Transfering skills in the tech industry
Working in technology requires a diverse skillset and one of those skills is being able to apply knowledge to different areas of work. Depending on your career ambitions the career pathway one might find themselves following in the tech industry was once described to me as being a bit different from the conventional approach of “climbing the ladder” known in most industries but rather being more like “climbing the jungle gym”. One of the reasons for this is because the tech industry is evolving so rapidly. Having said this being able to apply your knowledge and at the same time transfer your skills from one area of work to another is very helpful in climbing the jungle gym of tech.
I’ve recently encountered this as I’ve changed my focus from being a front-end developer to data science and analytics. For the rest of this post I’d like to share some of the things I’ve learnt while going about this process. It might not be particularly relevant to you but it should still create an awareness of how you could leverage the skills you have.
For me it wasn’t as much about simply finding myself in a position where I began to do more data related work but rather a curiosity as to what was happening in the field of data science and analytics.
As a front-end developer my skillset predominantly comprised of JavaScript, HTML and CSS. The secondary skills I’ve found myself needing to have a basic knowledge of in order to better carry out my job include PHP and a little bit of SQL. Sometimes it just takes a basic level of knowledge to facilitate further self-learning.
The first step I took was to sign up for 5 different data science newsletters (Data Science Weekly; O’Reilly Data Newsletter; KDnuggets News; The Data Science Roundup; Data Elixir). This provided me with a great deal of reading from which I gained an understanding of the most prominent skills and tools being used by data scientists and how they were being applied to solve various problems and gain interesting insights from many different industries.
Data science is clearly different from front-end development I knew there would be some skills I would have to gain or just improve on so I looked for an online course to help me in this respect. As usual with any field in technology you encounter the question of “what language should I learn?” or the continuous debate as to which language is best. I know all too well about this as a front-end developer and all the JavaScript frameworks and their associated hype. On the other hand with data science it is R vs Python. My feeling on this, along with reading many opinions is to simply pick one and start learning. If you know the one you should then be able to learn the other quite easily. I picked R to get me started as I had worked with it during my university days so I had some familiarity with it.
You may have noticed a common theme so far… coding. Being able to code is obviously fundamental as a web developer and this is synonymous with data science. I rapidly learnt the power of coding in R to clean and format datasets, apply algorithms and produce visualizations. I found the functional programming style of JavaScript to be similar to the coding style used in R. However, one rather significant difference I became aware of was to think in terms of vectors and matrices instead of objects and arrays.
Another skill used in data science is producing visualizations for representing results. As a front-end developer and although I’m by no means a designer I at least have an idea as to what might work visually and what definitely wouldn’t. I have also encountered various charting frameworks for which JavaScript has a wide range available. Working with R I discovered Shiny, which is R’s framework for developing dashboards and interactive visualizations. Knowing HTML, CSS and JavaScript made it very easy to develop these dashboards and visualizations in Shiny.
Lastly, I found some data analysis work that could be picked up within MoneySavingExpert.com and by doing this I have been able to gain some practical experience, which is very valuable in any learning process.
This is by no means a list of skills necessary in data science and analytics. There are many more requirements to carry out data science and analytics tasks and some of these I’m still learning. My intention was hopefully to emphasize the ability to make use of the skills you may already possess that can be used in doing something else by giving my experiences.
In closing I have one last thought and that is sometimes climbing across the monkey-bars of the jungle gym is a lot harder than climbing a ladder.
Before you leave
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