Mastering Business Analytics: The real MBA for career success, Step-by-Step
Uniquely, if our students aren't
satisfied, we aren't satisfied. Should a class ever fail
to live up to student expectations, there's no charge for it.
Even trying to skirt re-education, unfortunately, finds H1B and other factors are currently ruling out many traditional career alternatives to the (B) of this page's opening paragraphs. Would it be overly indelicate to note Big Blue's most recent contribution to the problem? (That's a fraternal dig; Lou and your present scribe spent several years in the same "gang.")
Approach: We introduce topics based upon business decision-making needs, not functional theoretic constructs. If students understand the business need, theoretics tend to take care of themselves. We push for hard-kill solutions coupled with executive-friendly communication. Excel (with add-ins!) constitutes both our analytical and communication platform. Within the first 30 minutes on day-1, students start producing their first model; it doesn't remotely resemble a, "Hello, World." (It's, basically, the example cited above.) We raise the bar continually as the students progress.
Over the two-module Core sequence, students come to find communicating both easy and enjoyable. "How's that?" Half of the communication "battle" is having the right answers, the "what" to communicate. No problem, you'll find that you can deal with virtually any problem — and, more importantly, recognize those with which you can't.
The other —and far trickier— half of communicating entails learning "how" to communicate. You'll ace the "how" by mastering accounting-speak and graphic communication. Without fitting a too fine point, you'll come to find executive presentation both entertaining and, quite often, amusing. BTW, we are great fans of Tufte, however, it's often technically impossible to hew to all his tenets; in corporate settings, "simple" tends to prove best.
Though perturbation, optimization, and simulation rapidly become second nature, students are constantly drilled on effectively presenting findings in ways palatable to senior management.
Structure: Each module is cross-curricular (integrated), analytical, and limited to "hard" subjects. Eliminating the MBA curricular fluff and the "review" otherwise necessitated by traditional silo-centric academic coverage reduces necessary contact hours by about 80% while vastly improving understanding and retention. Think: Pareto ("80/20 rule"). Here's an example (again, as cited above) of cross-curricular/analytical coverage using a simple Excel model and PowerPoint "explanation."
Class format typically blends (1) grad-school seminar with (2) lab-team analytic exploration (of mini-cases). Though students may team largely as they wish, they are urged to avoid monolithic composition, e.g., all engineers. You must learn to communicate with all "types"; part of that learning entails understanding (better) how "they" think ... okay, "think."
Focus/Content: "Traditional" academic strictures cause even AMP's to suffer factoid-itis bloat. Our curriculum focuses upon strategy, tactics, and metrics (STM); these are the foundation elements of business. Organizing around STM, instead of academic departments, integrates the curriculum and eliminates redundancy. Our "default" metric is financial statement-centricity — as are those of most businesses (read: ROA, CFROI, etc.). A highly beneficial consequence of this focus is that learning to apply the STM-isa framework to any business entity prepares the student to work in any business sector, ergo, any other entity. Kiss industry career constraints, goodbye.
Focusing upon the primary financial statements reinforces learning. Such focus also reduces the learning framework to grasping a series of cause & effect Excel models. We teach in spirals by revisiting variants of familiar mini-cases and their related models. The objective is to both extend and reinforce conceptual/ theoretic understanding and quantitative methods. Taken together, these didactic elements greatly improve learning efficiency. Crucially, students also develop an executive perspective built from the ground up, better informing analysis and vastly facilitating communication. They also develop a highly useful portfolio of models for use either "as is" or for "code-scavenging."
Analytics: Our Core (below), as all of our modules, is cross-functional, intensively analytic, and focuses upon quantitative methods modeled in Excel (Excel, "Builder" model) with companion "explanations" (PowerPoint, ditto). That said, unless your name is Walkenback, some of it will likely seem a foreign dialect of Excel as "our" Excel begins where the 1200 page doorstops typically end and relies upon undocumented or under-documented functionality.
Unlike most of the world, our students revel in abstract thought and enjoy intellectual rigor. They are unalarmed by basic calculus concepts. Again, non-quants, relax. Hint: Let Excel do the heavy lifting as a "black-box." (Gratuitous aside: Raw IQ can go a long way in offsetting weak mathematical preparation, but you might want to get this book anyway.)
Faculty: Our faculty members are grad B-School rarities: Current/former professors who are also seasoned executives (many C-level). Several of us were scientists / engineers before getting the MBA's which we all took many years ago. (Your scribe: former scientist, CFO, COO, CEO; member of 8 different functional academic departments at 4 major universities.) All are actively engaged in business.
Much of the advanced coursework is based upon real-world datasets which we didn't merely research. We interpreted, strategized, and led implementation (today-speak: innovation). Crucially, students may ask, "how, what, why," questions and get intelligent answers. (No surprise, "who," typically remains off limits.)
Our approach is fraternal not professorial. Our classrooms are collegial and (reasonably) egalitarian. Our sole objective is student learning, full-stop. No ego-trips; no beggarly attention. We, each, owe much to early career mentoring. This isa undertaking seeks to partially repay our mentors by passing on the mentoring "torch." More, here.
ISA-1 Presupposes NO formal business education. It provides an intensive economics-grounded understanding of accounting concepts, accounting terminology, finance, and financial methods. Marketing and operations topics and concepts are introduced (JIT) to assure that students understand precisely how what they’re learning relates to how the overall business actually functions. The analytical approach stresses perturbation and deterministic methods. Limited use of Access as a statistics-support ETL front-end to Excel is introduced — infinitely more useful than 1 million-row spreadsheets! — in anticipation of later real-world datasets. Support topics, e.g., statistics, are introduced / reviewed as necessary and any ancillary software is provided. As with ISA-2, key topical coverage includes: macro-economics, micro-economics, financial accounting, managerial accounting, managerial finance, corporate finance, managerial marketing, operations management, and statistics. The material is presented in Office 2K3 on Win XP. Students are expected to have (FrontLine System's) Solver loaded as an Excel add-in.
ISA-2 Delves deeper into financial concepts and their accounting underpinnings to expand financial literacy. Appropriately, the models become more sophisticated, including fairly complex cross-functional (e.g., finance & marketing) analysis. Quantitatively, the module builds a deeper understanding of deterministic modeling, while extending abilities considerably with probabilistic concepts and methods. So, instead of merely being able to respond in real-time to virtually any form of, "What if," question, students selectively reduce model rigidity (and brittleness) by replacing fixed mathematical drivers with appropriate probability distributions. As real-world datasets tend to be large, we continue some use of Access as a SQL intermediary for Excel. (Note: We do more with large datasets in the advanced modules in finance, marketing, statistics, and SPSS. Our focus, here, is more introduction than "practice.") As in ISA-1, ancillary topics, e.g., probability, are introduced / reviewed as necessary and requisite ancillary software provided. ISA-2 requires ISA-1.
Still reading? Should any SlashDot-ers actually get this far, three prime factors have driven our undertaking this "open" program. One has been increasingly frequent requests for admittance from non-client individuals — predominantly engineers and CS/IT-types, but, interestingly, quite a few MD/JD's, too. Another has been our own difficulty in finding suitable talent for our consultancy. What had once proved bullet-proof "sourcing credentials," are now just laughable; sad, really. The third is our collective disgust at what's happened to graduate business school education in the name of "research."
That's what we're about.
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Thanks, again, for your help. We do certainly appreciate it.
human element — all work and no play ...
We make a point of leaving quality time for student relaxation and socializing—the real
(Experienced) != (Dead )
Of course, you could always opt for the Video
Professor. (Now his inane infomercials make some sense!)
Be sure to read the, "And this:," at the bottom.
The academic plague is spreading!
Some "entertaining" derivatives links, here, here, here, here, and here. There's just TONS more to come and it's disguised as high-yield 30-year paper (investments) in lots 'n lots of (to name but a few) pension-, credit union-, and hedge-fund portfolios around the globe ... some $700T. That's not a misprint, read: trillion. Straining credulity, JP Morgan-Chase is pushing $50T by itself. All of this nonsense is predicated upon grossly flawed "research," relying upon laughable "theoretic models," and implemented by shoe salesmen in expensive suits.
Amusingly, two progenitors of "modern portfolio theory" received Nobel prizes for same in 1997. In September of 1998, the hedge fund, LTCM, failed spectacularly (overnight) to the tune of some $13B owing to a Russian rouble derivatives cock-up. (Needless to say, they tried to put a different "spin" on it.) A fine irony stems from said Nobel laureates being partners in and trading strategists for said LTCM. Sic gloria transit ... et fortuna.
If all this seems implausible
—that research long-accepted as "gospel" could be so wrong —
try this, this,
oops! Okay, the underlying research referenced via the NYT and WSJ articles
also puts a double-underscore to how few researchers possess even a reed-thin
grasp of statistics and/or statistical methods. Chant in unison, "self-selected
samples are NEVER 'random' " and "correlation does not equate with causality."
Oh, well, at least YOU get it....