowitz model, a point to the right of the vertical axis denotes a portfolio that has RewardandRisk.

In an uncertain world, many future outcomes are "uncertain", sometimes known as "risky". A probability distribution describes the relative frequency that we would se the various outcomes, if we conducted the experiment infinitely many times.

For example, if you do something that will produce a numerical result, with equal probability, that result could be -1 or +1, each with probability 1/2. The Reward could be receiving the outcome, 1. The Risk could be receiving the outcome, -1, or it could be receiving 2 less than the +1. The Reward might also be the expected value of the outcome--zero, in this case. The risk might be the standard deviation of possible outcome, +1 in this case.

In Markowitz portfolio theory, the Reward is an expected rate of return, and the Risk is a standard deviation of rate of return. In the logo at the top left of each page on this website, Reward is on the vertical axis, and Risk is on the horizontal axis. The green curve indicates the RewardandRisk for minimum-variance portfolios. Risk is always positive, so it is not possible to get any Reward--even a negative Reward--without Risk. This is a standard result for portfolios with Risk assets. 

People in different jobs and professions—including actuaries, ballisticians, chemists, derivatives traders, engineers, farmers, gamblers, human cannonballs, investors, judges, kangaroo wranglers, lawyers, medical doctors, narcotics agents, ordnance disposal specialists, political pollsters, quantitative analysts, real estate agents, social workers, toxicologists, urban planners, veterinarians, waitresses, x-ray technicians, yak milkers, and zookeepershave to deal with bundles of RewardandRisk. So do parents, grandparents, employees, retirees, etc.—all of us, really.

You and I and everyone else try to do things for our personal benefit (the "Reward"), but everything we do for our benefit exposes us to a potential downside (the "Risk"). Inevitably, Reward and Risk go together. We call the package "RewardandRisk".


So, RewardandRiskAnalyticsTM is the study of (or body of knowledge about) the analysis of the opportunities (bundles of Reward and Risk) that we call RewardandRiskTM.

RewardandRisk Management

When confronted with RewardandRisk, we manage it, somehow. We may ignore the issue out of laziness, fear, or indecision. We may compulsively avoid action. We may dodge the issue. We may think things over and decide that "benign neglect" is the best way to manage it.

We may act, without thinking, perhaps doing what Mom or Dad did in the same situation. We may analyze the issue to death.

Sometimes, we may manage it well. Sometimes not so well. 

RewardandRisk Analysis

If the stakes are high enough, we may analyze the RewardandRisk appropriately, carefully, and professionally. By their nature, RewardandRisk is typically uncertain, so proper analysis would involve probabilistic and statistical analysis. A specific type of RewardandRisk may call for a specific type of analysis. As the old saying goes, "The problem defines the solution." The point is that several forms of analysis may be possible for analyzing RewardandRisk, and the study of those forms of analysis is Analytics.

'Analytics'. That's just a fancy word for 'analysis', right?

No, but glad you asked. "Analytics" is a field of study and a body of knowledge. Namely, "Analytics" studies forms of analysis. Any single type of analysissuch as portfolio analysis, qualitative analysis, quantitative analysis, or statistical analysis is just one thing that Analytics studies. Here's the explanation in two steps: 1. Analysis, 2. Analytics.

1. Analysis

"Analysis" is the process of breaking something complex thing into its components, and seeing how those components relate, so as to gain a greater understanding of the complex thing. For example, if a five-year-old were driving his grandmother crazy, she gave him a broken down, windup clock and a screwdriver, and the kid disassembled the clock and observed the springs and gears that made it tick—literally—that would be analysis. If the tyke reassembled the clock, that would be synthesis—combining pieces into a single entity. If the reassembled clock worked, that would be a miracle.

Thousands of years ago, the most developed and esteemed form of analysis was logical analysis. Geometry (geometrical analysis) was important for building pyramids and other structures. Today, we have many more forms of analysis: chemical analysis, qualitative analysis, quantitative analysis, data analysis, statistical analysis, time-series analysis, economic analysis, portfolio analysis, psychoanalysis, etc.

2. Analytics (II)

"Analytics" is the synthesis of the noun, "Analytics" (discussed, above), and the suffix, "-ics". The suffix, "-ics", indicates "a body of facts, knowledge, principles, etc." (e.g., statistics and tactics) or—more often—a field of study.  Examples of fields of study include aerodhynamics, civics, classics, economics, ethics, geriatrics, linguistics, mathematics, pediatrics, phonetics, physics, politics, psychometrics, semantics, semiotics, statistics, thermodynamics, and hundreds of other fields of study. The article, "Analytics", in Wikipedia, says, "A simple definition of analytics is 'the science of analysis'."

We use the term, "Analytics", to mean (a) the field of study of analysis and/or (b) the body of knowledge about analysis, depending on context.

"So, everyone agrees to your definition of 'analytics'?"

That's like asking if everyone agrees with my definition of automobile. My definition of automobile is the standard definition of automobile. My definition of Analytics is the standard definition, which I have explained in detail.

I have stated and explained my definition of one proper subset of "Analytics": RewardandRisk Analytics. Does anyone disagree with that definition? I don't know. 

Other subsets of Analytics exist.  The working definition of "data Analytics" appears to be consistent with "the study of forms of data analysis". "Predictive analytics" seems to be "the study of forms of prediction, using data, statistics, and computers". People in that field don't mention any role for crystal balls, Ouija boards, and seances.