Different Career Paths in Data Science
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The document provides an overview of different career paths in data science, including data scientist, data engineer, and data analyst roles. It summarizes the typical job duties, skills required, tools used, and average salaries for each role. Additionally, it notes the large and growing demand for data science professionals, with over 215,000 open jobs in the US as of January 2017 and top hiring locations of San Francisco, New York, and Seattle.
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Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
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QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
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- Quantum Computing QRG
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Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
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Welcome & Introductions
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Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
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This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
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- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
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Different Career Paths in Data Science
- 1. Data Science Career Paths Overview & Comparison
- 2. The world produces 2,500,000,000, 000,000,000+ bytes of data every day, that’s equal to 10 million Blu-Ray disks stacked the height of 4 Eiffel Towers. Source: http://www.vcloudnews.com/every-day-big-data- statistics-2-5-quintillion-bytes-of-data-created-daily/
- 3. A Data Scientist solves problems with large amounts of data. Example: Analyze tweets to figure out whether or not a tweet sent to a company is positive or negative.
- 4. The US alone will face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers
- 5. What makes Springboard unique?3 common roles in Data Science
- 7. Data Scientist Also called Statisticians, Data Managers
- 8. A Data Scientist will be able to take data science projects from end to end. They can help store large amounts of data, create predictive modeling processes and tell stories about the findings Data Scientist
- 9. Average salary $USD 115,000 Data Scientist Skills needed Mathematics, Programming, Communication Tools used SQL, Python, R
- 10. Data Engineer Also called Data Architects
- 11. Data Engineers are versatile generalists who create data pipelines to help process large amounts of data. They typically focus on coding, cleaning up data sets, and implementing requests that come from Data Scientists. Data Engineer
- 12. Average salary $USD 100,000 Data Engineer Skills needed Programming, Mathematics, Big Data Tools used Hadoop, NoSQL, Python
- 13. Data Analyst Also called Business Analysts
- 14. A Data Analyst helps people from across the company understand data insights they’ve unearthed. Data Analyst
- 15. Average salary $USD 65,000 Data Analyst Skills needed Statistics, Communication, Business Knowledge Tools used Excel, Tableau, SQL
- 16. How do the roles connect? Inspired by Drew Conway
- 18. ~215,000 Open job positions Data from Indeed.com, January 2017
- 19. Top 3 hiring locations in the U.S. San Francisco New York Seattle
- 20. What makes Springboard unique?Data Science Profiles
- 21. DJ Patil Data Scientist As the former Chief Data Scientist of the United States Office of Science and Technology Policy, DJ is the perfect prototype of the Data Scientist.
- 22. Doug Cutting, Data Engineer The creator of Hadoop and a member of Apache’s Board of Directors is a model of the data engineer who builds tools to process big data.
- 23. Roger Huang, Data Analyst The humble author of this piece, while nowhere near as talented as the two individuals referenced above, did serve a brief stint as a data analyst for a pharmaceutical company.
- 24. Data science is a new and exciting field filled with lucrative career paths. Curious to learn more? Check out Data Science Career Track!
- 25. Ready for the next step? Read more at www.springboard.com/blog/data-science-career-paths- different-roles-industry