The wide scope of applied Data Science in almost all industries is mostly because of Machine Learning and Artificial Intelligence. Let’s go to the core of Data Science with Filip Nikolovski!
This topic always sparks a debate revolving around the impressive opportunities these technologies offer, as well as their diversity and infinite potential.
But, we rarely talk about what lies beneath the surface — what is the core of this complexity called Data Science?
To discuss these topics, we interviewed Filip Nikolovski — a Maths and Statistics professor with 10+ years of teaching experience. Filip’s an instructor on our Data Science Bootcamp as well, specializing in Module statistics.
Brainster: Filip, you’ve completed the stats & maths module with several groups already. What’s your experience with beginners? Are you satisfied with their progress, taking into account that this Module is one of the most intensive and complex ones on this Bootcamp?
Filip: I agree with the complexity and intensity of this module. What we cover in those few weeks, is usually learned over the course of months. After having a couple of successful groups, I can claim that the students and I handled this intensity well!
At first, I had my doubts about adult students going back to maths after spending years without practising it. Let alone depending on it for their progress on this Bootcamp.
The first moment when I realized I was wrong in doubting the students’ motivation, I was instantly relieved.
I especially enjoyed the working atmosphere and the ‘chemistry’ between colleagues. It inspired me to be more creative myself.
Brainster: After completing your module, the students have moved on to learning Python and Machine Learning. How will stats & maths help them in overcoming the challenges in Machine Learning?
Filip: Actually, ML is based on statistics. Taking this into account, I can say that statistics is imperative for this field. If I have to choose, I’d say — the way of thinking and the ability to synthesize research results.
As opposed to maths, the research results in statistics come with a level of probability, meaning that they are not absolute.
This means that, for example, if there is the slightest probability that something will happen, observing it long enough will make sure that it happens — unless the observer has some sort of bad luck!
The admissions for the next batch of students on the Data Science Bootcamp are open. Save your spot now and begin our online prep programme.
Brainster: When talking about Data Science, the main buzzwords always seem to be Machine Learning, Python, AI, and Business Intelligence. Statistics are more often lurking in the shadows. That is why Data Science is more often related to coding than to the analysis of statistical models. How to break this loop and avoid misconceptions?
Filip: I believe that the core cause for these misconceptions is the nature of the above-mentioned terms — they belong to the applicable part of Data Science. Statistics lie in the theoretical part of the field.
In my opinion, if we give statistics the attention it needs informal education, we’d catch up with the global trends and avoid this misunderstanding.
Brainster: This is going to be a follow-up to my previous question. The experts say that Data Science in its core resembles ‘glorified statistics’. This myth makes statistics practitioners a bit afraid to start learning Python and coding. What would your advice be?
Filip: I can see where that comes from and we definitely can’t take pursuing a career in Data Science lightly. But, everyone with a strong knowledge base in maths and/or statistics, can easily figure coding out, mainly because of two things: the analytical mind, and the habit of ‘working with limitations’. In maths, those are axioms and theorems, and in coding, they are the structure and the syntax of the programming language. I’d add that Python is by far more intuitive when compared to other languages I’ve worked with.
Brainster: Statistics has always been important but has recently skyrocketed due to the Development of Data Science. Data Science as a concept emerged in 2001, but ‘exploded’ in 2010. What happened with Data Science all those years? Why did the world need so much time to realize that many important answers lie in data?
Filip: I’d say that professionals have always been aware of the opportunity to extract information from a data set. In my opinion, this statistical ‘revolution’ is since collecting data is now easier than ever before. In the past, we didn’t have as many opportunities to collect and analyze data as today. We didn’t have an idea about what to do with the data as well. Taking all of this into account, I’d sum up that the course of development is pretty clear.
Brainster: Filip, you are a professional statistician. Do you use statistics for other parts of your life other than work i.e decision making, personal finance, etc?
Filip: To be 100% honest, other than some elementary ‘analytics’, I don’t use it in everyday life. Nowadays, we don’t even have to put in the slightest effort in doing our taxes — that pleasure has been taken away from me 🙂 Maybe if I had more data about myself…
Brainster: What are some Data Science highlights for 2020 that caught your eye?
Filip: One of my top highlights would be that a new antibiotic has been discovered with the help of AI. I hope we see more of this.
Brainster: In your opinion, what is crucial to complete quality training in Data Science?
Filip: I’d say that it is imperative to have a balance between theory and practice. Regardless of the professional background of students — everyone that is at the beginning of learning Data Science should have the basic knowledge of applying methods and algorithms, but the theoretical correlation behind them as well.
Brainster: Please send a message from you to all future Data Science students on our Bootcamps, and everyone else that is considering starting a career in Data Science?
Filip: It is not going to be easy, but it definitely is going to be fun! The Bootcamp enables students to learn, get a sense of teamwork, participate in discussions, and execute practical tasks. Just like a simulation for a career in Data Science!
If you have ever thought about a future-proof career in Data Science, have a look at our remote Bootcamps.