Monthly Challenge – Learning-by-doing in Data Science

The theory is not enough

Academic education is indeed the best long-term investment in our professional and personal achievements. However, nowadays it becomes crucial for universities to include different practical seminars in their educational programs with the aim of preparing students for the real problems which they will be solving as professionals in a given domain. The “learning-by-doing” concept is becoming more and more accentuated in academic education and is proving to be a successful strategy. This idea is of huge importance especially in the Data Science field since only theoretical knowledge is far from enough to become a real professional. Data science is an area that requires a combination of many skills – excellent analytical capabilities, business knowledge, technical skills, good communication skills, ability to communicate clearly your ideas and of course – creativity. ☺

How to obtain crucial Data Science skills while in University?

The question then arises: how to obtain all these skills and take a step towards becoming an expert in the field while you are still in university? The answer lies precisely in good academic education which involves various practical tasks provided directly from the business. This is what the Data Science Society and the Faculty of Economics and Business Administration (FEBA) at Sofia University are trying to achieve through their various collaborations, aiming at the introduction of interesting data science problems to students at hackathons and turning good theoretical knowledge into action using modern e-learning methods.

Environmental Issue to be solved with the method learn by doing

The levels of air pollution caused by solid fuel heating and motor vehicle traffic are an evergreen problem for the cities. An important step to better understand the factors influencing air pollution is the prediction. This will help to take actions to reduce its effects and to prevent respiratory problems.

The Monthly challenge is yet another joint activity of the Data Science Society and FEBA. During the challenge, participants are given one month to prepare a comprehensive solution to a real data science problem with an environmental cause – fighting air pollution. Students and beginners in the area of data science work in teams of 3 to 6 people or as individuals and this helps them to develop their teamwork capabilities and simulates a real-working environment.

The challenge is open for everyone who wants to learn and improve in the field. Something interesting is that the Monthly Challenge takes place also at a university program as part of an innovative seminar in the Master’s program in Business Analytics at FEBA. The main aim of the seminar is to give students the opportunity to ‘learn by doing’ several key aspects of the Data Science field through a series of business cases. Some of these aspects are:

  • What problems face the business?
  • Which business needs are perceived as crucial and which are considered as not so important?
  • How the data really looks like before being processed and turned into an appropriate format for further analysis?

What they don’t teach you in the Data Science textbooks?

In textbook examples, beginners in the area often work with clear data, but in reality, this is often not the case. It is important for future data scientists to be aware that much of their time will be spent in pre-processing of data using various techniques.

There is no right or wrong answer. Data Science is a field that, among other things, should also arouse your creativity. You shouldn’t think that there exists only one correct decision. Most often it is enough to have plenty of ideas which will guide you in finding the answer to your research problem.
Well, finding that answer often leads to the emergence of a new question. Data can tell us a lot more than the business thinks it knows. Often the problem may not be properly defined and the responsibility to specify it will be entirely yours.

Learn by doing at the Data Science Monthly Challenge

The Monthly challenge organized by the Data Science Society is a wonderful opportunity for students to get familiar with these important aspects and develop their practical skills in a variety of ways. During the challenge, there are both academic mentors and business mentors who will guide the participants and provide them with meaningful feedback about their analyses.

The challenge is set up in such a way that teams and individuals will have the chance to deepen their understanding and pay attention to all details part of each stage in the development of high-quality data science projects. Also, participants will present their results at each stage of the challenge which will not only help them to proceed successfully into next stages of the analysis but will also create to a reasonable extent a competitive environment – there will be a peer to peer review each week and participants will be able to compare their performance with the rest of the teams.

Data Science Monthly Challenge

FEBA and the Data Science Society harness the power of modern e-learning methods to bring together the academic and business world and develop more skillful young professionals in the data science field in Bulgaria.

The October edition will challenge participants to build a successful prediction model on the expected air pollution and forecast the pollution level in the capital of Sofia. Well, this is not at all only a business case – this problem concerns Bulgarian government and our society as a whole! So, it seems that the October edition of the monthly challenge will bring up some interesting solutions from the analytics world to vital social problems!

The Monthly challenge begins on 16th October and ends on 20th November. Of course, the challenge is also open to international participants, so do not waste your time and register until October 16, 2018! ☺

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