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Building Data Science Teams

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As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.

Topics include:
What it means to be "data driven."
The unique roles of data scientists.
The four essential qualities of data scientists.
Patil's first-hand experience building the LinkedIn data science team.

30 pages, Kindle Edition

First published September 15, 2011

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About the author

D.J. Patil

7 books15 followers

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5 stars
79 (23%)
4 stars
97 (29%)
3 stars
114 (34%)
2 stars
33 (9%)
1 star
8 (2%)
Displaying 1 - 20 of 20 reviews
73 reviews3 followers
January 10, 2015
Building Data Science Teams (Kindle Edition) by DJ Patil gives information about (a) how the linkedin data science team was built (b) what are the different attributes to look for hiring people interested in joining your organization as data scientist.

There is nothing exciting or interesting in the book and it is very high level. It read more like Data Science is different, so lets hire folks with diverse backgrounds and run things differently.
Profile Image for Srikar.
127 reviews58 followers
May 26, 2021
Predates the current artificial intelligence machine learning hoopla. Mostly relevant nevertheless.
Profile Image for Rafael Ghossi.
36 reviews
January 5, 2021
Leitura bem curta e sem muita profundidade, mas útil em algumas opiniões dadas pelo autor que pode se aplicar ao cenário da sua empresa.
Profile Image for m.
13 reviews
May 31, 2019
Altogether, an informative read for help framing the data scientist role to newcomers, discussing their core competencies, along with some tips on how to hire for the role and build an effective team.

I felt some of the hiring tips around under the "Would we be willing to do a startup with you?" question section are ripe for projecting biases, so I would caution against blindly using that as a point of reference, and would have preferred the author to do the same.

Notably there's no reference to ethics either, though I suppose in 2011 the field was younger and at least the topic crops up more often in the author's later writing.
Profile Image for Leonardo Longo.
135 reviews12 followers
June 22, 2020
The book title promises an overview of skills, tools and perspectives behind data science groups, but the set of skills presented is a very superficial view of the topic, with room for exploring more how technical teams work together with businesses one.
I didn't had much expectations on going deep into the tools, as they get obsolete really fast and a Gartner report could give a more accurate overview of the actual landscape, but presenting it in such a brief chapter is much less than I've expected.
It's a good starting point on the subject, but needs to explore more at least the three topics presented on the book's title.
Profile Image for Ray.
45 reviews5 followers
May 23, 2018
What I enjoyed about this was its account of how LinkedIn built its data science team, and some of its philosophies on finding and nurturing talent. Overall, it was a good quick read that was relevant and helpful during the time I was first being introduced to data science as a discipline and career.
Profile Image for Ed Barton.
1,302 reviews
April 4, 2020
Quick Monograph On Data Science

A decent read on the approach used by the author in building out the data science team at LinkedIn. Focusing on the role of the team and some of the traits to look for in the hiring process, there is enough here to point you in the right direction as you build out a data science function. Decent read.
Profile Image for Ashish Gourav.
112 reviews31 followers
January 6, 2020
This is an excellent & concise introduction for aspiring 'Data Scientists' and managers/founders/CEOs setting up a Data Science team.
February 8, 2020
Brief summary of how the 'Data Scientist' role was born. Patil's ideal characteristics of a new hire for your data science team.
Profile Image for Elvis Rodrigues.
234 reviews3 followers
March 20, 2017
Apesar de distribuído como um livro, está mais para uma matéria ou post de blog. O autor apresenta os desafios de montar uma equipe focada em Data Science, e conta como os resolveu ao organizar o time do LinkedIn nesta área. Não oferece nada realmente inovador, mas apresenta interessantes insights acerca da divisão de papéis na área.
Profile Image for Fermin Quant.
194 reviews14 followers
March 16, 2017
An interesting read, but way too short and kind of lacking in details.
Profile Image for Miguel.
20 reviews
September 27, 2011
Fairly good introduction to the subject. A bit short, but with many pointers to further info on the emerging area of Data Science. The author shares many useful lessons learned while running the data team at LinkedIn. A good starting point for those who want to become acquainted with this rapidly growing field.
43 reviews3 followers
September 6, 2014
Interesting story about how they create the first data science team in LinkedIn. He includes some details about the type of the profiles needed to create the team, how they was working, how it is better for data-products to work.
77 reviews15 followers
October 15, 2012
DJ Patil presents lots of good ideas in this free short book/long article about how to build a data science team, and what that term even means. Recommended for people working in analytical roles.
Profile Image for Henrik Berggren.
23 reviews21 followers
July 1, 2014
Data team basics. A good check list for hiring data ppl in the middle.
Profile Image for Jonas.
17 reviews1 follower
March 18, 2016
A very high level overview building Data Science team based on LinkedIn experience. Nice quick read, but lacks details.
Profile Image for Mike Fowler.
184 reviews7 followers
January 29, 2021
Given it's age (8 years) the definitions have held surprisingly well. Some interesting ideas around hiring, especially the questions around "Would I run a start up with you?".
Displaying 1 - 20 of 20 reviews

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