Non-traditional strategies for mid-career switch to #Datascience and #AI
In this post, I explore strategies to switch to Data Science mid-career. This switch is not easy, but based on the experience of many who I have taught/mentored/recruited – it is possible. Most people consider PhD/MooC etc for switching their career to Data Science.
But here, I will explore some non-traditional/unorthodox ways of switching to Data Science. I draw upon my personal experience as a teacher, data scientist and in recruiting data scientists – especially in creating personalized AI / Data Science courses
So, here are my insights
1) Consider Data Engineering instead of Data Science: Data Engineers are the relatively less known cousins of Data Scientists but are rapidly growing in importance as Data Science matures. More importantly, depending on your experience, a transition to data engineering may be easier (ex if you had previous ETL/ SQL experience)
2) Draw on your business knowledge: Business knowledge will be valuable in Data Science especially with many areas like feature engineering. Also, most algorithms improve previous benchmarks – but the task itself remains the same. For example, Churn prevention / Fraud detection etc are well defined industry problems. AI/ Machine learning simply improve the previous benchmarks but the domain knowledge is still valuable.
3) Github: Probably the best way you can differentiate. People study for MooCs or even PhDs but they cannot demonstrate that they can build anything. You need a Github repo which will put you far ahead of many
4) Niche: Focus on a niche in Data Science. For example, I am working with Tensorflow mobile. Considering the current success of Tensorflow – it’s a no-brainer that tensorflow mobile will be interesting. Apple is following a similar strategy with Coreml for AI on iPhone devices
5) Focus on AI: This may sound unusual. But let me explain. I consider a boring definition of AI. AI is (mostly) based on Deep Learning. Deep Learning is a set of complex (and math based) techniques and are used for automatic feature engineering. AI will be become increasingly pervasive. In doing so, many companies will come forward to simplify AI. Therein lies the opportunity. We see this already in cases like Driverless AI from H2o.ai. This means, at some point in the near future you can implement AI without knowing Deep Learning in detail.
6) Look for tangential algorithm applications: I can explain this best with examples from my personal experience. I started off with IoT (which I still work with). However, I have also worked with fintech and healthcare applications I did not have a substantial background with Healthcare or fintech – however IoT is mostly based on Time series. As are also parts of fintech and Healthcare.
7) Choose the right books: If you are learning Data Science, broadly there are two types of books. An example of the first type of book is by Hastie (large pdf book). Another type of book is – Deep Learning with Keras by Antonio Gulli and Sujit Pal. The former is heavy on concepts and maths. The latter is very pragmatic. With each chapter based on code and with a github repository. You need both types but you definitely need the later.
8) Give yourselves a year (at least) - This switch will not be easy In my view, it needs a year but it’s worth it!
9) Keras: One word .. Keras ..DI/ML are hard enough as it is. You need the best strategy to make your life simple but also to cover depth. Hence Keras. PS I note gluon from Microsoft and Amazon which sounds like a similar approach to Keras but I am not personally familiar with it yet
10) Develop end to end problem solving skills Ultimately, tools don’t matter as much as the ability to use data and algorithms to solve problems. This great post by Vincent Granville on forecasting meteorite hits shows the end to end skills needed for problem solving in data science. I believe many people work on specifics (ex an algorithm) but miss how to solve problems end to end
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in https://www.datasciencecentral.com/profiles/blogs/non-traditional-strategies-for-mid-career-switch-to-datascience