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. With this report, you’ll learn: How salaries vary by country and specific regions in Europe Average size of companies by region How salary is affected by a country’s GDP Top industries for data scientists, including software, banking, finance, retail, and ecommerce Most commonly used tools vs tools used by respondents with above-average salaries Primary and secondary job tasks performed by survey respondents To stay up-to-date on this research, your participation is crucial. The survey is now open for the 2017 report; please take just 5 to 10 minutes to participate in the survey here.

In this fourth edition of O’Reilly’s Data Science Salary Survey, 983 respondents working across a variety of industries answered questions about the tools they use, the tasks they engage in, and the salaries they make. This year’s survey includes data scientists, engineers, and others in the data space from 45 countries and 45 US states. The 2016 survey included new questions, most notably about specific data-related tasks that may affect salary. Plug in your own data points to the survey model and see how you compare to other data science professionals in your industry. With this report, you’ll learn: Where data scientists make the highest salaries—by country and by US state Tools that respondents most commonly use on the job, and tools that contribute most to salary Two activities that contribute to higher earnings among respondents How gender and bargaining skills affect salaries when all other factors are equal Salary differences between those using open source tools vs those using proprietary tools Salary differences between those who rely on Python vs those who use several tools Participate in the 2017 Survey The survey is now open for the 2017 report. Spend just 5 to 10 minutes and take the anonymous salary survey here: https://www.oreilly.com/ideas/take-the-2​017-data-science-salary-survey.

Early this year, more than 5000 software engineers, developers, and other programming professionals participated in O’Reilly Media’s first Software Development Salary Survey. Participants included professionals from large and small companies in a variety of industries across 51 countries and all 50 US states. With the complete survey results in this in-depth report, you’ll be able to explore the world of software development—and the careers that propel it—in great detail. With this report, you’ll learn: The top programming languages that respondents currently use professionally Where programmers make the highest salaries—by country and by regions in the US Salary ranges by industry and by specific programming language The difference in earnings between programmers who work on tiny teams vs those work on larger teams The most common programming languages that respondents no longer use in their work The most common languages that respondents intend to learn within the next couple of years Pick up a copy of this report and find out where you stand in the programming world. We encourage you to plug in your own data points to our survey model to see how you compare to other programming professionals in your industry.

For the third consecutive year, O’Reilly Media conducted an anonymous survey to expose the tools that successful data scientists and engineers use, and how those tool choices might relate to their salary. For the 2015 version of the Data Science Salary Survey, we heard from over 600 respondents who work in and around the data space for a variety of industries across 47 countries and 38 U.S. states. The research was based on data collected through an online 32-question survey, including demographic information, time spent on various data-related tasks, and the use or non-use of 116 software tools. Findings include: Download this free in-depth report to gain insight from these potentially career-changing findings, and plug your own variables into one of the linear models to predict your own salary. Average number of tools and median income for all respondents Distribution of responses by a variety of factors, including age, gender, location, industry, role, and cloud computing Detailed analysis of tool use, including tool clusters Correlation of tool usage and salary The survey is now open for the 2016 report, and it takes just 5 to 10 minutes to complete: http://www.oreilly.com/go/ds-salary-​survey-2016.

What tools do successful data scientists and analysts use, and how much money do they make? We surveyed hundreds of attendees at the O'Reilly Strata Conferences in Santa Clara, California and New York to understand. Findings from the survey include: Average number of tools and median income for all respondents Distribution of responses by age, location, industry, and position Detailed analysis of tools used by respondents and correlation to their salaries - including by tool clusters (Hadoop, SQL/Excel, and other) Correlation of specialized big data tools usage and salary What tools should you be learning and using? Read this valuable report to gain insight from these potentially career-changing findings.