Are you wondering whether it is a noble idea to take a Masters in Data Science?

If yes, then you are probably turning every page in university publications looking for more insight on this course.

Data Science has been proven to be of a significant role for obtaining success in conquering consumer markets.

It is due to the utilization of available business data, that enterprises can assess the market needs, trends and even predict events likely to happen in the future.

With the ever-changing market nature, companies have re-strategized, or instead are planning (for those that have not changed over) to give a shot to big data and data analytics in their operations.

As a result, there has been an influx of demand for data scientists in the labor market.


Data Science is not a rocket science.

It is a blend of both statistics and computer science that is designed to equip learners with problem-solving skills that in the long run enable them to make appropriate and informed decisions.

You need to have mastered the art of numbers and be proficient in computing, such as programming and database management garnished with some interpersonal skills.

You can learn all of that through an intense full-time program pursuing a Masters in Data Science and becoming a qualified Data Scientist.

But whether that is the only way to achieve that, you can evaluate for yourself after you finish reading this article.

A study by Burtch Works 2015 alluded that 88% of companies that require big data specialists are looking for candidates who have pursued post-graduate studies.

This means that those who have Masters in Data Science have higher chances to be selected than those who only have a degree in Statistics or Computer Science.

However, don’t take this for granted – read further and learn.

A data Scientist works closely with almost all departments in the institution with an aim to collect data, collate it and analyze it.

Upon analysis, they conclude and disseminate useful information to the appropriate teams for actions.

They literary drive the institution’s thinking. It will be such a great chance to exercise a leadership role as you shape opinions and decisions made at your workplace.

Graduates of this course can fit well not only as a data scientist, which is a general term but also as a data analyst who purposively goes into a fact-finding mission with a hypothesis about the company’s product or even the customer’s feedback and mint relevant information, tests the theories associated and make recommendations.

They also work in collaboration with the administration as monitoring and evaluation officers or even as a data specialist who helps inform on the progress of the institution.

As dreamy and attractive as it looks, be keen to note the flaws related to Data Science Master’s Degree so that you can in a position to weigh the advantages against the shortcomings before you can indulge in erroneous career assertions.

It may not be fun to make a mistake that involves wastage of your limited resources such as time and money.

Study the following four reasons for why not enrolling in a Master of Data Science before you decide to walk back to class.


To acquire your data analytical skills, you do not have to seat in a conventional classroom.

You have been learning mathematics and IT since you were in elementary school and all through your bachelor studies, so you have already acquired some knowledge and skills in statistics and computing.

Then why do you need to waste at least $40,000 reinventing the wheel, yet you have the motor?

Firstly, the content tackled at the master’s level is similar to the one that was learned in the basic degree level. You don’t expect the statistical concept such as Chi-Square test or the techniques of Java Scripting to change just because they are acquired at the master’s level.

The only things that may change are the examples and complexity of the sums.

The rest will be focused on exposing the learner to more complicated setups of statistical theories and manipulations.

Education professionals will advise those repeating ideas over and over again that despite the fact that it may help in enhancing their memorization, it can have its share of adverse effects.

For instance, the learners may have over-confidence and may lose interest in the subject being taught which will consequently lead to poor performance.

Monotony will also be unavoidable when re-reading and can cause boredom.

Secondly, for you to successfully pursue a second degree related to data science, you will require at least a whole year or so away from your regular work, so you can concentrate on your studies through the full-time program.

This would mean that you will spend a whole year without your regular income.

Let us do a quick tally.

What can be achieved in a year?

You can make enough to buy a new car, take the vacation you have anticipated for so long, or even earn enough to put down a deposit for your first house – too much can be ticked off your wish list.

Besides that, this time can be used to make you a better-skilled employee, to sharpen your skills or even if you are the kind who want to add more papers to your resume, you could pursue a different field of specialization altogether.

It is during this period that as you earn more experience in the field can get mentorship from gurus in your area or even enroll an online class to fine-tune the skills you already have.

You will be surprised that some of these database management skills are better learned at your own pace from YouTube, online courses, and offline libraries.

What shall be left to be worked on is incorporating the technical skills with a few interpersonal skills and there you are a super data specialist.

Communication and problem-solving skills among others are competencies that you have garnered all through your life.

You will require those competencies to not only help you to identify existing problems but also to use the knowledge and wisdom you must solve these problems.

It is also one thing to get the information on how to settle a threatening peril to the institution, and it is another to pass that information to relevant authorities who can facilitate action.

You will, therefore, need skills that will enable you to communicate your findings and (or) recommendations to the right persons efficiently.

So, if you are ready to invest time in expanding the education you already have then you can do this without investing your money as well.


Just like you cannot climb a ladder from the top, it is equally impossible to build on the knowledge you don’t have.

Have you ever imagined what would be the outcome if your teacher came introducing the multiplication concept in mathematics while you don’t understand the different numerals?

The learners may not acquire the knowledge they were set to learn, and the teaching objectives will most likely not be met.

It is also true that it can be almost impossible to pick a study subject if you do not know the pre-requisite subjects of study.

Unfortunately, this information might not be readily available when you are making the application to the program.

Most institutions of higher learning are busy trying to add numbers to their flock.

They will use marketers who when approaching you with convincing details about the entry criteria for this course may not mind your abilities.

Some are ignorant of the fact that learners have different capabilities.

In fact, rarely will a marketing agent or an advertiser enlighten you on the specific details entailed. General and non-committal terms are always on their lips.

Phrases like “basic computing” or “basic knowledge in mathematics” will rarely be explained to you as to what level of knowledge is “basic”.

Boaler Jo in her studies concluded that different learners have different thinking and strengths when it comes to STEM subjects.

If you are the kind of students who are afraid of numerals, this course may be problematic to you. You will require not only knowledge of mathematics but also statistical tools and systems.

You will be using computer technology in the analysis, so you will also be expected to have mastered this skill manually.

Formulas never change.

What changes is how they are put into use, whether in a manual or digital setup.

You indeed will use programming skills and web designing to some extent not forgetting the domain language.

Therefore, without prior knowledge in these areas, starting a Masters in Data Science will not make much sense.


Just as Nielsen Global Survey of Education Aspirations revealed in their study, most people are pursuing higher education studies to get a better opportunity for job and salary advancement.

In that regard, you are not an exception from this.

You must have also ventured into data science in the first place due to the attractive employee’s benefits and incentives associated with these career paths.

Burtch works in his survey describes that Data Specialists are earning 14% more than the traditional predictive analytics managers.

It has even been rumored that some members of this cadre smile all the way to the bank with slips worth more than $200,000 from their employers.

Sounds interesting, right?

But how authentic is this information?

If you examine carefully, it will perplex you to know that this is an exaggeration of the ideal situation.

If you follow the survey that was conducted by PayScale, you will learn that all factors held constant, the average pay of a Data Scientist per year is $91,000 with the lowest paid personnel in this field earning at least $62,000 while the highest pocketing not more than $130,000.

Having done a master’s degree, you should be expecting a more handsome pay that the one stated above.

You will have lost a year or two in class, sacrificed your social system and the luxuries that abound to it just to pay tuition fee yet get awarded in a manner that is not satisfactory.

Furthermore, you shall not have earned income over those days you will absent yourself at work.

Before committing to take this course, you need to re-consider your motivator, and if it is plainly pegged on the financial benefits and incentives, you will have to consider a class that you will get value for your money.

It is also essential to evaluate the employer’s expected behavior. Once you graduate and get your academic papers in your hands, you are likely to re-negotiate higher terms of compensation.

Remember, most employers aim at reducing their expenses and maximizing the profit.

So, instead of adding you to the payroll, most will prefer splitting the roles you have played amongst IT experts, data miners, and statisticians who already exist who are not likely to request a pay-rise.

Consequently, you are likely to lose even the little chance to work in the company you have always desired.


Are you an unemployed bachelor graduate?

Then look for a job post first, and later you can apply for your masters. A career path is not cast on a stone.

While others study as their eyes are on the goal they hope to achieve, most have no clue where they will end up after they graduate.

Job search is not fun – you walk from job interview to job interview just to get rejections or no response at all.

Instead of this experience, some give up on the graduate job search and tend to substitute it for further education.

These are the graduates who fail to understand that failure to act on the existing problem only postpones the problem.

At the end of the study duration, they will still be jobless and rarely will the hiring manager want to gamble with an inexperienced candidate.

More so, these graduates believe that jumping into further studies will give them a better chance over those without a master’s degree.

And yes – somewhat this theory is correct, but it may not always work for you.

Most times work experience provides leverage too.

Now that you already have acquired those desirable traits and skills in your undergraduate studies, you need to put them to use or you will forget them.

Skills in technical fields such as computing and statistics if not practiced may be unlearnt.

If you sincerely have faced shortcomings securing a job, better start at an apprenticeship level. You can quickly get an opportunity to work as an intern or a volunteer where after you have gained perfection on the skills in questions, you shall be able to work independently and with efficacy.

As you know it is a plus to your experience: this may get you some job eventually.

As you work, you shall be able to recognize your weaknesses and strengths: areas you are good at shall be magnified; while those that require you to put more effort to achieve excellence shall be noted.

Networking with peers will be unavoidable.

Look at it this way: you will get a fair chance to meet people who have had more experience and training, who can nurture your skills and help you harness them.

These people may share not only their jokes but also the challenges they have had to face in the career path and how they conquered these challenges successfully. And who knows, this will help you solve similar problems that you may encounter in the future.

This exchange of ideas will help nurture and grow your experience. Free mentoring will come your way, and undoubtedly you shall be able to consult others as you practice your craft.

Do not rationalize the idea of going back to class after making only two or three job applications unsuccessfully.

Be keen to observe how employable you are and get the experience and mindset you need to enroll in studies at a post-graduate level but as a part-time student.

With this arrangement, you can study as well as earn a living concurrently.


To be fair enough, pursuing higher education can be such a life-fulfilling experience. One that will give you the sense of achievement and for some, it will massage their ego.

Nevertheless, it is not an experience you walk to blindly.

Below are competency areas you require to fine tune to achieve your dream of becoming a spectacular data specialist.

Technical skill set

Data analysis and statistical skills are a must for any data scientist – starting from simple manipulations such as the measures of central tendency and those of dispersion all the way to predictive statistics and analytics where you evaluate the odds and various probability ratios.

You need to be good at data collection, data processing, interpreting and even presenting the findings from the data.

Remember this is what shall be your selling point as the company wants you to be able to aid in making a sound judgment.

Do not stop at just manual calculations; it will help if you know how to do it electronically. You should be able to mint or even perform data logging using the various statistical concepts.

For experts, you would agree despite the difference in the names of the software and systems, they all can be traced back to a familiar root concept.

These tutorials are available online on websites such as YouTube, Udemy and others, find them. They are free.

It is advisable also to take an interest in database management systems.

If you have ever been into computing, you know the drill. Make arrangements to learn what you are not sure in – regardless if that is related to database management system or the querying languages such as MySQL. Do not confuse programming skills and data management skill; you need to be good at languages such as R and Python.

It is also of importance to beef up multivariable calculus and linear algebra. This will ensure you are sharp enough to make consistent yet right predictions.

Do not ignore the visualization tools for they are significant when it comes to data presentation.

You will also not go wrong if you make yourself familiar with Methods such as k-Nearest neighbors, random forest and other approaches used in machine learning.

This will be instrumental as you map data that is unprocessed and create another format of it that will be more useful.

Other skills

You are not there yet with possessing only technical skills as outlined above– not without other skills such as communication skills and even the ability to craft and (or) introduce new methods to gather, understand and process data into valuable information.

Seems easy but communication is an art, it is not complete if the intended message has not been received as it was designed.


Apart from attending data science boot camps that are available from time to time, you may consider making the Internet your friend. Not too fast though.

In this technological era, it is as easy to get misleading information from the search engines as it is to get authentic information. Make sure you can authenticate the information you hope to use.

Assess who has published and if it helps you can do a background check on the author. Be careful not to rush into indoctrinating yourself with information from wikis as most of them contain unverifiable information. You may also explore online libraries like HINARI.

Having assessed the above four reasons not to get that Masters in Data Science and the alternatives to it, you may be well placed to make an appropriate decision whether you still want to pursue a Master’s degree in Data Science.

Of course, if it is your dream course, and you are passionate about it: go for it!

4 Reasons Not To Get That Masters in Data Science

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