The 22 skills of a Data Scientist..
My first article on “How To Become A Data Scientist” explored the basic four different types of Data Scientist – Data Business People, Data Creatives, Data Developers and Data Researchers (as per the O’Reilly study “Analysing the Analysers”). It highlighted the need for a data science team with diverse and complementary skill sets. It is clear that no one “superstar” can fulfil all the required roles, and it is up to us as recruiters to understand the requirements of any organisation to ensure that there aren’t any gaps in their capability.
Therefore, in this piece, I wish to assess in more detail the primary skills of each type of Data Scientist, investigating in which areas they might collaborate – thus starting to compile a basic profile of each role. I’ll be following up next time with the different routes to becoming a Data Scientist.
To recap: Data Business People (DB) are leaders and entrepreneurs. Data Creatives (DC) are multi-talented artists and hackers. Data Developers (DD) are programmers and engineers. Data Researchers (DR) are scientists and statisticians.
As you can see by the following graphic, there is a usually a stronger skill set for each Data Scientist group. As recruiters, it is important that we identify not only the general skill set of our candidates, but also where they have particular strengths.
There are certain areas in which each type will collaborate. For example, Data Creatives might work with Data Researchers on Statistics, Data Developers might work with Data Creatives on ML/Big Data work, while Data Business People may work fairly independently on the Business side.
The skills of a Data Scientist can be broken down into 22 sub-sections, and I offer my interpretation of the key skills for each of the four Data Scientist types. This is my subjective view and of course is open to debate.
Algorithms (ex: computational complexity, CS theory) DD,DR
Back-End Programming (ex: JAVA/Rails/Objective C) DC, DD
Bayesian/Monte-Carlo Statistics (ex: MCMC, BUGS) DD, DR
Big and Distributed Data (ex: Hadoop, Map/Reduce) DB, DC, DD
Business (ex: management, business development, budgeting) DB
Classical Statistics (ex: general linear model, ANOVA) DB, DC, DR
Data Manipulation (ex: regexes, R, SAS, web scraping) DC, DR
Front-End Programming (ex: JavaScript, HTML, CSS) DC, DD
Graphical Models (ex: social networks, Bayes networks) DD, DR
Machine Learning (ex: decision trees, neural nets, SVM, clustering) DC, DD
Math (ex: linear algebra, real analysis, calculus) DD,DR
Optimization (ex: linear, integer, convex, global) DD, DR
Product Development (ex: design, project management) DB
Science (ex: experimental design, technical writing/publishing) DC, DR
Simulation (ex: discrete, agent-based, continuous) DD,DR
Spatial Statistics (ex: geographic covariates, GIS) DC, DR
Structured Data (ex: SQL, JSON, XML) DC, DD
Surveys and Marketing (ex: multinomial modeling) DC, DR
Systems Administration (ex: *nix, DBA, cloud tech.) DC, DD
Temporal Statistics (ex: forecasting, time-series analysis) DC, DR
Unstructured Data (ex: noSQL, text mining) DC, DD
Visualisation (ex: statistical graphics, mapping, web-based data‐viz) DC, DR
The success of your Big Data organisation will depend on how your team functions within these 22 distinct areas. Collaboration between distinct work streams is the key to success and it is vital that you recruit and retain “T-shaped” individuals – i.e. with a solid general skillset plus one or two “stand-out” skills.
The next post will explore the different routes to take in becoming a Data Scientist.
We can help compile an audit of your Big Data organisation. Are you sure that you don’t have any gaps? If you do, Big Cloud can help you find the right people to fill them!
Matt Reaney is the Founder of www.bigcloud.io specialists in Big Data recruitment - connecting innovative organisations with the best talent in Data Analytics and Data Science.
Chief Technical Officer bei CyKlone Tidal Energy
10yIn that scenario guy number 5 is the Business or Requirements Analyst. He needs to fill the gap of the DB skills and have sound hands on Controlling experience. At best creating useful BI / KPI instruments starting on a blank page. Didn't we want to have a talk on that a while ago?
Freelance Data Analytics & DWH
10yExcellent
Senior Data Scientist at Crayon Data
10yI am glad to see that there are recruitment specialists who take pains to understand in depth what they are doing rather than merely engage in keyword/syntactic pattern matching.
I’ve solved hundreds of data driven business issues. Balance of hands on coding, data science and analytic skills, team leadership, client facing, strategy, and a consultant's mindset.
10yThe key 23rd skill is the ability to articulate results to diverse audiences. This includes the ability to have high EQ and be nimble during the sharing of results or ideas. (IMO)
Java J2ee ,Web development expert. Create value from your Online Reputation.
10yok. in reality we can become experts only in very few areas in a given technology spectrum. say 3 out of 22. regards vinodhsen