Now that you may have or have not read the following article which we have shared and written in our BLOG many years ago.  Apparently, there may have some correlations why you have chosen to attend a Masters degree program in Business Analytics or to become a future data science professional with the information that we have shared in the past several years.  This article has confirmed AAEA’s early hypothesis, in that the US is experiencing serious shortages for data science professionals.  This is the reason why many higher ed institutions are offering the program, starting in the past a couple of years.

To the current graduate students, the Association has a little suggestion while you are still in your program.  It may help you to take useful classes or are thinking on doing a summer internship before finishing your graduate program.  First and foremost, your classes only fill a-half of the knowledge and expertise in preparing you as the future data scientist.  The other half is coming from the real world.  How can you fill and get the experience from the real world in data science, unless you have been or are employing in the industry or your current company.

If you start fresh the second career as a data scientist, a few things that will increase the odd (with disclaimer) that your dream will come true are:

1. Capitalize the real-world knowledge that you may have had in the past or currently.  For example, if you are, for whatever reason have received your undergrad in “soft” science which have forced you to work at a minimum wage company such as at one of the fast-food chain stores, you can and need to capitalize your past experience in the fast food industry.  Therefore, concentrate to understand what kind of data which you can analyze differently which will help the corporate office of that chain store may see your experience as important core competency.
2. If you never been working, fresh after your undergrad graduation to attend the grad school, you need to think what do you want to specialize in?  For example, after the second and third year, a medical student has a pretty clear idea what he wants to specialize in and where he wants to work and live.  Becoming a brain surgeon, while making the top dollars, may not be fun for those who cannot cope with the work related stress, plus it took extra years to finish the residency program.  If you are unsure, a good start is looking at a grocery store shelf.  The next time when you go shopping at one of your favorite stores, look what are available on the shelf.  This may give your some future ideas what industry or even a company you wanna to specialize in?  Great grocery stores have a wide range of products from the pharmaceutical to fresh produce.  From breakfast cereal to jewelry.
3. Do not avoid hard classes such as advanced econometrics or advanced mathematical statistics.  You got to have those, plus various programming classes where you can learn how to code using Python, R, SQL, C++, JavaScript,  etc.
4. You got to have advanced managerial/microeconomics, managerial finance, and if possible managerial accounting.  Depending on what the super-super specialist that you wanna to be?  Think what a fellow in medical school training meant.
5. You need mathematical programming classes where you will be taught how to model and run a stochastic simulation modeling.
6. If your have a two-year program, try to do a summer internship after finishing two semesters of your first year.
7. Be certified.  Example, SAS offers various options.
8. Write and present manuscripts at various professional annual meetings.  Net working with others in your area of interests.  A data scientist has to know how to write efficient reports – and most likely the readers may not have rigorous background in stat or math.
9. Try to teach undergrad classes–this is good training to be a great presenter and a public speaker.  Data scientists need to know how to present their works to others, which usually non-technical users of your findings.
10. Yes, you need to take 2 semesters qualitative theory, psychometric, survey design and advanced marketing research & consumer behavior courses.
11. Last, but certainly not the least important, work as hard as you can and avoid or minimize taking student loans 🙂

Now that everyone is talking about analytics.  When the Association introduced the approach for the first-time, different interest groups are rushing—try to take advantage of it.  The software companies are racing to get their first product in the market–try to get the competitive advantage to be the analytics product leader.  In 2013, there was only one institution which offer formal training in data analytics.  Now, there are more institutions offering Business Analytic programs than, may be the applicants.  They are mushrooming every where. This current development is paralleled with the booming of the MBA program back in the 80’s.  The Association glad be able to share and contribute to the development of the curricula not just in the US, but all over the world.

But, the business consideration and profit motive alone is not enough-companies need to educate their users.  Selling analytics is not just another hamburger store–try to sell as many burgers as possible.  It is more than that.  Most of the users think, analytics is just a point-and-click kind of thing which gives amazing results without knowing how the capabilities are structured and built. It is not just something one can produce and visualize, and show the output to their supervisor without knowing what is going on, and how the algorithm works, and what the assumptions are in finding those solutions. Weather the solution is unique or multiple?  In other words, it is not a canned software driven profession–rather the other way around.  The need to solve the real world problems dictates what kind of software needs to be built.  It is not software that derives the needs!  On other account, it is pretty funny that some users think data visualization and data analytics are equivalent.  However, they are not.  For example, an article written about a couple of years ago using the words “data czars“, yet some of them use data visualization and canned programs which is a bit of inflated to portray them as the “czar”.  However, the Association is glad as the innovator–the first entity to promote the application of analytics in higher ed, which then followed by the laggards.  The use of analytics may not exist, limited, unpopular or never been known or applied until after the article was presented and published.  By the way, predictive analytics is just only one component among many of the IRI-Education Analytics.

These are the flaws, facing most of the users who are lack of understanding about the statistical theory or mathematics behind the software.  Most of the analytics, not data visualization, is built, based on the unwritten assumptions in that the Central Limit Theorem is satisfied and that the data are random and the residual terms behave normally plus some other basic assumptions either for univariate or multivariate regression theory.

Sadly, our previous BLOG has mentioned pretty clear that majority of the events are not random, but systematic.  For example, a college administrator may constantly have made sub-optimal decision (in other words, wrong decision) simply because she or he does not apply any historical data to support her of his decision or using the :best guess” or because the past data are generated by the wrong or suboptimal policy.  In such a case we heard what has been mentioned as GIGO-or Garbage In, Garbage Out. If one applies any type of analytics based on tainted data that contained systematic errors, what kind of results will they produce?  Only garbage. A wise man suggests that knowing your data is the first step to use any analytics applications. Test if the events that generate those data satisfy and are consistent with the Central Limit Theorem.

Can a-non decision maker such as Business analytics or IR professionals make a correction or validating the tainted data pulled from Oracle or any database system?  If you are an analytics user, think about it, before getting too excited in applying point-and-click canned program in your cubical 🙂

Let us bring our discussions on student loans (SL) to the next level.  There are many sub-system entangled one-to-another that affect the student loans which all the American public has seen these days. If the System is broken, then one needs to know what are the reasons or what factors have caused the problems.  To avoid the unproductive discussions, let us approach this important scholarly work by using the real data with approaches that are academically sound, justifiable and verifiable by who ever that have the interest on one of the gigantic tasks to be solved in the modern history of the USA.  We will apply econometrics and multivariate statistics in these efforts.  To do that, let us bring what the possible relationship among the subsystems may look like Figure 1, as shown below.  Basically, there are four main elements–the US lawmakers, US regulator, the US higher ed institutions and the American public and students.  The first three are endogenous to the system, while the student (American public) sub-system is exogenous.  Therefore, the public sub-system is excluded in Figure 1.  So, none of the students has the ability to shape up the policy directly, but through the representative/lawmakers that they may have voted for, iff (if only if) these lawmakers truly represent them on such an important issue.

• The lawmakers who have the power to set the general policy has almost a full-control which direction for Uncle Sam to move to, for example NDEA. If things works as planned, this subsystem supposed to operate for the interests of the students or the public as their constituents.  Did they? may be not, read here.
• The legislator, based on the purpose why this country is established as stated in the Declaration of Independence, is also to work for the people of the country?  Did they?  May be not.  Read here for detail.
• Higher ed, especially at public universities and colleges are supposed to be managed for the interest of the tax payers.  Did they?  May be not.  Click here.

The overwhelmingly evidences show that none of these subsystems actually directed or managed for the benefit of the American public.  Therefore, the student loans are ballooning.  The real world evidences show that all the errors are non-random, but systematic or Bias E(β)≠β head. 

From the statistical theory, random errors, can always be fixed by adding the observations.  In statistical, mathematical concepts or measurement’s theory, there is no cure for systematic errors.  For example, in industrial engineering (IE) field, if a machine, say B that is used to produce the final product name A, consistently yield an output (product A) that cannot pass the quality control standard, then the factory manager got no choice, but to replace that particular machine (B).  But IE and the system of government is not a comparison apple-to-apple.  So, the readers can find their own answers….

Some people may think it is no big deal, when regulator’s spending surpasses its inflows.  Think about your household income.  If ABC family spends more than what they make, somehow they need to cover that excess spending from, say, credit cards.  The same logic applies here in that the credit card company is parallel with buyers of US treasury.  The Fed Chairman, Jerome Powell blamed expensive healthcare delivery system and the aging of our population contributing to such a deficit.  Recently, it was reported the total US national debts is about $21.6 trillion.

One may think that spending has great impacts to the economy, and it does within certain boundaries.  Increasing in aggregate demand has a positive impacts on companies’ revenue from sales.  That makes the stake/stock holders (Wall Street) is happy.  The happiness of Wall Street even higher when the administration cut both the corporate and individual income tax.  These bring huge effects to the DOW (26,743.50 level on September 21, 2018)—the highest since 1929.  So, the economy will expand and unemployment is reduced to the lowest level as well.  In theory, all current macro economics policies will increase the standard of living of many, except those who got trapped with their student loans. In reality, it may not.  Please check if your monthly take home salary has increased this year surpassed the inflation rate, compared to a year ago.

However, if you own, 1 million Amazon’s (AMZN), Alibaba (BABA) or Apple (AAPL) shares, your wealth surely has increased tremendously.  So, you can make your own conclusion where the current economic policies are heading to or directed toward 🙂  But, one needs to be able to have the initial capital/fund/money to buy that 1 million shares at the first place.  We strongly believe that only a handful of US citizen will have that kind of investment money.

Cutting tax while it increases the aggregate demand, it surely will reduce government revenue from tax such that deficits will increase.  However, if the US can increase its export, the build ups may be slower.  Increasing export means winning the competition in the world market. This only happens if the US is able to produce high quality products and  cheaper than other sellers.

The trade skirmish that have been initiated by the administration to many of its trading partners may have lowered the US export.  But, at the same time, it also reduces the US import because of increasing imposed tariffs.  Depending which one is higher, the net effect will have final impact to the budget deficits.

So what did we learn from the discussions above?  It seems that managing the US Colleges and Universities are affected more by the administration’s policy in recent years.  Consequently, education industry is riskier and more challenging to be managed than before. The risk of financial failure does increase, especially to those who relied heavily on government support or aid programs, i.e., Pell Grants, Federal Student Aid, such as Perkins, Stafford, Parent Plus and others.  That is one of the main reasons, why efficiency nowadays, becomes an important vocabulary in managing a higher ed institution.

Interesting enough when one puts together the budget deficits and accumulated student loans side-by-side as shown in Figure 2, then one can start telling an interesting story. Without any formal training in IRI or Education Data Analytics, an individual will notice the two lines persistently are moving toward the same direction, in particular, after 2013.  The existence of a positive correlation between the two lines can be observed through Data Visualization (DV-Figure 2 upper side) or Data Analytics (DA-Figure 2 lower side).  What will be the possible circular and spiral implications of such a relationship?

1. The American public will not see, at least in the near future that the accumulated student loans will decline, so long the country spending is higher than its fund inflows.
2. For those who expect to have their loans are forgiven will soon realize that may be far from reality.  Simple reasons, where the government will get the resources to do so?  Especially if budget deficit continues.
3. Prolong deficits will cause less funding is available from both federal and may be state agencies.
4. US higher ed institutions will depends on tuition revenue to fund their operation.  That said, tuition will likely to creep up.
5. Accumulation of student loans will keep going up.
6. Less and less citizens of the country are able to afford a college education.
7. College enrollment will be impacted in a negative way.
8. With less money is available due to reduction on both federal and state funding, alumni’s donation, as well as from the tuition money, will finally force higher ed institutions to cut their research fund.  That will affect the innovation in the future.
9. College down sizing, merger and closures will likely to continue occurring.
10. The likelihood of universities and colleges employees lay-off will go up.

These circular and spiral effects will get larger impacts to the whole country. When they do, what will happen to the economy?  Your thoughts?

Yesterday, The Association has discussed in its BLOG, the impossible task for selected College graduates in paying their student loans because of lower return on their investment.

Therefore, the Association is not surprised when CNBC has reported in its September 22, 2018 article.  The signs of failures are all over the map.  It is just a matter of time, either a broad loans bailed-out scenario (loans moratorium) will happen or the Adam Smith’s invisible hand will make the correction.  If the market make the corrections, the consequences will be more painful.  It seems that more and more people, who got into the deep trap of their student loans take it into the extreme level, in that, they do not even care to pay their debts anymore.

They just give up in paying, even trying to convince themselves mentally that they do not have any loans.  This level of frustration occurs because their loans are ballooning far beyond all their combined wealth.  It is truly sad situation if a country fail miserably to pursuit of its citizens happiness as it has been stated in the second paragraph of the Declaration of Independence.  There are 8 million people who are defaulting their loans–that means 2.45% of the US total population in 2018 cannot pay their student loans at due date.  That is the same way to say that 8 millions graduates potentially will be impossible to pay their student debts, ever.

Reading the article closely, one may notice two important facts (1). Some students have attended the for-profit schools where employers may not in favor to employ graduates from these schools and (2). Most of them major in soft science (Example, humanities majors).  The reason is obvious–lower ROI (Return on Investment) as discussed before.

About two weeks ago, AAEA has written in its BLOG, short analyses on the student loans interest rate. Our analyses look at the economic philosophy why interest rate is necessary from the Classical Economic theory.  We also presented the rival’s of this theory from the monetarist point of views.  This analysis is an effort to understand, why the lawmakers is charging, in some loans, higher interest rate, than what the commercial banks do.  Only the lawmakers can answer this question for sure.  It seems that the public is the interest-rate takers–that means students have rarely ask, and they may not know about this whole thing until after receiving the first repayment bill from the loan servicing entities.

A necessary and sufficient conditions for any loan borrower to survive in its business plan iff (if and only if) the return on investment (ROI) is higher than the interest charges.  Otherwise, it surely will go bankrupt with the probability of one.  A quick question that student loan borrowers and their family should ask before pursuing any college major is to ask if she or he will get paid high enough that can cover the loan payment amount–if not, be realistic and scrap your plan.  These are the weaknesses that the Association has learned across the board, that the dreamers think they can get by.  May be they can in the past 30 to 40 years ago, but not in 2018.

The Financial Aids Office, the Student Advisors, the Student Success Office along with the loan providers have failed to convey this simple question or may be, they just do not  care.  The same situation with car salesmen–whose sole interest is to sell as many cars as possible–because that is their job. However a student financial aids advisor is not a car salesman.  They are in a better position, and are equipped with a better understanding and resources to better advise the loan borrowers than the car salesmen.

By charging the same interest rate among the student borrowers regardless of their majors is an evidence of over simplifying the student loans issue which has been happening for along time, without anyone critically thinks about it.  As results, the loan problems are getting worse every day as shown in Figure 3).  Increasing students’ default rate is a real example which shows that the ROI from investing in a college degree, for most part, definitely is lower than the interest charges (5-7.78% APR, depending on the type of loans).  So, America may witness another potential big event happens in the history of the country.