As you know the Data Science Monthly Challenge starts on the 16th of October so here are
9 steps to help you progress during the Monthly challenge
1. The Challenge begins on Tuesday, 16th of October at 19:00 (UTC +3:00) with a short explanation of the case and introduction mentor’s guidelines.
2. To not miss the mentors’ guidelines we encourage you to sign up for our YouTube channel to get a notification when the Challenge’s Opening starts: https://goo.gl/iuiM1q!
3. At the beginning of the challenge, the participants will have an initial boost for the first step they should complete which will be uploaded in an article when the challenge begins.
4. You have to create your article by Friday, 19th October.
Don’t worry – it’s only a creation of the article without a need to include your progress yet – you will have to submit your solution to the first step by Monday, 22th October.
Watch how to create your article and a group channel in the Data.Chat (if you are participating with a team).
5. Your article title should look like this: “Monthly Challenge – Sofia Air – Solution – [your unique team name]”.
When creating your article you should fill the fields including your case name, teammates (if any or solo if you are participating as an individual), the mentors to this case (boryana, ekaterinamarina, paspaldzhiev, junior ), your location, tags, and a category. Here is an example:
6. Each Monday is a Submission day! The participants should submit their solution to the specific stage of the challenge in their article until 11:59 PM (UTC +3:00) .*
7. Each Tuesday will be a Feedback day! It is important to note that this is a self-learning process guided by experts in the field. The review/evaluation of the work is Peer-to-Peer. This means that participants will evaluate other participants projects by giving comments below the others’ articles.
8. Each Wednesday is 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.
9. The Monthly Challenge will end on 20th of November with a presentation of the best machine learning solutions to the air-pollution problem.
The Communication:
We are using a Data.Chat for the communication and the channel dedicated to this case is #case_sofia_air_sept_2018. You are strongly advised to use the channel to ask questions and share useful tips from your learning journey. If you have any problems, please use the channel #issues. You are welcome to explore and join also the rest of the interesting channels on the Data.Chat! 🙂
And don’t forget that the Data Science Challenge is opened to the global community and everyone is welcome to participate, either in a team or as an individual. So if you have a team, invite them to join and learn together!
Some remarks:
Here are our general technical remarks towards all teams, participating in the Monthly challenge:
1) Include your code in the article as a selectable text (NOT AS IMAGES, NOT AS PDF, NOT AS GITHUB REPOSITORY). We have built a great feature on our platform which reads and renders directly Jupyter Notebooks for that particular use. If you have not used jupyter notebooks before – it is about time to start – you are not going to be data scientist without this skill. And also it takes like several clicks to install, run, and convert your current code to notebook. The least you could do is just paste your code as text (maybe in a quoted box).
2) We strongly discourage you from putting part of all of your solutions on some URL outside of the data science society platform. All your files, visualizations, codes (see above), etc. SHOULD BE INCLUDED IN THE ARTICLE. Everything outside of our website WILL BE DISREGARDED BY OUR MENTORS.
3) Contrary to some beliefs the mentors for the case are not robots, so PLEASE DO NOT POST ONLY CODE, without any argumentation. Without explanations, this is not science, it is homework for your 3rd grade teacher. We want to understand your ideas and your thought process.
4) On the other hand, DO NOT PUT ONLY TEXTUAL EXPLANATIONS, on various topics surrounding air pollution. We in data science deal with facts. Keep the blurry texts for Gender studies class.
5) Judging by some titles of your articles, it seems like most of you believe that you are going to write one separate article for every week – BUT THIS IS WRONG PERCEPTION. Your article, once started, will grow from now on in every week. This is the only place to write your solution for the Monthly challenge. And yes – you will need to tidy it up so that it is readable.
6) Be serious about participating in the monthly challenge – if you start an article, start building up your solution. We need to count the serious participating teams, and articles with lone content such as “DATA! DATA! DATA!” are not helping at all. RESPECT THE WORK OF YOUR PEERS AND THE MENTORS. Also do not use an article as a chat room – we have the Data.Chat for that.
7) ALWAYS KEEP YOUR ARTICLE: status: published, category: prediction, article type: solution, event: monthly challenge. Or else nobody would read it.
8) BE ON TIME! Follow the schedules. And if you are in different time zone, please account for that as well. If you want to participate fully in the monthly challenge, learn new things, get feedback from our mentors and your peers, you should do your obligations on time. For example the teams which failed to register by Friday deadline will not be included in the weekly peer review.
9) Finally – a reminder. Your PEER REVIEW assignments for every week, are given to you as a comment below your articles. You have to do them on Tuesday in the hours before the next week challenge.