Area(s) of Interest.

Various individuals pursue M.Sc. course for a variety of reasons which range from a demand for a higher pay package in the company they are in, real curiosity for science to being in a position to demand a slightly higher dowry; To each his own.

But there should be some reason why a person chooses a data science specialisation, from the start, over a generic computer science masters course. So what is it? As a student, you need to know why you want to do a M.Sc. with a specific specialisation.

I’ve come across a few cases, 4 of which I would like to specify below:

Case 1: While working, you have come across a problem which can be solved using data science method. You want to know more about it and thus, decided to do a M.Sc. course.

Case 2: A person wants to climb a couple of positions up the hierarchy in the domain of business s/he is in. S/he is already working on topics related to machine learning, deep learning, data mining and thus wants to do an M.Sc. course now.

Case 3: A person wants to climb a couple of positions up the hierarchy in the domain of business s/he is in and wants to get into data science.

Case 4: A person is a fresh graduate from the bachelor course and his/her parents want him/her to do a M.Sc. course.  

If you are in case 1, that’s incredible. Just understand what problem you want to solve. That’s the best case scenario in my opinion.

People falling in case 3, 4 are the most delicate ones.

If you are trying to get introduced to “data science”, most probably you’ve been introduced to “data science” by some magazine article online or offline. You need to sit down and figure a couple of things.

Question 1: What in “Data Science” fascinates you? How can you apply it in the things you love doing the most? Example:

  1. If you love sports, there’s sports analytics that you might be interested in.
  2. If you love marketing, there’s marketing analytics and/or Marketing science, methods using which you predict the growth of your business.
  3. If you are interested in photography, how is artificial intelligence changing the digital photography scene? What makes the system more intelligent? There are billions of photographs up on the internet, how can we make better use of them? Check this link out.

Your area of interest is just one Google search away.  

Question 2: Are you interested in the “science” of data science? Or are you confusing it with engineering? If job is your only motif, then you might want to know about the institutes of applied sciences (fachhochschulen) and not a general university like Otto von Guericke University, Magdeburg.  

Question 3: How are you sure of the things you love doing the most? How much time have you spent doing it? And could you keep doing them for a substantial period of your life, with love, without getting tired or cribbing about it?

It is perfectly okay if you do not have a perfect or precise answer to the questions above. The purpose of introducing the questions to you, is to make you think about it. The more you think and the more precise you get at your answers, the better it is for you.

While the first set of questions make you understand in which direction you want to go, the next set of questions would make it slightly complex. Nevertheless, you should be aware of the questions. As you start with the course, things will eventually be clear.  

What would you find more interest in? If it is processing the data more efficiently, then you’re more inclined towards Data Engineering. Knowledge Engineering deals with methods employed to learn from the huge chunks of data. It involves data mining and machine learning. Then there are knowledge representations and visualisation. They just are broad categories that fall within Data and Knowledge Engineering, M.Sc. course (ref).

I’m not sure why you’ll still reading this long article. But since you are let me give you a couple of more things to explore. The Facebook Research areas should excite you, and it shows the areas in which Facebook is currently researching. This is the link to Google AI research teams and this link gives you an insight to what they are currently working on. Beyond this, it would be too overwhelming for you and thus, I draw an end to this post.

Send me questions by clicking here.