Data Science for Social Good Summer Fellowship
By: Prakhar Rathi
Tags: Time Series Forecasting, Jobs, Internships, Social Good, Data Science

Work Experience

This blog would be remiss without me talking about my experience during the fellowship. The program commenced on 7th June with 16 fellows coming from a variety of backgrounds and many different countries. This was one of the best things about the fellowship. I was able to meet and become friends with people across the globe. The fellows were divided into teams of 4, working on 4 different projects. My team comprised of people from Israel, Taiwan and India and we were working with the German Federal Ministry for Economic Affairs and Energy (BMWi). They represent the main governmental body in Germany for economic affairs and economic policy. Our project partners wanted to come up with a solution to forecast unemployment rate at the county level in Germany. You can read about the detailed problem statement and the solution here. I wanted to talk more about the experience and workflow in this section rather than the problem statement.

Data Science Workflow

A lot of people might think that because universities and research institutes are involved, hence, the program would be a bit unstructured. However, that is far from true. As I mentioned earlier, we work with people from industry and academia. Each team was assigned project managers and technical mentors. Our project manager made sure that we followed an agile workflow throughout the project which means we followed all the standard practices that you would see at any tech company. Every Monday we would have ‘sprint planning’ to discuss and finalize the things we would do over the week and allocate story points to the tasks. It was highly organized with each member being aware of what they would be working on. Apart from that, we met every morning (actually afternoon for me because we were working during UK work hours) to update on the previous day’s progress and the plan for that day. This allowed us to be accountable to the team and also discuss any blockers that we might be facing. Every member in my team was super helpful which meant that if I was ever stuck on a problem, all I had to do was ask for help and someone or the other would work with me to solve the problem. If we were still stuck, we could reach out to the technical mentors or discuss them with the partners or in the Friday Deep Dives (I will come to these soon). There were also weekly roundups called ‘retrospectives’ on Fridays, where we would assess our weekly progress and objectively evaluate the sprint progress. This gave us insights into what went well and what could be done better during the next sprint.

So, anyone who might be thinking that this is a research internship and you wouldn’t get the industry experience, you’d be surprised by how similar our work was to a tech company.

Working with project partners

However, it wasn’t exactly like a tech company because of the focus on the social good aspect. DSSGx aims to work on social good problems by collaborating with governments and NGOs, both of which have a direct positive impact on people’s lives. Like I mentioned, our project partner was the BMWi. Now the reason I stress on the word “partner” is because of how we worked together. The organization wanted to solve a problem along with us and did not want us to solve a problem for them. This, I believe, is an important distinction with working for a company where the clients just give you a problem and expect you to come up with a presentation to solve everything for them (speaking from experience, might not be completely generalizable but it is pretty common). We met with our partners every week to discuss our progress and explain our solution (or parts of it) to them. On the basis of the weekly discussions, we would plan the next week’s work and then present our findings again. This way we had constant feedback on our work and I really appreciated the time they took out to work with us on the problem. I feel that this was an integral aspect of the success of our solution.

Since DSSG believes in research reproducibility and open science, our solution is publicly available here. Another positive aspect of our project with BMWi was that they experienced the impact that data science can have on their organization and are open to exploring more data-driven solutions to their other existing problems. This means that there would be a higher standard of database maintenance and application of data science methodology in this organization. I would call that a resounding success!

Public Lectures and Deep Dives

DSSGx was committed to our learning as well, which went beyond the projects that we were working on. This was achieved through the public lectures every Friday. These were talks given by eminent professors, researchers and industry stalwarts who had a lot of experience in data science. These talks served as a great means to break-away from the daily work and learn more about current challenges faced by the data science community. I learned so much through each of these talks and also interacted with the speakers, which would not have been possible otherwise! A list of public lectures which we attended can be found here.

After the public lectures, we had the most awaited event of the week — Deep Dives. This was an opportunity for all the teams to talk about their weekly progress and plans for the next week. I loved these sessions because we could ask any and all questions we had from professors, technical mentors and visiting guests. Since everyone came from different backgrounds, we almost always got multiple ways of solving a particular problem that we were facing. This was truly very helpful. Additionally, we were able to interact with the other fellows, know about their project and experiences which was very interesting.

Overall, it was a day of learning and collaborative problem solving. It did get hectic at times, because of multiple sessions one after the other but it was a great learning experience and we were able to make a lot of progress because of these sessions.

Socials

The public lectures and deep dives weren’t the only thing that happened on Friday though! After a week’s hard work, all the fellows relaxed and engaged in social activities. These were organized once a week by one of the teams and was a great way to talk to fellows outside of my team and get to know them better. I loved all these sessions!

Gartic Phone Logo

We played many different games (whatever was possible online) including Skribbl, cryptic hunt, Olympic themed quizzes, group activities and so on. My personal favorite was Gartic Phone!

Friday was definitely my favorite day! It was filled with learning and fun, and it definitely made me look forward to mondays so that we could start working again. I don’t think I had ever looked forward to mondays before the fellowship.

Teamwork

My fellowship experience wouldn’t even be 1/10th complete if I didn’t talk about my amazing team! I could talk for hours about what a great experience I had with them but let me first introduce them:

Amit Sasson - Masters graduate in Statistics from Tel Aviv University, Cinny Lin - Data Science, NYU ’22, Vighnesh N Ganesh - junior at BITS Pilani, India majoring in computer science engineering, and finally myself, Prakhar - Computer Science, SNU’22.

BMWi team at DSSGx 2020

A good team is the most essential aspect of a successful project and I had the privilege of working with some of the best people. Our very diverse backgrounds proved to be one of the major factors in the success of our project. Our team had people who had a vast knowledge of machine learning algorithms, people who could write complicated pipelines like it was nothing, people who built the tool which would be used by BMWi and people who could explain even the most complicated results through effective visualization. This, combined with the fact that each of them was always ready to help out one another, made us work together very productively. I can confidently call this one of my best work experiences with some of the best team members.

Unicorn by Team BMWi which was adjudged the best unicorn!

An example of our team work can be seen through a unicorn drawing competition which was conducted by one of the technical mentors as a fun team-building exercise. We had to make a unicorn by coloring in the boxes of a google spreadsheet in limited time. It was no Van Gogh but we were still proud of it. Our unicorn was declared the best unicorn by the organizers!

While we worked really hard, it wasn’t the only thing we did. I think we were definitely the most chatty team as well. We were always willing to learn about each other’s culture, language and academic background. I know so much more about Israel, Taiwan and south of India that I could probably win a trivia competition.

Like I said, I could talk for hours about how much fun we had as a team but the blog is already too long but I am glad that you have read so far.

Conclusion

This summer has been an enlightening journey for me. I grew as a data scientist and as a person, all thanks to the fellowship. I have picked up a lot of new skills over these last months which I will be able to apply to other projects. I was also able to connect with academics and people in the industry which was also one of my goals.

I recommend this fellowship to budding data scientists and academics who’re looking to apply their skills to problems that matter and learn a lot in the process.

Photo by Franki Chamaki on Unsplash

Acknowledgement

This article could not be completed without the helpful comments from Cinny Lin, one of my team members from the fellowship. She also writes about Data Science and Technology. Check out her blog here! I also received really helpful comments from Prof. Jürgen Branke and Prof. Colm Connaughton at the University of Warwick, who also worked with us closely during the fellowship. My teammates Amit and Vighnesh also provided really helpful comments.