Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways. In my world, there are two main types:
Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren't taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.
The Type A Data Scientist can code well enough to work with data but is not necessarily an expert. The Type A data scientist may be an expert in experimental design, forecasting, modelling, statistical inference, or other things typically taught in statistics departments. Generally speaking though, the work product of a data scientist is not "p-values and confidence intervals" as academic statistics sometimes seems to suggest (and as it sometimes is for traditional statisticians working in the pharmaceutical industry, for example). At Google, Type A Data Scientists are known variously as Statistician, Quantitative Analyst, Decision Support Engineering Analyst, or Data Scientist, and probably a few more.
Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data "in production." They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).
In this post, we discuss strategies to transition your career to the role of a (type A) Data Scientist.
The ideas I discuss here are based on my work with the participants of the Data Science for IoT course
When I first read this idea of Type A and Type B Data Scientists, I found it incredibly liberating.
It makes you realize that the ‘unicorn’ Data Scientist (who know it all!) - like their equestrian counterparts - are also largely mythical.
Having acknowledged that, you can then start to make practical progress towards a strategy for transitioning your career towards Data Science.
Here, I discuss twelve uncommon strategies for transitioning to a Type A Data Scientist based on my work with my course participants.
But first, let us discuss one common theme. Yes, you must build an app to learn Data Science. But building itself is not enough. When you start with limited resources and try to build a serious app for Data Science, you find quite quickly that the biggest limitation is the lack of serious data. So, like everyone else you also end up building your app against the UCI datasets (or similar).
Hence, you need to think of wider strategies in addition to building. Here are some of the strategies we follow in our teaching:
The audience here is someone who is exploring Data science on their own.
I use the Type A classification as someone who uses Data Science to solve many complex problems – but is not responsible for working with a high performance model in Production.
Start with what you know: This seems obvious but often ignored. For example, imagine you have spent the better part of your career with Oracle and you want to be a Data Scientist. Why not start with Oracle? There is a whole suite of Oracle BI tools . These cover the whole range from visualization to advanced analytics. They use Pl/SQL or R. That helps to get you started a lot faster instead of learning something entirely new.