by Roger Magoulas | Publisher: O'Reilly Media, Inc. | Release Date: May 2016 | ISBN: 9781491957813

The 2015 O'Reilly Data Science Salary Survey provides insights that could significantly impact your career in data science. The video starts with a longitudinal look at what's common across the surveys O'Reilly's done over the last three years, then moves through the latest results and the process for gathering and analyzing the data. The Survey's research derives from data collected in 2015 through online surveys. It includes demographic information, time spent on various data-related tasks, and the use or non-use of 116 software tools. Data came from over 600 respondents working in a variety of industries, two-thirds of whom are based in the United States. See the industries data scientists work in and the tasks they work on Compare salaries paid to data scientists in the U.S., Asia, UK/Ireland, and the rest of Europe Compare salaries paid to data scientists in different regions of the U.S. See the salary range paid to female data scientists vs. male data scientists Learn how age, education level, and company size affect salary levels Discover the impact hours worked each week have on salary range Identify the data science tools that correlate to higher salariesRoger Magoulas is the director of market research at O'Reilly Media. Magoulas runs a team that is building an open source analysis infrastructure. He provides analysis services, including technology trend analysis, to business decision-makers at O'Reilly and beyond. In previous incarnations, Magoulas designed and implemented data warehouse projects for organizations ranging from the San Francisco Opera to the Alberta Motor Club.

1. The Research Behind the Data Science Salary Survey 00:11:43
2. Understanding the Audience 00:06:52
3. Exploring Demographics 00:05:42
4. The Relevance of How People Spend Their Time 00:07:43
5. The Impact of Certain Tasks on Salary 00:07:41
6. How Tool Clusters Correlate to Salary 00:07:51
7. Median Income, by Tool Use 00:02:53
8. Correlating Job Titles to Salary 00:03:55
9. Building the Statistical Model and the Effect of Variables 00:05:02