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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

 

I hope you found these strategies useful If you want to know more about my work, please see my work in creating personalized #DataScience and #AI courses

 

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Comment by ajit jaokar on October 18, 2017 at 6:47am

Many thanks Michael 

PS we need to get back in touch re your course. call?

what you describe below is a key part of my thinking @Oxford(and also our course)

see this article which explains more

https://www.kdnuggets.com/2017/10/data-science-systems-engineering-...

kind rgds Ajit

Comment by Michael Hummel on October 17, 2017 at 6:52am

Hi Ajit,
I really like your post as it addresses a question many experienced professionals are asking themselves right now.

Especially your recommendation to build an AI career on in-depth business experience finds my full support - AI is nothing without the business perspective when asking the right questions and evaluating the results.

Recently, I discussed traditional and AI based product development with very experienced VPs of Engineering. One of our - well, quite obvious - findings is that you need to manage the product lifecycle of an AI-based product like with any other software product too. You need to gather requirements, design, build, test, deploy, run and this all in an agile, iterative process.

This brings me to an additional career path for experienced engineers - expand on what you are good, e.g. requirement engineering, quality assurance, system maintenance by learning about the special needs of AI based products.

How do you test AI-based systems, how do you run and support AI-based systems, how do you define requirements?

These jobs will need to adapt to the new technological opportunities and challenges that come with AI. However, solid experience with software product development processes and tools is a kick-starter and certainly will increase your market value.

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