Running a Remote Data Science Study Group

Earlier this year, a few friends and I have started a remote Data Science study group. Since then, we’ve met once a week to talk about Data Science, Machine Learning, and Python. Our aim is to get better, together. In this article, I want to share how we’ve set up the group and what has been working for us so far.

Why?

There are a plethora of reasons why running a remote study group for any topic is a good idea. Here’s what motivated me.

  • Reach personal goals. Improving and practicing my Data Science and Machine Learning skills outside of work has been part of the goals I’ve set for myself at the beginning of 2020.
  • Healthy peer pressure. Social pressure works, at least for me: I know that I would have a hard time sticking to a weekly cadence of studying on my own, but if a peer group holds me accountable to at least show up, I would always try to have something to show for it.
  • Share knowledge. If you find a group of motivated people, they will bring different experiences and questions to the round. This leads to healthy discussions and skill sharing.
  • Study from home. Remote study groups are very compatible with a pandemic lifestyle.

Find a group and make it easy to commit for everyone

To get started and get others on board, I made two choices to reduce the initial friction of getting things running:

  • Set the initial topic. “I am going to read the following book on Data Science in the next few weeks. It’s available as a free PDF. Do you want to join me?”
  • Reasonable commitment. We’ll meet once a week for a video call of 1 hour. It won’t ever take longer.

This was easy to say “Yes” to and three friends immediately joined me.

Read a book together and discuss notes afterwards.

Start simple: Read a book together

The first few weeks, we’ve read a book together. The goal was to start broad with a high-level overview. Our first book was Steven Skiena’s The Data Science Design Manual, which is available for free from Springer.

The book lends itself well for this purpose because it goes over central Data Science topics at a conceptual level. In some chapters, Skiena dives into algorithms, but not too deep. As an overview to get our group started, it was a good choice. Moving forward, most of us agreed to pick a book that has more in-depth explanations and code examples to encourage trying things out.

We’ve read 1-2 chapters per week, depending on their length and complexity. In our weekly discussion, we went through our notes and shared in turn, asking: “What’s one thing you’ve learned from this chapter?” These discussions easily filled 60 minutes and I think it never got boring.

Intensified Learning: Code together

It’s hard to argue that trying things out yourself will lead to deeper understanding, so we’ve tried from the start to incorporate that. While reading, we would experiment with one of the mentioned algorithms or look at a dataset linked from one of the chapters. Having finished the book, we continued that practice: Everyone picked a personal data project to work on, and we updated each other once a week. These data projects were all motivated by challenges available on Kaggle, and we had good fun toying around with them.

When you simply want to read a book, but are too used to agile practices. Voila, a book burnup.

Learning with Pandas

We’ve recently moved on to a new book: Wes McKinney’s Python for Data Analysis. As the Pandas library is the de-facto standard for data handling in Python, a book by the author of Pandas seemed like the right choice. In the group, we have different levels of experience with Pandas, but revisiting the foundation and strengthening the practical skills were favored by all of us. As this book is heavy on code examples, we hope to get a good balance of reading and coding in as we move along.

Moving forward

Reading a book, working on mini-projects, starting with the second book. It almost feels like we’ve entered “Season 3” of our little Data Science journey now. So far, I’ve learned a lot as an individual, and I think as a group we are motivated to keep going, probably experimenting with the format in the future.

The experience of launching a remote study group has been great so far. If you have a topic you want to explore more thoroughly, take this as an encouragement: In the age of video calls and free online resources on every topic imaginable, collaborative learning has become as easy as never before.

Attending a Remote Hackathon

2020 is the year of doing things remotely. It was therefore my home home office and a healthy internet connection that provided the space to participate in the AI for Good hackathon last weekend, organized by Deep Berlin. The task description was broad, but it pointed the teams to work on something related to climate change, specifically the occurrence of wildfires.

As a team of four, we spend the weekend looking at the relation of human activity and wildfires. We focused on data about touristic activity in Northern Spain, an area that has seen intense wildfire seasons in the past.

Final presentation video

(Excuse the nervous beginning, anyone who has attended a hackathon before will be familiar with the last minute push, in this case submitting a final video to the hackathon organizers on time.)

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Some notes

A few thoughts on what we did and what I took away from the weekeend.

Pandas and scikit-learn

In my day job, I mostly work with Python, and am familiar with deep learning libraries like PyTorch and Tensorflow/Keras. The hackathon was a welcome opportunity to do some hands-on Data Science work again, and I enjoyed using Pandas and scikit-learn for quick data analysis and plotting. What a nice ecosystem.

Free location data

Open street map is an amazing community project providing labeled location data from all around the globe. Open Street Map location data is provided in the osm format. To read these files in Python, we used the osmium package. Reading the file and filtering the nodes for our usecase was straight forward, but loading from that format can take surprisingly long.

Free geo data

Once you start looking, you discover some interesting datasets out there which are freely available. We used the MOD14A1 dataset, which provides satellite data of very recent recordings (up to a few days from today), with access to multiple levels of abstraction in the data format.

Pretty maps in folium

Our team member Markus spend some time creating pleasing visualisations of maps in folium.

A map of north-western Spain with two data distributions shown. The yellow/red clusters show the locations of wildfires in the past 10 years. The blue outlined areas show locations of high touristic activity. Clearly visible is the Camino de Santiago which extends all the way through Galicia. Map rendered using folium.

Code and resources

Our results were interesting, but anecdotal. You can find the collective resources from our team on Github. https://github.com/florianletsch/fire-tourism

How does a remote hackathon feel?

What I valued during that weekend was being in my default work environment. Our team quickly developed a working rhythm where we would have a video call for 30 minutes and then disconnect and spend some focused 2-3 hours by our own. I’ve never experienced such a focused working environment at an on-site hackathon.

Obviously, the main shortcoming of being remote was not having the chance to talk to people outside your team, or just bump into someone. Also, there was no way of passively observing what everyone is up to. From what gathered on Slack, many teams actually didn’t constitute properly, and then some lost participants tried to get into other teams, but it wasn’t as easy for them, as it might have been in person.

Would I join a remote hackathon again? Yes, to really get something done on 2 days. To actively socialize, it isn’t the right thing for me, though.