The Data Monthly Challenge
The Data Monthly Challenge is a monthly online challenge where beginners and advanced people in the field of data science are challenged to learn through solving one real-world business case step by step.
The goal is the participants to build up their knowledge and expertise helped by mentors, experts, and other like-minded data enthusiasts coming from all over the world. During the challenge, the participants will improve their knowledge by following their own pace and getting each week guidelines how to proceed the challenge. Thanks to the Society’s mentors, the participants will have guidance and feedback on what they can improve on the previous phase helping them to get the most of the challenge.
The most creative and precise solutions of the provided dataset for the challenge will be presented!
October’s Data Science challenge
In Sofia, Bulgaria, air pollution norms were exceeded 70 times in the heating period from October 2017 to March 2018, citizens’ initiative AirBG.info says. The day with the worst air pollution in Sofia was January 27, when the norm was exceeded six times over. Things got so out of control that even the European Court of Justice ruled against Bulgaria in a case brought by the European Commission against the country over its failure to implement measures to reduce air pollution. The two main reasons for the air pollution are believed to be solid fuel heating and motor vehicle traffic.
Experiment with data and find out how to predict air pollution within the next 24 hours through machine learning algorithms!
The case is a predictive analysis and it integrates knowledge from various sources. The idea is to predict the PM10 high peaks of concentration and forecast the pollution level in the capital of Sofia. Read the case details at http://bit.ly/2QFo5Le
The data consists of a spatial and temporal dataset including dependencies related to Meteorology, Topography, Transboundary pollution, National-level policies such as Household fuel subsidies and Vehicle standards and Local-level policies and behavior.
Of course, during the challenge, each participant can enrich the dataset to develop even more precise prediction model!
The goal is to build a successful prediction model on the expected air pollution to be measured within 24 hours.
How does it work?
The challenge has several phases of completion – in order to start solving the next phase, the participants would need to complete the previous one.
The Challenge begins on Tuesday, 16th of October at 19:00 (UTC +3:00) with a short explanation of the case and some introduction mentor’s guidelines at our YouTube channel. At the beginning of the challenge, the participants will have an initial boost for the first step they should complete.
Monday – a day for a Submission! Each Monday the participants should submit their solution to the specific stage of the challenge.
Tuesday – a Feedback day! There will be a peer-to-peer review where every participant is encouraged to give comments below the others’ article.
Wednesday – a Mentor’s Guidance day! The mentor will upload his/her approach for the solution of the case for the participants to go further if they are stuck on the specific stage of the challenge. Also, the mentor will provide some helpful resources and tips on how to approach the next phase of the challenge.
The Monthly Challenge will end on 20th of November with a presentation of the machine learning solutions to the air-pollution problem.
The Data Science Challenge is opened to the global community via our platform and everyone is welcome to participate either in a team or as an individual.
Motivational words from our mentors.
Check out this article made by our mentors, it’s there to motivate you and to give you a clearer idea about the project.
What is expected?
The monthly challenge is an interactive educational tool designed for (MSc- / PhD-) students in the field of Data Science and Business Analytics. It provides a complete walkthrough of the process adopted by professional analysts when delivering a data-driven solution.
Even though it is based on a real-world case study, the challenge is well-suited to beginners is the field as well. In particular, the work on the case is broken down into simple and clear steps supported by mentors’ instructions. Furthermore, every week we organize a discussion on the issues you have encountered while working. Yet, if you feel lost at a certain stage of your analysis, it’s OK, you could always ask the mentors for further hints.
Of course, it is a month-long hard work, therefore, we would advise the following:
- Work in teams of 3 to 6 people or individually.
- Use the Data.Chat for communication and questions
- Make a schedule of tasks with proper deadlines on weekly basis.
- Invest some time in getting deeper knowledge on the recommended techniques and tools. Put it differently, avoid application of code based on a theoretical framework that seems to be unfamiliar to you.
- Make regular discussions and catch-up meetings with the other members of your team.
- A Document clearly and on a timely basis every progress of your works.
Don’t forget that to provide better guidance, the virtual places are limited, so be fast – sign up before October, 15th and start the challenge now!