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  17 European Data Science Salary Survey

Tools, Trends, What Pays (and What Doesn’t) for Data Professionals in Europe

  

  

  

  

  

  

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  2017 European Data Science Salary Survey

Tools, Trends, What Pays (and What Doesn’t)

for Data Professionals in Europe

  John King and Roger Magoulas REVISION HISTORY FOR THE FIRST EDITION 2017 EUROPEAN DATA SCIENCE SALARY SURVEY 2017-02-10: First Release by John King and Roger Magoulas

  Editor: Shannon Cutt While the publisher and the authors have used good faith efforts to Designer: Ellie Volckhausen ensure that the information and instructions contained in this work Production Editor: Shiny Kalapurakkel are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for Copyright © 2016 O’Reilly Media, Inc. All rights reserved. damages resulting from the use of or reliance on this work. Use of the Printed in Canada. information and instructions contained in this work is at your own risk. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, If any code samples or other technology this work contains or describes Sebastopol, CA 95472. is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies

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  2017-02-10. First Edition

ISBN: 978-1-491-97750-7

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Table of Contents

  

2017 European Data Science Salary Survey ........................................ i

  

  

  

  

  

  

  

  

  

  

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY HERE WE TAKE A DEEP DIVE YOU CAN PRESS ACTUAL BUTTONS (and earn our sincere

INTO THE RESULTS FROM

  gratitude) by taking the 2017 survey—it only takes about 5 to 10 minutes, RESPONDENTS BASED IN EUROPE, EXPLORING CAREER and is essential for us to continue to provide this kind of research. DETAILS AND FACTORS THAT

INFLUENCE SALARY

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Executive Summary

  ■

  IN 2016, O’REILLY MEDIA CONDUCTED A DATA SCIENCE Among those who use R or Python, users of both SALARY SURVEY ONLINE. The survey contained 40 have the highest salaries

  questions about the respondents’ roles, tools, compensation,

  ■ A few technical tasks correlate with higher

  and demographic backgrounds. About 1,000 data scientists,

  salaries: developing prototype

  analysts, engineers, and other profession-

  models, setting up/maintaining

  als working in Data participated in the

  data platforms, and developing

Respondents who use

  survey—359 of them from European

  products that depend on real-time

  countries. Here, we

  analytics

Hadoop, Spark, or

  take a deep dive into the results from

  ■ Respondents who use Hadoop,

  respondents based in Europe, explor- Python were twice as

  Spark, or Python were twice as

  ing career details and factors that

  likely to have a major increase in

likely to have a major

  influence salary. Some key findings

  salary over the last three years,

  include:

  

increase in salary over

compared with those whose stack consists of Excel and

  ■ Most of the variation in salaries the last three years. relational databases can be attributed to differences in the local economy

  We hope that these findings will be

  ■ Data professionals who use Hadoop and useful as you develop your career in data science.

  Spark earn more

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Introduction

  

SINCE 2013, WE HAVE CONDUCTED AN ONLINE SALARY respondents are paid in other currencies, such as pounds or

SURVEY FOR DATA PROFESSIONALS and published a rubles. Over the period in which responses were collected,

  report on our findings. US respondents typically dominate there were some important shifts in exchange rates, most the sample, at about 60%–70%. Although many of the notably the fall of the pound after Brexit. However, the findings do appear to apply to people across the globe, we geographical distribution of responses did not correlate in any thought it would be useful to show results specific to Europe, meaningful way with any period of collection (e.g., when the looking at finer geographical details and identifying any patterns pound was high or low), so these currency fluctuations likely that seem to only apply to Europe. In this report, we pool all translate into noise rather than bias. 359 European respondents from the Data Salary Survey over a 13-month period: September 2015 to October 2016.

  The median salary of European respondents was €48K,

  In the horizontal bar charts throughout this report, we include

  but the spread was huge. For example, the top third earned

  the interquartile range (IQR) to show the middle 50% of

  almost four times on average as the bottom third. Such a

  respondents’ answers to questions such as salary. One quarter

  large variance is not surprising due to the differences in the

  of the respondents have a salary below the displayed range, per capita income of countries represented. and one quarter have a salary above the displayed range.

  A note on currency: we requested responses about salaries

  The IQRs are represented by colored, horizontal bars. On each

  and other monetary amounts in US dollars. In this report, we

  of these colored bars, the white vertical band represents the have converted all amounts into euros, though many European median value.

BASE SALARY (EURO) SHARE OF RESPONDENTS

  €0K €20K €40K €60K

  €80K (EUROS) y

  €100K alar

  €120K ase S B

  €140K €160K €180K > €180K

  0% 5% 10% 15% 20% 25% 30% 35% 40% Share of Respondents

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Countries

  €

THE UK WAS THE MOST WELL-REPRESENTED EUROPE-

  54K, Spanish and Italian respondents tended to have much

  AN COUNTRY, with about a quarter of the sample, followed

  lower salaries (€35K). Portugal was somewhat of an outlier in by Germany, Spain, and the Netherlands. By far, the highest Western Europe, with a median of €22K. The median salaries salaries were in Switzerland, with of Germany, the Netherlands, and a median salary of €117K, followed France were close to the regional

  

Unlike in the west, Eastern

  by Norway with €96K, although median (about €53K). the latter figure is only based on

  European salaries appeared Salaries drop dramatically as we

  five respondents. Among countries move south and east. The median

  

to be fairly consistent, even

  represented by more than just a salary of respondents from Central handful of respondents, the UK had and Eastern Europe was €17K. Russia

across national borders.

the second-highest median salary: and Poland, the two most well-rep-

  € 63k (£53). resented countries in this half of the

  Even within Western Europe, there was significant variation continent, also had median salaries of €17K: unlike in the west, in salary. While UK, Swiss, and Scandinavian salaries were

  Eastern European salaries appeared to be fairly consistent, significantly higher than the Western European median of even across national borders.

COUNTRIES COUNTRIES SHARE OF RESPONDENTS SHARE OF RESPONDENTS SHARE OF RESPONDENTS

  United Kingdom United Kingdom United Kingdom United Kingdom Germany Germany Germany Germany Spain Spain Spain Spain

  Netherlands Netherlands Netherlands Netherlands France France France France y tr oun

  Ireland Ireland Ireland Ireland C

  Russia Russia Russia Russia Switzerland Switzerland Switzerland Switzerland Poland Poland Poland Poland

  Italy Italy Italy Italy 0% 0% 0% 5% 5% 5% 10% 10% 10% 15% 15% 15% 20% 20% 20% 25% 25% 25% 30% 30% 30% 0% 5% 10% 15% 20% 25% 30%

  Share of Respondents

  COUNTRIES SALARY MEDIAN AND IQR* (EURO) United Kingdom Germany

  Spain Netherlands y

  France tr oun C

  Ireland Russia Switzerland Poland

  Italy €0K €30K €60K €90K €120K € 150K

  Range/Median (Euro)

*The interquartile range (IQR) is the middle 50% of respondents' salaries. One quarter of respondents have a salary below this range, one quarter have a salary above this range.

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Salary Versus GDP NATIONAL MEDIAN SALARIES SHOULD BE EXPECTED One shortcoming of this plot is that it does not take into ac- TO VARY according to the economic

  count years of experience, which turns conditions of the country, so the out to be very uneven in the sample

  

The question becomes,

  question becomes: given a country’s among different countries. In particu- economy (in particular, its per capita lar, respondents from Western Europe

  

given a country’s

  GDP), do the salaries of data scientists tended to be much more experienced

  

economy (in particular,

  and engineers vary? Here, we plot per (with an average of seven years) than capita GDP and median salary of each respondents from Eastern Europe

  

its per capita GDP),

  country in the sample. The resulting (with an average of four years). graph is remarkably linear, with outliers do the salaries of Since experience correlates with salary, largely explained by small sample size: the West-East salary difference is

  

data scientists and

  Greece, for example, has a high- exaggerated due to this experience er-than-expected median salary given a differential.

  

engineers vary?

  relatively low per capita GDP, but this is based on just one respondent.

  SALARY VERSUS GDP The size of each circle represents the number of respondents from the country in the sample. MEDIAN SALARY VERSUS PER CAPITA GDP Source for per capita GDP: https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita

  €80K Switzerland Norway s) o

  €60K ur f E

  Denmark Ireland Sweden thousands o

  Austria €40K Netherlands

  United Kingdom Belgium Finland a GDP (

  Germany France apit

  Italy Per c

  Spain €20K

  Slovenia Portugal Greece Estonia

  Czech Republic Hungary Slovakia Poland Croatia

  Turkey Romania Russia Belarus

  Serbia Armenia €0K €0K €20K €40K €60K €80K €100K €120K €140K

  Median salary of data scientists / engineers (thousands of Euros)

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Company Size

COMPARED TO THE WORLDWIDE SAMPLE, THE SUBSAMPLE FROM EUROPE TENDED TO COME FROM

  SMALLER COMPANIES. While 45% of US respondents were

  from companies with over 2,500 employees, only 35% of European respondents were from such companies. This number rises to 39% if we consider only those from Western Europe; only 13% of respondents from Central/Eastern Europe were from large companies.

  Largely because of the East-West split, salaries at larger com- panies tend to be high: the 19% of respondents from compa- nies with over 10,000 employees had a median salary of €61K. In contrast, the half of the sample that was from companies with 2 to 500 employees had a median salary of €43K.

COMPANY SIZE SHARE OF RESPONDENTS

  19% 10,000+

  16% 8% 2,501 – 10,000 1,001 – 2,500

  5% 501 – 1,000

SALARY MEDIAN AND IQR*

  22%

  

1

101 – 500

  • – 2

  

25

s

  • – 26 100

  ee y

  • – 101 500

  17% f Emplo

  • – 501 1,000 26 – 100
  • – 1,001 2,500

  Number o

  • – 2,501 10,000 10,000 +

  11% €0K €20K €40K €60K €80K €100K €120K

  2 – 25 Range/Median

  1%

  1

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Industry

  A PLURALITY OF RESPONDENTS (20%) WORKED IN CONSULTING, after which the top industries were software (18%), banking/finance (10%), and retail/ecommerce (9%).

  These figures are very similar to those of the worldwide sample. As with company size, the differences in salaries among in- dustries was largely attributable to geography. Manufacturing, insurance, and publishing/media were all overrepresented by countries with higher salaries. One exception to this was bank- ing/finance, which had a high median salary of €58K and did not correlate with a particular country or region: data profes- sionals in banking do appear to earn more.

INDUSTRY SHARE OF RESPONDENTS

  2%

  INSURANCE 3%

  ENTERTAINMENT 3%

  OTHER 5%

  PUBLISHING / MEDIA 5% MANUFACTURING / HEAVY INDUSTRY

  5% CARRIERS / TELECOMMUNICATIONS

  6% EDUCATION

  6% HEALTHCARE / MEDICAL

  6% ADVERTISING / MARKETING / PR

  9% RETAIL / ECOMMERCE

  10% BANKING / FINANCE

  18% SOFTWARE

  21% CONSULTING

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Tools

THE TOP FOUR TOOLS FROM EUROPEAN RESPONDENTS those who used more than 10 tools had a median salary

WERE EXCEL, SQL, R, AND PYTHON , each used by over of €53K.

  half of all respondents. These four tools have kept their top Since there is significant overlap between users of individu- positions in every Data Salary Survey we have conducted, and al tools, it is useful to consider mutually exclusive groups of there does not appear to be any sign of this changing. Almost respondents based on tool usage. The groups we will define every respondent reported using at least one, and about half here are based on a simple set of rules, but using a clustering the sample used three or all four. algorithm would produce very

  Commonly used tools with similar results. The rules are:

  

Commonly used tools with

  above-average salaries include 1) If someone used Spark or

  Scikit-learn (whose users have

  

above-average salaries include

  Hadoop, we call them “Hadoop” a median salary of €52K), Spark

  

Scikit-learn (whose users have

  2) If someone (not in the Hadoop (€55K), Hive (€57K), and Scala group) uses R and/or Python,

  

a median salary of (€52K),

  (€70K). Readers may notice that they are labeled “R+Python,” most tools have a higher median

  

Spark (€55K), Hive (€57K), and

  “R-only,” or “Python-only,,” as salary than appropriate

Scala (€70K).

the sample-wide median salary

  3) Everyone who uses SQL and/ of €48K. This is because respon- or Excel (usually both), we call dents who use lots of tools tend to “SQL/Excel” earn more (and they are counted in a large number of tool salary medians). The 43% of respondents who used no

  The five resulting groups each contain between 13% more than 10 tools had a median salary of €43K, while and 26% of the sample. The Hadoop group reported the

TOOLS SHARE OF RESPONDENTS

  Excel SQL R Python ggplot MySQL

  Scikit-learn Bash Matplotlib Spark

  Microsoft SQL Server PostgreSQL Oracle Tableau

  Hive D3 Java JavaScript

  Shiny Spark MlLib ool

  Apache Hadoop T

  Cloudera ElasticSearch Scala MongoDB

  Visual Basic/VBA QlikView Matlab Hortonworks

  SQLite Google Charts Impala Kafka

  Hbase C C++ Power BI

  Weka 0% 10% 20% 30% 40% 50% 60% 70% Share of Respondents

TOOLS SALARY MEDIAN AND IQR*

  T ool Range/Median

  €0K €20K €40K €60K €80K €100K Weka Power BI C++

  C Hbase Kafka Impala

  Google Charts SQLite Hortonworks Matlab

  QlikView Visual Basic/VBA MongoDB Scala

  ElasticSearch Cloudera Apache Hadoop Spark MlLib

  Shiny JavaScript Java D3 Hive Tableau

  Oracle PostgreSQL Microsoft SQL Server Spark

  Matplotlib Bash Scikit-learn MySQL ggplot Python

  R SQL Excel

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY

  highest salaries (median: €56K), while the R-only group had the lowest (€42K). However, this doesn’t mean that knowing R means less pay: respondents using Python and R earned slightly more than those using Python and not R.

  Aside from salary, one important difference between the groups is experience. The SQL/Excel group—in other words, those who don’t use Python, R, Spark, or Hadoop—was more experienced than the other groups (8.3 years on average), followed by the R-only (7.3 years), Hadoop (6.3 years), Python-only (6 years), and Python+R groups (5.2 years). Since we expect more-experienced data professionals to earn higher salaries, the median salary of €46K for the SQL/Excel group is actually quite low, while the €48K of the Python-R group is high.

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Tasks WE ALSO ASKED FOR INFORMATION ABOUT WORK Tasks that correlate most strongly with high salaries are

TASKS: this is meant to dig a little deeper than what we those that involve management and business decisions, such

  can glean from a job title. Respondents could say they had as “communicating findings to business decision-makers,” “major” or “minor” involvement in each task. For the most “identifying business problems to be solved with analytics,” part, tasks that correlate positively with salary also correlate “organizing and guiding team projects,” and “communicat- positively with years of experi- ing with people outside of your ence (and often are clearly asso- company”. The median salaries ciated with being a manager). of respondents who reported

  

Tasks that correlate most

  major involvement in these tasks Among the most common

  

strongly with high salaries are

  were €54K, €56K, €66K, and tasks were “basic exploratory € 55K, respectively.

  

those that involve management

  data analysis,” “data cleaning,” “creating visualizations,” and

  Aside from management and

and business decisions.

“conducting data analysis to business strategy, several answer research questions,” each technical tasks stood out for with 85%–93% of the sample above-average salaries: as a major or minor task. Data cleaning has the unfavorable “developing prototype models” (major involvement: €52K), distinction of being the only task for which each level of “setting up/maintaining data platforms” (€50K), and involvement means less pay: those with major involvement “developing products that depend on real-time analytics” earn less than those with minor involvement, who in turn (€62K). For each of these tasks, respondents who reported earn less than those who never clean data. However, this may major involvement earned more than those who reported have more to do with the fact that more-experienced data minor involvement, and those who reported minor professionals (who we know earn more) tend to do less data involvement earned more than those who did not cleaning. engage in these tasks at all.

  

RESPONDENT CATEGORIES WHICH OF THE FOLLOWING MOST ACCURATELY

BASED ON TOOL USAGE DESCRIBES THE NEXT STEP YOU WOULD LIKE TO

TAKE TO ADVANCE YOUR CAREER? SHARE OF RESPONDENTS SHARE OF RESPONDENTS

  26% 41%

  HADOOP / SPARK LEARN NEW TECHNOLOGY/SKILLS

  22% PYTHON+R

  20% WORK ON MORE INTERESTING/

  18%

  IMPORTANT PROJECTS R ONLY 13%

  18% PYTHON ONLY MOVE INTO LEADERSHIP ROLES

  12% 19% SWITCH COMPANIES SQL/EXCEL

  (NO PY/R) 6% START YOUR OWN COMPANY

  (EUROS) SALARY MEDIAN AND IQR (EUROS) SALARY MEDIAN AND IQR

  Hadoop / Spark Learn new technology/skills Python+R

  Work on more interesting/ important projects R only tep t S

  Move into leadership roles Python only x ool Usage

  T Ne

  SQL/Excel (no Py/R) Switch companies

  

100

  20K €40K €60K €80K Start your own company Range/Median

  €0K €20K €40K €60K €80K €100K

  T ask Number of Respondents

  0% 10% 20% 30% 40% 50% 60% 70% Using dashboards and spreadsheets (made by others) to make decisions Developing products that depend on real-time data analytics Setting up/maintaining data platforms

  Developing data analytics software Teaching/training others Planning large software projects or data systems Communicating with people outside your company

  Developing dashboards Organizing and guiding team projects ETL Collaborating on code projects (reading/editing others' code, using git)

  Implementing models/algorithms into production Identifying business problems to be solved with analytics Developing prototype models Feature extraction

  Creating visualizations

Data cleaning

Communicating findings to business decision-makers Conducting data analysis to answer research questions

  Basic exploratory data analysis RESPONDENTS COUNTED IF THEY SAID THEY HAVE "MAJOR INVOLVEMENT" IN THIS TASK TASKS

TASKS SALARY MEDIAN AND IQR*

  Basic exploratory data analysis Conducting data analysis to answer research questions Communicating findings to business decision-makers

Data cleaning

  Creating visualizations Feature extraction Developing prototype models Identifying business problems to be solved with analytics

  Implementing models/algorithms into production Collaborating on code projects (reading/editing others' code, using git) ETL ask T

  Organizing and guiding team projects Developing dashboards Communicating with people outside your company Planning large software projects or data systems

  Teaching/training others Developing data analytics software Setting up/maintaining data platforms Developing products that depend on real-time data analytics

  Using dashboards and spreadsheets (made by others) to make decisions €0K €20K €40K €60K €80K €100K Range/Median (Euro)

  

2017 EUROPEAN DATA SCIENCE SALARY SURVEY

Coding and Meetings

FOR TWO BROADER TASKS, coding and attending meetings,

  we asked respondents for more detail: namely, how much time they spend on them. As we have consistently seen, attending meetings correlates with salary: respondents who spend over 20 hours per week in meetings earn more than those who spend 9–20 hours, who in turn earn more than those whose spend 4–8 hours per week in meetings, and so on. This is unlikely to be a direct causal relationship, but rather both are effects of a shared cause (such as working in management). As for coding, the highest earners were those who don’t code at all, but that’s because they tended to be managers. There is a dip in salaries among respondents who code over 20 hours per week, but this is explained by the fact that this group was, on average, less experienced than the rest of the sample. Within the middle groups—those who code 1–20 hours per week—there was not much variation in pay.

TIME SPENT CODING SHARE OF RESPONDENTS

TIME SPENT IN MEETINGS SHARE OF RESPONDENTS

  1 TO 3 HOURS / WEEK 2% NONE

  1 TO 3 HOURS / WEEK 9% NONE

  4 TO 8 HOURS / WEEK 10%

  9 TO 20 HOURS / WEEK 23%

  36%

  23% OVER 20 HOURS / WEEK

SALARY MEDIAN AND IQR (EUROS)

SALARY MEDIAN AND IQR (EUROS)

  Hour s C oding

  Range/Median

  9 TO 20 HOURS / WEEK 43%

  23%

  3% OVER 20 HOURS / WEEK

  Hour s in Meetings Range/Median

  4 to 8 hours / week 1 to 3 hours / week None

  €20K €40K €60K €80K €100K €120K Over 20 hours / week 9 to 20 hours / week

  4 to 8 hours / week 1 to 3 hours / week None

  €0K €20K €40K €60K €80K €100K €120K Over 20 hours / week 9 to 20 hours / week

  4 TO 8 HOURS / WEEK 29%

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Salary Change

AN ALTERNATIVE METRIC TO CURRENT SALARY is the A final question asked respondents about the next step they

  amount that one’s salary changed in the last three years. Most would like to take in their career. The top response was “learn respondents’ salaries grew at least a little in the last three years, new technology/skills” and respondents who gave this answer and about a third of the sample saw tended to be less experienced (5.5 their wages rise by 50% or more over years on average) and have smaller this period. This latter group tended to Most respondents’ salaries salaries (€40K median) than the rest be less experienced, with an average of of the sample.

  

grew at least a little in the

  4.4 years of experience (compared to Respondents who said they would

  7.6 years among those whose salaries

  

last three years

  like to move into leadership roles did not grow by 50% or more). had salaries far above average

  For Spark/Hadoop and Python-only (€65K median). The other top users, we use the tool-defined groups from page 8. They responses were “work on more interesting/important were most likely to have had 50% or more wage growth projects,” “switch companies,” and “start your own (40% and 44% of them did, respectively). Respondents who company”. Respondents who work in the healthcare did not use Hadoop, Python, or R (the “SQL/Excel” group) industry were far more likely to choose “switch companies” were the least likely: only 19% of them reported a 50% rise (33%) than respondents from other industries (11%). in their salaries.

PERCENTAGE CHANGE IN SALARY

  6%

  • 20% TO +30%

OVER LAST THREE YEARS SHARE OF RESPONDENTS

  7%

  • 30% TO +40%

  5%

  • 40% TO +50%

  11%

  • +10% TO +20%

  6%

  • 0% TO +10%

  11%

  • 50% TO +75%

  17%

NO CHANGE

  5%

  • 75% TO +100% (DOUBLE)

  7%

NEGATIVE CHANGE

  10%

  • 100% TO +200% (TRIPLE)

  6%

OVER TRIPLE

  7%

N//A (SALARY WAS ZERO)

  2017 EUROPEAN DATA SCIENCE SALARY SURVEY Conclusion

  software costs, but labor expenses as well. We hope that

  THE PURPOSE OF OUR SALARY SURVEYS and the

  the information in this report will aid the task of building reports based on them is to provide an annual, data-driven estimates for such decisions. snapshot of how much professionals in your field make, and to expose details of their

  If you made use of this report, work and career. There are please consider taking the online

  

Business leaders choosing

  plenty of resources out there survey. Every year, we work to that can give an idea of how build on the last year’s report,

  

technologies need to consider

  much a data scientist can and much of the improvement

  

not just the software costs, but

  expect to earn or which comes from increased sample software tools are on the rise, sizes. This is a joint research

labor expenses as well.

but there aren’t many places effort, and the more interaction where these data points are we have with you, the deeper integrated into one report. we will be able to explore the data science space in Europe.

  Thank you! This information isn’t just for employees, either. Business leaders choosing technologies need to consider not just the

  We need your data.

  To stay up to date on this research, your participation is critical. The survey is now open for the 2017 report, and if you can spare just 10 minutes of your time, we encourage you to take the survey. How do data science salaries for people in Europe compare to their counterparts in the rest of the world? Among the more than 1000 people who responded to O’Reilly’s 2016 Data Science Salary Survey, 359 live and work in various European countries as data scientists, analysts, engineers, and related professions.

  This report takes a deep dive into the survey results from respondents in various regions of Europe, including the tools they use, the compensation they receive, and the roles they play in their respective organizations. Even if you didn’t take part in the survey, you can still plug your own information into the survey’s simple linear model to see where you fit. n With this report, you’ll learn: n How salaries vary by country and specific regions in Europe n The average size of the companies respondents work for, according to region n

How a respondent’s salary is affected by their country’s gross domestic product

  

The type of industry they work for, including software, banking and finance, and

n retail and ecommerce Which tools are most commonly used vs the tools used by respondents with n above-average salaries The major and minor tasks that respondents perform John King is a data scientist at O’Reilly Media. Having previously worked on survey-based sociolinguistic research in the Republic of Georgia, he now runs surveys at O’Reilly, using the results not just for internal use but also to share his findings with the public.

  Roger Magoulas is Director of Research for O’Reilly Media.

  To stay up to date on this research, your participation is

ISBN: 978-1-491-97750-7

  crucial. The survey is now open for the 2017 report; please take just 5 to 10 minutes to participate in the survey oreilly.com/ideas/take-the-2017-data-science-salary-survey

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PENGARUH PENERAPAN MODEL DISKUSI TERHADAP KEMAMPUAN TES LISAN SISWA PADA MATA PELAJARAN ALQUR’AN HADIS DI MADRASAH TSANAWIYAH NEGERI TUNGGANGRI KALIDAWIR TULUNGAGUNG Institutional Repository of IAIN Tulungagung

17 292 43

PENGARUH PENERAPAN MODEL DISKUSI TERHADAP KEMAMPUAN TES LISAN SISWA PADA MATA PELAJARAN ALQUR’AN HADIS DI MADRASAH TSANAWIYAH NEGERI TUNGGANGRI KALIDAWIR TULUNGAGUNG Institutional Repository of IAIN Tulungagung

13 233 23

PENGARUH PENERAPAN MODEL DISKUSI TERHADAP KEMAMPUAN TES LISAN SISWA PADA MATA PELAJARAN ALQUR’AN HADIS DI MADRASAH TSANAWIYAH NEGERI TUNGGANGRI KALIDAWIR TULUNGAGUNG Institutional Repository of IAIN Tulungagung

2 165 24

PENGARUH PENERAPAN MODEL DISKUSI TERHADAP KEMAMPUAN TES LISAN SISWA PADA MATA PELAJARAN ALQUR’AN HADIS DI MADRASAH TSANAWIYAH NEGERI TUNGGANGRI KALIDAWIR TULUNGAGUNG Institutional Repository of IAIN Tulungagung

14 226 23

KREATIVITAS GURU DALAM MENGGUNAKAN SUMBER BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN PENDIDIKAN AGAMA ISLAM DI SMPN 2 NGANTRU TULUNGAGUNG Institutional Repository of IAIN Tulungagung

14 305 14

KREATIVITAS GURU DALAM MENGGUNAKAN SUMBER BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN PENDIDIKAN AGAMA ISLAM DI SMPN 2 NGANTRU TULUNGAGUNG Institutional Repository of IAIN Tulungagung

11 288 50

KREATIVITAS GURU DALAM MENGGUNAKAN SUMBER BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN PENDIDIKAN AGAMA ISLAM DI SMPN 2 NGANTRU TULUNGAGUNG Institutional Repository of IAIN Tulungagung

5 164 17

KREATIVITAS GURU DALAM MENGGUNAKAN SUMBER BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN PENDIDIKAN AGAMA ISLAM DI SMPN 2 NGANTRU TULUNGAGUNG Institutional Repository of IAIN Tulungagung

7 298 30

KREATIVITAS GURU DALAM MENGGUNAKAN SUMBER BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN PENDIDIKAN AGAMA ISLAM DI SMPN 2 NGANTRU TULUNGAGUNG Institutional Repository of IAIN Tulungagung

14 329 23