Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work

by Harlan Harris, Sean Murphy, Marck Vaisman

Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking.

Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths.

This report


Four data scientist Data Businesspeople, Data Creatives, Data Developers, and Data Researchers
Cases in miscommunication between data scientists and organizations looking to hire
Why "T-shaped" data scientists have an advantage in breadth and depth of skills
How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists

Genres: Technology, Business, Science, Reference, Nonfiction, Computers

40 pages, Kindle Edition

First published June 10, 2013

Reviews

[Gavin · Rating 3 out of 5]

I never expect these fluffy little business pamphlets to contain anything worthwhile, but I've referred to this one a few times. Imagine getting some data before you pontificate about data!

The list of the skills involved is the best I've ever seen, if misleadingly intimidating.

[João Coelho · Rating 3 out of 5]

quick and interesting read on how the "data scientist" job description can be broken down into a few distinct, skill-specific categories. Very useful for getting a clearer picture of the actual work being done in "data science". Splitting both the data scientist roles and data science skills into different, clear categories based on research queries results is a helpful insight

[Yasmeen · Rating 3 out of 5]

Short and insightful read into what makes a data scientist.

[Nguyen Hoang Phong · Rating 5 out of 5]

It's useful if you're even thinking about being caught in the whirlpool of data science. It's cute, it's not any longer that it needs to be. And the data-science seems solid. What not to like?

[Sean · Rating 3 out of 5]

30 page mini-book (paper) about breaking "data scientists" into 4 different types and giving them names and advice.

[Andrew Saul · Rating 3 out of 5]

Whilst not a very engaging read, the authors make it very hard to get to the meat of the research they undertook most of the time, this is a very useful book if you want to better understand Data Science roles and where you might fit in.

Thankfully the book isn't long so you only have endure the convoluted way the information is laid out for a short period of time.

This will definitely help you express your own skills better and explore what skills you'd like to add to your data science toolkit.

For those two reasons alone it is more than worth the effort.

[Zarni · Rating 4 out of 5]

I have spent quite some time digging around information on what the "Data Science" field entails as somebody thinking of going to grad school in that field. As a jack of trade, what is my comparative advantage in this venue?
This is a good primer for anybody who is new to the field and need to look more into the details of what type of people do what kind of data science? What type of skill sets are needed? What is the career path etc?
As the authors noted, more research is needed. Hope there is an update to the book.

[Angelo · Rating 4 out of 5]

This book points out different kind of Data Scientists and which are the main differences among them. This operation has been done exploiting surveys, finding four main classes. Therefore, data scientists have been classified with data. How amazing is this?
For data scientists is important to define themselves in order to avoid miscommunication with companies.
Moreover, the book is clear and well written.

[OR · Rating 4 out of 5]

interesting book

Interesting book, it talks about the Data Scientist (DS) profession, and the writer mentions 4 different types of DS that has been used. On the book are shown the tables and diagrams, as references, used as support of the study.
The books contains interesting and useful information.

[Mms Mamdouh Al Shamy · Rating 5 out of 5]

the book is small in number of pages but goes deep to describe the types of Data Scientists and skills each type is best at. And shows the regular miscommunications that happens with the spectrum types of Data Scientists

[Matthew · Rating 5 out of 5]

This book can easily be read in an afternoon, but to fully absorb the results will take more than an afternoon. Very good read for those interested in Data Science.

[Lars · Rating 5 out of 5]

Quick and very useful read. Gives a rare, level-headed look at what data scientists can actually do and how they are most effectively utilized in an organization.

[Sefa]

easy read on meta-data-science. clustering analysis of data science people bases on business, math/stat and programming related skills.

[Alex · Rating 4 out of 5]

Although none of the insights were particularly shocking and most were intuitive, still an interesting read. I particularly appreciated the concision of both the text and the visuals.

[Faye Zheng · Rating 4 out of 5]

Quick and useful; brief insight into where a person with my training/skills might fit in.

[Andy · Rating 5 out of 5]

It gave me a good overview of what a data scientist skills entail.