In my undergraduate business and economic analytics course, I have adopted Murtaza Haider‘s excellent text Getting Started with Data Science. I chose it for a lot of reasons. He is an applied econometrician so he relates to the students and me more than many authors. I truly have a very positive first impression.

Updated: November 7, 2020

On my campus you can hear economics is not part of data science, they don’t do data science, that is, data science belongs to the department of statistics (no to the engineers, to the computer science department, and on and on like that.) We have come a long way, but years ago, for example, the university launched a major STEM initiative and the organizers kept the economic department out of it even though we ask to be part of it. Of course, when they did their big role out, without our department, they brought in a famous keynote speaker who was … wait for it … an economist.

My department , just launched a Business Data Analytic economics degree in the College of Business Administration at the University of Akron. We see tech companies filling up their data science teams with economists, many with PhDs. Our department’s placements have been very robust in the analytic world of work. My concern is seeing undergraduates in economics get a start in this field. and Murtaza Haider offers a nice path.

Dr. Haider, has a Ph.D. in civil engineering, but his record is in economics, specifically in regional and urban, transportation and real-estate, and he is a columnist for the Financial Post. and I can attest to his applied econometrics knowledge based on his fine book which I explore below.

WHAT IS DATA SCIENCE

Haider has a broad idea of what is data science and follows a well-reasoned path on how to do data science. Like my approach to this class, he is heavy into visualizations through tables and graphics and while I would appreciate more design, he makes an effort to teach the communicative power of those visualizations. Also, like me, he is highly skeptical of the value of learning to appease the academic community at the expense of serving the business (non-academic) community where the jobs are. I really appreciate that part of it.

PROBLEM SOLVING AND STORYTELLING

He starts with storytelling. our department recognizes that what our economists do, what they do to bring value is they know how to solve problems and tell stories. Again this is a great first fit. He then moves to Data in a 24/7 connected world. He spends considerable time on data cleaning and data manipulation. Again I like how he wants students to use real data with all of its uncleanliness to solve problems. Chapter 3 focuses on the deliverables part of the job and again I think he is spot on.

Then through the remaining chapters he first builds up tables, then graphs, and onto advanced tools and techniques. My course will stop somewhere in the neighborhood of chapter 8.

(Update: Chapter 8 begins with the binary and limited dependent variables, and full disclosure my last course did not begin this chapter, we ended in Chapter 7 on Regression). Perhaps the professor in the next course will consider Getting Started in Data Science for Applied Econometrics II. (Update: Our breakdown in our Business Data Analytics economics degree is that Econometrics I is heavily coding and application-based, while econometrics II is a more mathematical/ theoretical based course with intensive data applications. It is a walk before you run approach, building up an understanding of analysis and data manipulation first. )

I use a lot of team-based problem-based learning in my instruction and Haider’s guidance through the text is instructing teams how to think through problems to get one of many possible solutions, not highlighting only one solution. In this way, he reinforces both creativity in problem-solving. I like what I read, I wonder what I will think after students and I go through it this term. (Update: I/we liked the text, but did not follow it page by page. The time constraint of the large data problem began to dominate and crowd out other things, hence why I did not get to Chapter 8, my proposed end. However, because in course 1 which emphasizes data results over theoretical knowledge, I was well pleased.)

PROBLEM ARTICULATION, DATA CLEANING, AND MODEL SPECIFICATION

Another reason I like the book so much is he cites Peter Kennedy, the now passed, research editor for the Journal of Economic Education. Peter was very influential on me and applied econometricians who really want to dig into the data. Most of my course is built around his work and especially around the three pillars of Applied Econometrics.: (1) the ability to articulate a problem, (2) the need to clean data, and (3) to focus deeply on model specification. He argues that most Ph.D. programs fail to teach the applied, allowing their time to focus on theoretical statistics and propertied of inferential statistics. Empirical work is often extra and conducted, even learned, outside of class. I have never taught like that (OK, maybe my first year out of my Ph.D.), but my last 40 years have been a constant striving to make sure my students are prepared for the real as opposed to the academic world. Peter made all the difference bringing my ideas into sharp focus. I like Haider’s work, Getting Started with Data Science, because it is written like someone who also holds the principles put forth by Peter Kennedy in high regard.

SOFTWARE AGNOSTIC, BUT TOO MUCH STATA AND NOT ENOUGH SAS

On page 12 he gets much credit for saying he does not choose only one software, but includes “R, SPSS, Stata and SAS.” I get the inclusion of SPSS given it is IBM Press, but there is virtually no market for Stata (or SPSS) in the state of Ohio or 100 miles around my university’s town of Akron, OH. Also, absent is python, which is in heavy use in the job market. You can see the number of job listings mentioning each program in the chart below.

I am highly impressed with Haider’s book for my course, but that does not extend to everything in the book. My biggest peeve is his heavy use of Stata. I would prefer a text that highlights the class language (SAS) more and was more sensitive to the market my students will enter.

Stata is a language adopted by nearly all professional economists in the academic space and in the journal publication space, however, I think this use is misguided when the book is to be jobs facing and not academic facing. While he shows plenty of R, there is no python and no SAS examples. All data sets are available on his useful website, but since SAS can read STATA data sets that isn’t much of a problem.

SAS Academic Specialization

Full disclosure, we are a SAS school as part of the SAS Global Academic Program and offer both a joint SAS certificate to our students as well as offering them a path to full certification.

(Update: The SAS joint certificate program has been rebranded and upgraded to the SAS Academic Specialization and is still a joint partnership between the college or university and SAS, but now in three tiers of responsibilities and benefits. We are at tier 3 and the highest level. Hit the link for more details.)

We also teach R as well in our forecasting course and students are exposed to multiple other programs over their career including SQL, Tableau, Excel (for small data handling, optimization, and charting/graphics), and more.

Buy This Book

Most typical econometric textbooks are in the multiple hundreds of dollars (not kidding) and almost none are suitable to really prepare for a job in data science. This book on Amazon is under $30 and is a great practical guide. Is it everything one needs? Of course not, but at the savings from $30 you can afford many more resources.

More SAS Examples

So it is natural given our thrust as a SAS School, that I would have preferred examples in SAS to assist the students. Nevertheless, I accepted the challenge to have students develop the SAS code to replicate examples in the book. This is a great way to avoid too much grading of assignments. Let them read Haider’s examples, say a problem that he states, and then solves with STATA. He presents both question and answer in STATA and my student’s task is to answer the problem in SAS. They can self check and rework until they come to the right numerical answer, and I am left helping only the truly lost.

Overall, I love the outline of the book. I think it fits with a student’s first exposure to data science and I will know more at the end of this term. I expect to be pleased. (Update: I was.)

If you are at all in data science and especially if you have a narrow idea that data science is only Machine Learning or big data, you need to spend time with this book, specifically read the first three chapters and I think you will have your eyes opened and a better appreciation of the field of data science.

Between 1980 and today the world is getting better, humans are making amazing progress.

GDP per-capita in the US rose from $28,590 to $54,542, almost doubling as measured in 2010 dollars.

Worldwide, extreme poverty fell by over half as measured by the world bank. (42 percent of the world’s population was in extreme poverty in 1981, but by 2015 only 9.9% of the world was in that state).

The number of wage salary workers that are paid at or below the federal minimum wage in the US fell from 15% to 2%.

The US Official Rate of Poverty rose by 0.5 percentage points.

Wait. What?

The world is improving even if you don’t think so.

Ask your friends about the drop in extreme poverty. I bet most get it wrong. My evidence is from the Misconception Study conducted by Gapminder Foundation. In fact, take their test to see how many misconceptions you have about the world. (It is right on the front page at https://www.gapminder.org/). Out of 12 questions administered to thousands across the world, the average score for every group is less than if the answer had been chosen by random.

Once misconception is the world is getting worse, when indeed it is really getting much much better. But stories of better do not lead the news, only stories of woe. Further if you got your education in the 70s and 80s as I did you may have many misconceptions simply because you believe data learned correctly then has not changed.

Why has the official poverty rate not fallen with all this world wide progress?

If world extreme poverty is down, why is the US official poverty rate so flat, nearly the same now as almost 50 years ago? The first problem is world wide poverty is bench marked on an absolute income standard. The OPR in the US is a relative income standard. They measure very different things.

The second problem is income is the wrong measure for poverty. Using bad measures of important concepts like poverty creates a misconception that the problem is much worse and virtually unsolvable and attracts policy prescriptions to do exactly the wrong thing.

The World Bank expects Extreme Poverty to essentially vanish by 2030. The US government has made no such forecast by any year in the future.

What is the better measure of US poverty?

Meyer and Sullivan track what it costs to consume at a level not to be in poverty, that is, to create a consumption poverty rate (CPR) that is shown on the last track. The better question is not do the US poor have enough income, but do they have enough consumption? Without getting into what poverty programs are good and bad, the case of food stamps, now SNAP, is such instructive. Take two families with identical income and one of them received one or more consumption based forms of assistance such as SNAP and clearly one is relatively better off. The OPR does no consider any assistance to the people in poverty that they measure.

But a goal to eradicate poverty needs to be based against an absolute standard with policy clearly targeting families to get them across that standard. We do not want people deprived. It is not about income, its about existence beyond deprivation.

One of the reasons for the consumption poverty rate (CPR) is consumption is a better predictor of deprivation than income. (Perhaps two people have the same income, but one cannot afford to put good food on the table, who is worse off?).

You can find their excellent paper here (https://leo.nd.edu/assets/249750/meyer_sullivan_cpr_2016_1_.pdf)

To listen to the news media and the advocacy groups everything is a crisis and a disaster and the world and the US is getting worse. The nice thing about data is it proves the obverse, the world and the US is getting better at a rate begun in about 1980 that is astonishing. But good news does not bleed and therefore will not lead.

So here is what is remarkable: From 1980 to 2015 the consumption poverty rate fell by 9.4 percentage points, while the official poverty rate rose by 0.5 percentage points.

So what make more sense, that the US has a poverty rate of 13.5 (in 2015) that is virtually impossible to lessen or eliminate, or a Poverty rate based on consumption that is 3.5% of the population that we might be able to further reduce.

I would like to see the end of poverty wouldn’t you?

Be a Data Skeptic and do your own research

We hear constantly about bias reporting and fake news and you should be motivated to be skeptical about any data you hear reported and motivated to search out the actual facts.

In other cases, such as the FBI’s hate crime data, the data are not reliable without understanding how it is collected. The data is fine, but year to year comparisons are not easily possible because of the data design. (see The importance of data skepticism. Hate crimes did not rise 17% in one year. )

Many data websites do exist to help you find actual facts.

Some of the best fact based sites are
https://Justfacts.org
https://Gapminder.org
https://Fred.org
https://humanprogress.org.

So the message is be humble, don’t believe everything at face value and learn how and do your own research.

Month: August 2019

A Data Science Book Adoption: Getting Started with Data Science