Data Science Career: From Researcher To Data Scientist I’m currently working as a Research Assistant in Computer Science, specializing in both Human-Computer Interaction (HCI) and Health Informatics (HI) fields. As part of my role, I collect data from several clinical professionals (clinicians, physicians, and doctors) and analyze it (e.g., t-test, ANOVA and so on). Transforming the analyzed data and organizing it into several datasets, we report and use it by creating scientific papers that will fundament our analysis.

Recently, I started to have an interest in the Data Science field, and I’m considering to develop in that direction and become a Data Scientist. As I mentioned, I’m already applying statistical analysis as part of my job. That rises few questions:


How do the HCI and Data Science areas relate to each other?
How can I shift from being a Research Assistant in the HCI field into Data Science career?
What kind of studying/courses should I take?
Great question! I can appreciate that you have a base of statistics to work from. Most data scientists - especially the ones that go to these data "camps" or whatever - out there do not have this and it should serve you well in your career. Here are some answers to your questions, in order:


Your background will help you because you are used to things like study design and execution and working with data. But I don't seen anything specific to HCI that would help you. When you are a good data scientist, the data itself takes on a muted importance. Right now, when I work on a project, I care that the data was collected in a sustainable way, but what the data is doesn't really matter. Eventually you will reach this too - it's all just numbers and math :-)
You have the statistics side. So now you need the (1) programming skills and the (2) the modeling skills. You should choose to learn a language like Python or R and develop your skills there. From there, you should start looking at the relevant libraries like Tensorflow and Keras to help you build your modeling skills. You should also pick up a textbook or some other resource that talks about what models actually do and how you can tune them, agnostic of the language you choose. Finally, you'll be ready for some research on neural networks and how those work. The measure of success here is being able to write some "traditional" models in Tensorflow (like regression, decision trees, etc) and also being able to write a neural network in Keras. 


I think that your base of already working around experiments and (presumably) applying the scientific method will serve you well and already puts you ahead of most data scientists out there. Now, it's just a matter of being able to do all of that with code and, if you follow the steps above, I think you will see those connections and how you can enter the world of data science.