Training future Data Scientists - Part 5: To lead, embrace conflict

Keep your nose out the sky, keep your heart to God And keep your face to the risin' sun - Kanye West, Family Business

In the previous posts I have covered a number of items, the last being particularly focused on growth. In this post I would like to focus on what it means to grow with others. Specifically looking at how we work in the organisation and leadership of DSIDE. These lessons continue to spill over even into other work I do with the Deep Learning Indaba.

The organisation and leadership of DSIDE is basically made up of 4 core members 2 from each of the units MDS and Meraka. Given where we are all situated, physically and philosophically, the way we think of what the program means may sometimes not be the same. This eventually always rears its head at different times and with different effects. Just to go into more nitty gritty. The management of the program is not only shaping how the program looks like, which I covered in the 2nd post, but also many practical considerations like budgeting, contract management, logistics etc. It is a lot. Issa lot.

One of the earliest changes we advocated for at MDS was moving away from only targeting 3rd and 4th year students. We had a belief that targeting MSc and PhD students would increase the level of engagement and also strengthen some of the modelling work we were trying to get to. There is an appreciation that there is no right or wrong way but we ultimately pushed for this to allow MSc students and PhD students to also have an opportunity  be part of DSIDE and today believe the program is better for it (MSc and PhD students are a minority of applicants and participants). The magic happens when you have all these different levels of students in the same lab working together. A similar stretch has started to happen (with a suggestion from our other organisers) on opening up a path for more business/entrepreneurship oriented students.

The largest challenge thus far has been working to sync up on what we want out of the program. A stretch I pushed for early on was the de-emphasising of creation of web apps. What we had noted was that students would focus so much energy on getting a web app to work instead of really understanding much of the analysis/modelling task in front of them. As such the likely impact of DSIDE program would be inhibited by a large focus on working out kinks in a web framework. It is understandable on the side of partners that they may be looking for a nice demo to show off what the solution is, but I tended to see it as being a distraction. Ultimately, in a full team one would have access to front end developers who would be much better suited in building these things. The 2017/2018 year saw us heavily emphasise understanding of problem and modelling as be biggest outputs to the partners for the teams. Further outputs were further grounded to allow for easier communication. In the last year we now had outputs expected being, in decreasing importance:

  • Code (commented and documented)
  • A report/paper draft
  • A poster (Thanks to our visit to DSSG Portugal :D)
  • Presentation

So why title the post with the word conflict. These changes small or large sometimes lead to conflict in the organising team. I could try to paper over this and ignore it, but it is a reality that has to be shared and also we should learn that we have to embrace constructive criticism and growth. Embrace the conflict and use it to also question how you do things. It is not about always being right, but having an understanding of where people thoughts come from and how to communicate your thoughts.

I tend to use the feedback from students as a measuring stick on how well we are doing and adjust some of our programs accordingly. The change to focus on modelling and insights was informed a lot from students frustration with frameworks and lack of time in modelling and being able to interact with a partner. Our partner requirements now are a bit stricter and we are very thankful to partners who showed up weekly to interact with students and get a much more agile process as a side effect.

This is fifth in a series of blog posts covering the 2017/2018 DSIDE program. We are now almost done, 2 more to go.

More from the series

  1. What's in a DSIDE season?
  2. Preplanning
  3. Breaking down the process
  4. Growth - it's a process
  5. To lead, embrace conflict
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Training future Data Scientists - Part 4: Growth - it's a process

Sun don't shine in the shade - Kanye West, Waves

Growth is not only for the students, but for the program itself and all those involved. In 2017, growth was definitely on the cards for all involved. It was a time to stretch our methods, our relationships and our processes. Let’s take a look at some of the stretches we made in 2017.

Deep Dives

The CSIR is a research organisation. We have many researchers around the CSIR Modelling and Digital Science building. To tap into this talent during the DSIDE program, we work to have constant opportunities for collisions between student teams and researchers. One of these are Friday project progress presentations. These in the past were called Shoe* and Tell (The show was misspelled and we just kept it that way).

We tried to fit in 6 project presentations in 60 minutes. This gave the teams an opportunity to present their work and ideas to other researchers and developers at MDS, while at the same time exposing the rest of MDS to what the DSIDE and Data Science teams were working on. The challenge we quickly identified was that the time was too little (7 minutes for each team with Q&A). In 2017, we altered this model. We introduced both shorter and longer formats of the presentations. Read more ›

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Training future Data Scientists - Part 3: Breaking down the process

Nothing's ever promised tomorrow today, But we'll a find a way - Kanye West, Heard 'Em Say

In this post I want to break down how we tackle the challenges that are chosen for the DSIDE program. As such lets break down the process.

How we look at problems

When we decide on the final challenge with a partner, we have some question that needs to be answered, some data to be delved into and background that the project team will have to get.  We now work with partners to define their project. First the potential partners fill in the web form with information needed to start scoping a project. We then follow up with promising project partners with a scoping sheet. We currently send a scoping sheet inspired by the DSSG program. Once all of this is done, we can then get to tackling the problem. Read more ›

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Training future Data Scientists - Part 2: Preplanning

Reach for the stars so if you fall you land on a cloud - Kanye West, Homecoming

Before the students step on to the CSIR campus, a lot of preparation happens. How much preparation? A lot! Let’s talk about preplanning. We are going to break it down into program design, finding partners & problems, and recruiting students.

Program Design and Preplanning

So how many people does it take to run a program with 50 students showing up every season? A lot. We have 4 core program leads at the moment. Nyalleng Moorosi, Dr. Quentin Williams, Dhiren Seetharam and myself. We work together on the design of the program, coordination and organisation of all  other parts. The program design is always a work in progress. Our goal is to be able to reach the goal of the program of providing a rigorous  training program that delivers value for our partners. As such we have set expectations on”

  • what happens on both parts of the season,
  • what a day should typically be like,
  • what happens during a typical week,
  • when deliverables are due,
  • What is a deliverable?
  • When we start
  • What workshops will be available
  • Evaluation
  • Ambitions to get better.
  • Which other non-curricular enhancements do we add to the the schedule?

Thus in this pre planning phase, we discuss the philosophies we all might have and what changes we might introduce in the new season. This is a collaboration that stretches all of us and pushes us to think of the impact our own decision make on the program. Our ambitions on each season have to be high, and we are coignascant that this also means more pressure on the rest of the participants. To reach our goals, we work with other CSIR staff for recruitment, CSIR researchers for project leads, mentors who oversee a single project etc. Read more ›

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Training future Data Scientists - Part 1: What's in a DSIDE season?

Our work is never over - Kanye West, Stronger

We just finished another season of the Data Science for Insight and Decision Enablement (DSIDE). DSIDE is a Data Science training program that recruits 50 undergraduates (3rd & 4th years) and MSc/PhD students to come to the CSIR and tackle some of South Africa's challenges using a Data Science approach. The students spend 3 months at the CSIR, broken up into 1 month in the winter and 2 months in the summer. The program has been running since 2014 and the Department of Science and Technology is the main sponsor. You can find out more about the program on the program website. Now that the formalities are done, I wanted to look back at the just finished season and highlight some of the changes, successes and failures. Running such programs is very interesting and stretches the limits of the program team every year. Just a caveat, the DSIDE program is run between the Modelling and Digital Science and Meraka Units at - CSIR. Some experiences will be shared between the groups of students based at both, but some are unique to each unit. I will highlight this in the post.

Whats in a Season?

First let's start describing what actually happens during a season. The 50 students recruited will work in groups or 2/3 on a number of projects. We have had about 16 projects a year in the last few years. I believe a team of about 3 per project is a good number. It makes it easy to break ties and make decision :D. Our Data Science team at MDS takes 18 students, so 6 projects a year. The students are split into these teams and then assigned a project topic and a mentor. The project topic is not simply a description of the project, but access to a partner (who contributed the project topic) and data. The teams work to tackle the project challenge during the 3 month period they are given. The 1st month is focused on exploratory data analysis (EDA) and for the teams to refine their project challenge after spending some time with the data and essentially understanding the feasibility of tackling the challenge with the data given, the partner interactions and tools available. Read more ›

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