8 pieces of advice from my Data School training

by Collin Smith
DS17 receiving their ‘Andy’ awards

On Friday (two days ago), I graduated from The Data School’s intense four month training program. It’s been a challenging, wonderful, and hair-pulling experience from the first day until the last few hours. I wanted to leave a few reflections for those who come after me while it’s all still fresh in my mind.

1. Invest in your cohort

During your training, you’ll spend a huge amount of time in a classroom with the same 7 people, day after day. The relationships you build with them can make or break your experience. I feel lucky that my cohort was particularly full of people from diverse backgrounds, personalities and places. We hail from 5 different continents! However, I know that each cohort has a similar spread of incredible people.

That doesn’t mean it will always be easy to work together. After muddling through your first couple client projects, you’ll probably hit a moment where leadership styles, working styles, or personalities just don’t compute. It can feel like an argument or tension around a project might have ruined a working relationship or even a friendship. In those moments, it’s so important to act with humility and the understanding that your way may not be the best way for the group.

Part of investing in your cohort is an ability to be vulnerable in front of them, to admit mistakes and get through it. Because these moments are uncomfortable and take communal effort to surpass, they build trust. The more respect your cohort has for each other’s decision-making, skills and personalities, the better your experience in training will be.

2. Put everything in context

In the intensity of training it’s easy to get swept up in an outsize sense of the importance of everything you do. This blog, that client project, the other data viz you’re working on outside of class, the can’t miss networking event will become overwhelming. Remember that training is first and foremost the time to learn, to make mistakes, and generally get better.

You’ll write blogs if they help you, the client will get all your work for free, there’s always time for networking and personal projects. Don’t take on so much that you stop enjoying the experience of learning. This time is for your personal and professional development, even if TIL pays you for it. Your work during training is primarily for yourself, there’s 2 years of placements for The Information Lab afterwards during which you can pay it back.

3. Think hard about the way you want to be involved in the #datafam

Part of the expectation of being a Data Schooler is to be engaged in the #datafam. You become part of a very active community of data designers, engineers, humanitarians, analysts, visualizers and general intelligentsia. You will be presented with opportunities to give back in a variety of ways. Writing blogs, tweeting your vizzes, critiquing others, teaching the public, teaching in schools, and taking on pro bono non-profit projects are just a few examples.

All of these will be asked of you at some point, and you should absolutely take part. However, know that you can’t do it all, especially while still in training. If working with an NGO means more to you than writing a blog, then focus on that. If you are a twitter fiend (God save your soul), then become an active voice on that platform. If you love working with kids and influencing the next generation of data professionals, get your butt into a school. If you would rather not have to tame a rowdy classroom of 12-year-olds, become a prolific blogger. Options abound. But, as the recipient of the best education in data visualization in the world, remember that it is important to give back.

Tell your coaches or mentor about the type of community engagement that speaks to you and they’ll help you excel at it. Choosing just one or two methods during training will help you stay sane. And if you’re struggling to keep up with just the training, know that there is still plenty of opportunity to give back on the other side of your four months in classroom.

4. Get to know your mentor, your pod, and all the support structures available to you.

TIL is fundamentally invested in your well-being. It took me a while to believe it because I hadn’t experienced anything quite like it in any previous job. However, the numerous structures that supported me during training convinced me.

It’s important to have someone you feel like you can talk to if things are going poorly, or brag to if things are going well. I’m thankful that my mentor (shout out Steph Kearns!) and pod (shout out Ginger Pod!) were both fantastic sources of support, wisdom, and fun throughout training. If you’re mentor isn’t reaching out to you as much as you’d like, reach out to them or find someone else. There’s lots of fantastic people at TIL and, in all likelihood, a person and personality that you really jive with.

The company has invested time, thought and resources into these support structures. They don’t just happen. Take advantage of them in a way that works for you.

5. Don’t expect it to get easier.

As my fellow cohortian Alice Haslett famously put, “Every day is hard!” I feel the same way. I’ve worked in a lot of industries: field data collection with NGOs, IT support, digital marketing, retail, education. None were as consistently intellectually difficult as my training. Even as you learn more skills, you’ll start to take on bigger challenges and see opportunities to do more complex analysis and visualisation. Take those opportunities to keep pushing yourself. Yes, it means you’ll continue to strain your brain until the last hours. But that strain is far better than sleepwalking through the day, which is far more common in most jobs.

6. Don’t be afraid to look stupid.

There’s a steep learning curve at the Data School. The only thing that will help that curve feel more gentle is asking questions when you don’t understand. Remember that nobody is born with a tooltip in hand. Everyone at the DS has had to learn the skills that continue to wow me on a daily basis.

Coaches Carl and Andy, are both a wealth of information, though they can sometimes affect a sarcastic and critical persona. Trust that if they’re teasing you, it’s not to shame you for your ignorance. If the coaches make fun of you class, that’s a good sign they like you.

7. Ask for help early and often

Remember that the DS office is first and foremost a training space. Yes, people on Data School placements and the core team use it all the time to work. But they do that with the understanding (and often hope) that anyone in training could ask them for help while they’re there. Nobody I ever asked for help showed a trace of irritation. On the rare occasion when they couldn’t help me immediately, they always helped me find someone who could.

I can be a little bullheaded when it comes to solving problems and hesitant to ask for help until I’ve exhausted all my own ideas. While this approach has some benefits, in most cases I would have done better to ask questions when I first ran into an issue.

8. Enjoy learning

It’s rare for a company to spend as much on the professional development of their employees as The Information Lab. Coming from the United States, I had never heard of anything like it. Get paid for four months of pure training? Yeah right, what’s the catch? So far, though, no catch.

Even on the most difficult days, I kept my spirits up with the knowledge that I was learning so much each day. Not even university felt like such an intense and applied skill-building institution.


Remember to reach out if you need help. If you don’t know who to talk to, why not start with me? I’ll be available on Convo, in person, email and (yes, even) Twitter. I look forward to seeing all the amazing things you create.

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