Confident Data Skills: Master the Fundamentals of Working with Data and Supercharge Your Career

272 Pages·2018·3.916 MB·English

by  Eremenko, Kirill

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Confident
Data Skills
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Confident
Data Skills
Master the fundamentals of working
with data and supercharge your career
Kirill Eremenko
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Publisher’s note
Every possible effort has been made to ensure that the information contained in this book
is accurate at the time of going to press, and the publishers and authors cannot accept re-
sponsibility for any errors or omissions, however caused. No responsibility for loss or dam-
age occasioned to any person acting, or refraining from action, as a result of the material in
this publication can be accepted by the editor, the publisher or the author.
First published in Great Britain and the United States in 2018 by Kogan Page Limited
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as
permitted under the Copyright, Designs and Patents Act 1988, this publication may only be repro-
duced, stored or transmitted, in any form or by any means, with the prior permission in writing of
the publishers, or in the case of reprographic reproduction in accordance with the terms and li-
cences issued by the CLA. Enquiries concerning reproduction outside these terms should be sent
to the publishers at the undermentioned addresses:
2nd Floor, 45 Gee Street c/o Martin P Hill Consulting 4737/23 Ansari Road
London 122 W 27th Street Daryaganj
EC1V 3RS New York, NY 10001 New Delhi 110002
United Kingdom USA India
© Kirill Eremenko 2018
The right of Kirill Eremenko to be identified as the author of this work has been asserted by him in
accordance with the Copyright, Designs and Patents Act 1988.
ISBN 978 0 7494 8154 4
E-ISBN 978 0 7494 8155 1
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library.
Typeset by Integra Software Services, Pondicherry
Print production managed by Jellyfish
Printed and bound in Great Britain by CPI Group (UK) Ltd, Croydon CR0 4YY
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To my parents, Alexander and Elena Eremenko,
who taught me the most important thing in life –
how to be a good person
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Contents
List of figures xi
Bonus for readers xiii
Introduction 1
Part ONE ‘What is it?’ Key principles 5
01 Defining data 9
Data is everywhere 10
Big (data) is beautiful 11
Storing and processing data 13
Data can generate content 15
Using data 17
Why data matters now 18
Worrying achieves nothing 20
References 23
Notes 23
02 How data fulfils our needs 25
The permeation of data 25
Data science and physiology 26
Data science and safety 28
Data science and belonging 30
Data science and esteem 34
Data science and self-actualization 35
Some final thoughts 36
References 36
Notes 37
03 the data science mindset 39
1 Pick the right place to start 39
2 Flex your creative muscles 41
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viii Contents
3 Make use of your background 44
4 Practice makes perfect 50
5 Keep ethics in mind 52
References 57
Notes 57
Part tWO ‘When and where can I get it?’
Data gathering and analysis
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04 Identify the question 65
Look, Ma, no data! 66
How do you solve a problem like… 67
Timekeeping 79
The art of saying no 80
References 81
Notes 81
05 Data preparation 83
Encouraging data to talk 83
With great power comes great responsibility 84
Preparing your data for a journey 86
Reference 101
Notes 101
06 Data analysis Part 1 103
Don’t skip this step 103
Classification and clustering 104
Classification 105
Naive Bayes 116
Clustering 135
References 150
Notes 150
07 Data analysis Part 2 153
Reinforcement learning 153
The multi-armed bandit problem 155
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Contents ix
Upper confidence bound 160
Thompson sampling 167
Upper confidence bound vs Thompson sampling: which is
preferable? 173
The future for data analysis 175
Reference 175
Notes 175
Part tHrEE ‘How can I present it?’
Communicating data
177
08 Data visualization 179
What is visual analytics? 179
What is data visualization? 184
Speaking a visual language 185
Steps to creating compelling visuals 187
• Looking further: Colour schemes 190
Final thoughts 198
Reference 199
Notes 199
• Looking further: 12 ways to see 200
09 Data presentation 213
The importance of storytelling 213
Creating data advocacy 215
How to create a killer presentation 217
The end of the process 226
References 226
Notes 227
10 Your career in data science 229
Breaking in 229
Applying for jobs 238
Preparing for an interview 240
Interviewing them 242
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