O’Reilly released a free downloadable report a while back that presents the results of a survey of Data Scientists across the industry – circa 250 respondents. The report looks at a list of skills and classifies Data Scientists into 4 main categories:

• Data Businessperson
• Data Creative
• Data Developer
• Data Researcher

Under each of these headings the roles are defined as:

As an Architect I can see a fit to the “Jack of All Trades” box, however I think that there is a reach across the Researcher, Creative and Businessperson categories if we were to be classed. However as an Architect it is important to understand the skills that a Data Scientist needs across these areas as going forward there will be more opportunities to work side by side with Data Scientists in solutions and architectures.

The report gives a list of skills that a Data Scientist has under each classification of Data Scientist

• Algorithms (ex: computational complexity, CS theory)
• Back-End Programming (ex: JAVA/Rails/Objective C)
• Bayesian/Monte-Carlo Statistics (ex: MCMC, BUGS)
• Big and Distributed Data (ex: Hadoop, Map/Reduce)
• Business (ex: management, business development, budgeting)
• Classical Statistics (ex: general linear model, ANOVA)
• Data Manipulation (ex: regexes, R, SAS, web scraping)
• Front-End Programming (ex: JavaScript, HTML, CSS)
• Graphical Models (ex: social networks, Bayes networks)
• Machine Learning (ex: decision trees, neural nets, SVM, clustering)
• Math (ex: linear algebra, real analysis, calculus)
• Optimization (ex: linear, integer, convex, global)
• Product Development (ex: design, project management)
• Science (ex: experimental design, technical writing/publishing)
• Simulation (ex: discrete, agent-based, continuous)
• Spatial Statistics (ex: geographic covariates, GIS)
• Structured Data (ex: SQL, JSON, XML)
• Surveys and Marketing (ex: multinomial modeling)
• Systems Administration (ex: *nix, DBA, cloud tech.)
• Temporal Statistics (ex: forecasting, time-series analysis)
• Unstructured Data (ex: noSQL, text mining)
• Visualization

ML = Machine Learning

OR = Operations Research

From reading other reports this is by no means a full list of skills but provides a good insight into what a Data Scientist needs in their skills bag.

The report then looks at typical tasks that would be covered by each category and splits these into 22 core tasks across 5 main tasks.

The visualisation below illustrates the results showing the skills and tasks across each Data Scientist type to show a percentage of skill that is needed.

Overall a good report giving a highlight of the business areas and skills of a Data Scientist

Report Source

Analyzing the Analyzers

An Introspective Survey of Data Scientists and Their Work

http://www.oreilly.com/data/free/analyzing-the-analyzers.csp