Frequently asked questions about data science


Harvard Business Review called data scientist the ‘sexiest job of 21st century’. A lot of my friends, who were graduating from different engineering colleges at that time, set it as a goal. Some of them are in fact, working in the field of data science. I have had the opportunity to talk to them about some of the popular questions, curiosities and confusions regarding the discipline of data science and the career path of a data scientist. I will reveal some of their inputs for you in this article. So, read on.

Q.1. How difficult is it to become a data scientist?

This is a very subjective question. It depends on whether you like digging into the data sets and start from scratch each time you hit a brick wall. Truth be told, you cannot really become a data scientist just by taking up a data science course. It is definitely a start but it is not all. We had all started slow in roles like data base management, data mining or reporting. Most of us are still hard put to call ourselves data scientist, because that will take more time. There is no beginner or entry level data scientist.

Q.2. Can someone become a data scientist by learning from free material from the internet?

We get this question a lot. Had it been only about learning some theoretical facts, we could well, do away with the institutes and learn through free material. But real time analytics industry is more than that. A lot stays at stake with very little chance of success and time is of the essence. The little expert advices that designated courses come with can be the difference between success and failure in this industry. So, it is better for you to make the investments if you are looking for real returns. Remember Malaysia is booming with opportunities for people with skill. Just start with a big data training in Malaysia and you will know the difference that a formal training under the supervision of industry experts can make.

Q.3. What is the difference between data analytics and data science?

This is an easy one and a hard one at the same time. These two terms refer to different disciplines yet are often used as interchangeable words. We can discuss both their similarities and dissimilarities.

Firstly data analytics is an umbrella term which does not really refer to a certain technology or job profile. It rather refers to an interdisciplinary subject that studies the various aspects of managing and analysing data. It concerns itself with the collection of data, its storage and filtering, its organization and so on.

Data science on the other hand is a different more niche oriented discipline which combines statistics and computer science to gain insight from the data. Data science is clearly dependent on the process of data analysis because the latter works as a preliminary and indispensable stage of the former.

Q.4. How difficult is it to become a data scientist?

If you have a background in computer science and have a knack for statistics data science would come as a natural development in your career path. It is quite useless to consider how difficult it would be. You should rather go on with the process and see where it takes you. But if you insist, I would say, yes, it is not an easy discipline to master because the learning curve is quite exhaustive and virtually endless.

Q.5. What tools should one learn?

As far as data science is concerned the most popular tools are R, Python, and SAS. Master these tools and then learn how to poke your nose into disciplines like machine learning and deep learning. Start slow, keep steady and keep learning; you will be through.