In this blog post Greta continues sharing her experience on what helped her to break into data science. Previously, she discussed the psychological side of learning programming, today she gives three practical tips that have helped her to get a job in data science and hone her programming skills.
A few months ago, I shared three tips on how to push yourself into the world of programming. They were focused on the psychological aspects of getting started, such as starting with the belief that you can do it. This time I want to share three practical tips that, I believe, helped me to get into data science.
Search for scholarships and other credible freebies
When I first started learning programming, I relied solely on the learning materials provided by my university module convenors. Soon I started wanting to learn more, go beyond course requirements. At this point I started searching for additional material online and came across a number of MOOC platforms, such as Coursera, Datacamp, and Dataquest, that suggested to equip me with the skills. Which one do I choose? My ideal criterion was – free (to university students). Additionally, I wanted to receive a completion certificate to showcase some of my skills. With a lot of luck, I found out that Dataquest was offering several 6-month scholarships for individuals from underrepresented groups; I applied and, to my surprise, I got it!
Also, if you cannot find out whether a platform offers free access to students, it might be worthwhile contacting IT (or perhaps a programme coordinator in your department) to ask specifically about this. Some departments might have a partnership with a MOOC that you are not aware of. Also, do not hesitate to contact Warwick IT Training team to discuss your needs as they might be able to offer what you are looking for!
Finally, if you decide to pay for a course then I would suggest signing up for free trial periods. Each platform has a different teaching approach that suits one person but not another. I would like to note that GitHub offers Student Developer Pack that is bundled with several learning offers. For example, it includes free 3-month access to Datacamp. Perhaps this will be enough for you?
Take advantage of open code
Perhaps this tip is more applicable to the more experienced programmers, but do not underestimate the power of looking at the code written by others. Sharing one’s code publicly, especially in a research context, is a part of shift towards opening research/science. I will discuss more extensively on how to benefit from open science as a student in my next blog post, but for now I want to encourage you to sign up and search for interesting projects on, for example, Github or GitLab. Once you find something interesting, you might want to examine it. Do you understand what is happening? It’s OK not to. However, I learned so much about coding by “borrowing” code snippets from my supervisor’s repositories while I was learning spatial data analysis in R (do not forget to acknowledge this). Just a kind reminder that reading code might be harder than writing!
Share your journey and small wins
I have already discussed the value of celebrating small wins, but I would also encourage you to share your journey. Especially if it is something that you find interesting! Let me share a personal example. At the time when I was studying on Dataquest, I was also moving my personal website from WordPress to another platform. While reading about the options, I discovered that I could build and manage a website using RStudio. I shared my experience and some tips on Dataquest’s blog. It turned out to be a great learning experience: I experienced what it means to collaborate with an editor, had to systematise what I’ve learnt, and think of the best way to communicate this. Interestingly, my former supervisor brought up this article in our very first meeting. He was glad that I am interested in sharing my knowledge. Honestly, I felt super embarrassed as I saw (and still see) so many typos in that text; yet it was perceived as evidenced of my communication skills through showing rather than telling. I would argue that while technical knowledge is essential, there is a great benefit of being able to communicate the whats, whys, and hows of your work.
Have any of the tips resonated with you? Perhaps you’re applying them or have additional practical tips already to share? Let us know by tweeting us @researchex, leaving us a message on Instagram @warwicklibrary or by emailing us at libraryblogs@warwick.ac.uk
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