Data Science & Analytics are Changing University Course Design BUT Software Engineering Has A Word Of Caution

Total Data Science
Published in
7 min readDec 21, 2021
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This article dives into the new shift of university programs to include data science and analytics courses and how software engineering can sound a word of caution.

If you are in academia, you should have already realized that there has been a major shift of university courses designs to include more data science and analytics courses.

If you are outside academia, you should have noticed that there has been a proliferation of data science and analytics courses, from short certifications to diplomas through degrees.

Over the past two years, most universities and colleges have included many analytics related programs such as Bsc in Data Analytics, MSc in Data Science, BSc in Business Analytics, MBA in Data Science, MSc in Artificial Intelligence, MSc in Machine Learning, etc. and many specializations at the P.h.D. level.

One may ask why the sudden shift?

Well, universities are business entities set to make a profit, just like any other business venture. A university that has not adjusted to the new normal form of business demands(i.e. the demand for graduates with analytics skills) will be short of applications. Even students who earlier on had enrolled in traditional degrees such as marketing, law, geography, history are taking extra courses related to analytics. It will interest you to see an English major learning Python programming and SQL which was not the case about five years ago.

Most universities are now finding a way to tune their already existing curriculums in most programs to include analytical courses such as Python, Data Analysis, Visualisation, etc.

In the USA, about 65% of the universities have introduced MSc in Data Science among their traditional graduate programs. This trend is similar in Canada, China, India, Australia, among others.

The Data Science and Analytics market is growing, the demand is high, the interest is surging and the trend is promising.

BUT there is an expression of worry.

Are the curriculums being designed by the universities today ready to meet industry demands tomorrow?

Let’s go back in time…

The first company founded to provide software products and services was Computer Usage Company in 1955. Before that time, computers were programmed either by customers, or the few commercial computer vendors of the time, such as Sperry Rand and IBM.

The software industry expanded in the early 1960s, almost immediately after computers were first sold in mass-produced quantities. Universities, government, and business customers created a demand for software. Many of these programs were written in-house by full-time staff programmers. When Digital Equipment Corporation (DEC) brought a relatively low-priced microcomputer to market, it brought computing within the reach of many more companies and universities worldwide, and it spawned great innovation in terms of new, powerful programming languages and methodologies. The new software was built for microcomputers, so other manufacturers including IBM, followed DEC’s example quickly, resulting in the IBM AS/400 amongst others.

The industry expanded greatly with the rise of the personal computer (“PC”) in the mid-1970s, which brought desktop computing to the office worker for the first time. By the early 1980s, software engineering had already emerged as a bona fide profession to stand beside computer science and traditional engineering.

Before 1970, men filling the more prestigious and better-paying hardware engineering roles often delegated the writing of software to women, and legends such as Grace Hopper or Margaret Hamilton filled many computer programming jobs.

Software engineering was spurred by the so-called software crisis of the 1960s, 1970s, and 1980s, which identified many of the problems of software development. Many projects ran over budget and schedule. Some projects caused property damage. A few projects caused loss of life. The software crisis was originally defined in terms of productivity but evolved to emphasize quality. Some used the term software crisis to refer to their inability to hire enough qualified programmers.

Let’s come back to what we were talking about earlier…

After the software crisis, many universities began to include formal courses in their course catalog to train professionals and students on software engineering. Earning one of these degrees was like getting a gold medal in the Olympics (a sure path to success.) The late 1990s and early 2000s saw a spike in professional software developers who are certified by one institute or another. Companies craved these certified professionals and the salary was good. Many parents and guidance managed to convince their wards to do software engineering at their college times and it was a proud moment for both families and schools offering these courses.

During 2008, 2009, and afterward saw a different scenario.

Many graduates could not write effective codes that can be deployed in production. Some who know how to write code are short of socio-communication skills. Many people graduate with the same qualification but few can actually implement what is in their head.

What was the problem?

Technology changed BUT the school curriculum is at best static over years. The problems that the curriculum designed in 1960 were meant to solve are no longer the same as the software problems today. Things have changed, new technologies have arrived, new business models have evolved, new techniques of doing things are rapidly creating new levels of complexity. What about university curriculums? They have remained the same over decades. You may probably bear with me that what you learned during your time is the same thing your child is learning today in most universities. In addition to that, most university teachers or lecturers either do not have a taste of industry experience or do not keep up to date with industry trends. After all, they all passed through the same curriculum. Few make effort to be in the know. They are die-hard curriculum implementors. These are partly the cause of the problems in software engineering courses existing in the various universities and the woes of unemployment in software engineering graduates.

What does this mean to universities that are designing Data Science curriculums?

Caution, visionary, versatility, and open learning are a few terms that should come to mind when designing a Data Science curriculum.


Universities should not only think of how many students will apply to their programs but how their students will be relevant in the industry after graduation. Though Data scientists are in high demand, there are more positions that are not still field and that is because there are few who really understand and have what it takes to do Data Science work. If the school curriculums are repeating the same mistakes that the software engineering curriculum did 10 years ago, then there’s no point initiating it in the first place.


The Data Science curriculum should be made flexible enough to be modified to include new state-of-the-art research outcomes. Strict curriculums are thing of the past and should be buried. At least not the era of Data Science. Bodies such as the University Grants Commission (UGC), Ministry of Education, etc. should give universities the autonomy to maneuver around the nationally accepted curriculum. Lecturers as well should have the autonomy to digress from committee-designed curriculums to include their own research and recent trends they find helpful to students.

Open Learning

Open learning is one of the most important concepts that universities can leverage to help their Data Science enthusiastic students to be more creative in their own way and go beyond the school curriculum and expectations in exam performance. By Open Learning, I mean where students are not restricted to any specific path but are allowed to explore their own path within Data Science. Data Science is all about how you can creatively work around data and bring out something beyond the ordinary.

Word of advice for aspiring Data scientists.

There is no miracle in becoming a Data Scientist. You can go to the best school or pay for the best course, if you do not engage in practical work, forget about it.

Get yourself an internship(whether paid or unpaid) to have a real taste of how is it like to do hands-on Data Science. Or better still engage in hackathon initially to a sense of it BUT don’t stay there for long. Most hackathons have already prepared datasets for you to work on, but in the real world, no one gives you a prepared dataset to work on. In fact, that is where that real data science lies. If you are working on a problem statement with already prepared datasets then where is the Science in the Data? The best way is to get an internship or join a startup to get your hands dirty.


Data Science is said to learn from past data to predict the future. If the curriculums that are being designed today are not seeking to overcome the problems that software engineering faced years back, then it is a failure from day 1.

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