Brian Spiering, a faculty member at the University of San Francisco's MS in Data Science, provides practical advice on how best to navigate the seemingly unlimited choices. He covers how to learn programming skills you'll need, how much Machine Learning is enough, and how to develop the necessary communication skills.

1. Starting A Career in
Data Science

2. 1. Introductions
2. The ABCs of Data Science
3. Be a meaningful specific
4. How to conduct a successful job search
5. FAQs
6. Q & A
Agenda for Tonight

3. Who Am I?
Brian Spiering
• Formerly, an academic Computational Neuroscientist.
• Formerly, an industry Data Scientist.
• Currently, faculty at University of San Francisco’s MS in Data Science program.

4. Who Are You?
By a show of hands…
• Who is looking for their first professional job?
• Who is making a career transition?
• Who feels like they lack probability or statistics skills?
• Who feels like they like lack ML / DL / AI skills?
• Who feels like they lack programming skills?
• Who feels like they lack communication or soft-skills?

5. A combination of quantitative analysis,
advanced modeling, and domain expertise.
What is Data Science?

6. Artificial Intelligence
Big Data
Cloud
The ABCs of Data Science

7. Artificial Intelligence (AI)

9. The Cloud

13. How to conduct
a job search
It is all about them!

17. 1
5
Job Search Example

19. Self Reflection
• Do I have a standard resume?
• Is it free of errors?
• Is it attractive to employers?
• Is it understandable by both technical and non-technical people?

20. Brian J. Spiering, Ph.D.
805.637.8248 • bspiering@gmail.com • San Francisco, CA
SUMMARY
• Machine Learning, Natural Language Processing (NLP), and Deep Learning expert
• Data Science experience, especially founding and growing data teams
EXPERIENCE
University of San Francisco
Assistant Professor, Jan 2018 – Present
• Teach Data Science and Computer Science courses in Natural Language Processing, Machine
Learning, Artificial Intelligence, and Computer Programming
• Director of Peer-to-Peer Computer Science Tutoring Center (Spring 2018)
• Career Services lead for ~90 Masters in Data Science students (Fall 2018-ongoing)
Indigo Project
Machine Learning Engineer, May 2017 – Dec 2017
• Founding technical member of an Artificial Intelligence (AI)-first stealth mode startup
• Architected and coded a distributed Deep Learning system to automatically correct grammar and
rewrite sentences.
• Researched and developed cutting-edge solutions for Natural Language Processing, Understanding,
and Generation (respectively – NLP, NLU and NLG).
Galvanize / University of New Haven
Data Science Professor, June 2015 – Dec 2017
• Early team member to reimagine education with industry-align curriculum and cutting-edge teaching
techniques
• Taught graduate-level Data Science curriculum. Specializing in Natural Language Processing (NLP),
Machine Learning, Data Engineering, Deep Learning, and Artificial Intelligence (AI).
• Mentored students and helped them connect the dots between Data Science and business.
Supervised work with industry partners.
• Won "Teacher of the Year" award for 2017
LiveCareer (now BOLD)
Data Scientist, Aug. 2014 – June 2015
• As the first Data Scientist at LiveCareer, defined the vision for data science. Built and led a team
to realize that vision.
• Company-wide technical lead on data analysis, applied Deep Learning, search engineering,
information retrieval, building a Big Data platform, and creating data-driven back-end services.
• Architected and coded Machine Learning and NLP data pipelines.
TECHNICAL
Languages & Technologies: Python, Scala, R, SQL, TensorFlow, PyTorch, Spark, Hadoop, AWS
Coding examples: github.com/brianspiering
EDUCATION
Ph.D. in Cognitive Neuroscience University of California, Santa Barbara
B.A. in Psychology & Speech Communication San Francisco State University
Eligible to work in the US without sponsorship

22. Self Reflection
• Can I communicate in a business setting?
• Can I describe myself and my accomplishments?
• Can I talk to both technical and non-technical people?

23. Business Communication 101
• KISS - Keep It Simple, Silly
• Short, declarative statements
• Use bullet points
• Use business / domain jargon (Don’t use technical jargon)
• Use images and figures
• Tell stories

24. Self Reflection
• Do I process general tech skills?
• Do I possess current, in-demand data science skills?
• Can I apply them to solve valuable business problems?

25. Technical Interviews
vs.
On The Job
Technical interviews require a unique set of skills
that are only tangentially related to
performance on the job.

26. Technical interviews are like coding contests.
Treatment them as such:
• Learn the top tricks (hash-maps & dynamic programming)
• Memorize common questions
• Practice in the same style
• Be fluent (in your thinking, explaining, and answering)

27. FAQs

28. What are the different careers
in Data Science?
• Data Scientist
• Data Analyst
• Business Intelligence Analyst
• Research Scientist
• Machine Learning Engineer
• Data Engineer
• Data Architect

31. Does a Data Engineer need
to know Data Science?
Data Engineering is the box that Data Science happens in,
thus the bigger and better the box the better the Data Science.

32. Do I need a PhD to become
a Data Scientist?
No.
However, there is massive credential inflation in Data Science.

33. How can get into Data Science
if I don't have a PhD or a Masters?
1. Find a specific domain or an emerging trend where lack of experience is
not an issue.
2. Researched it obsessively.
3. Once you have built a knowledge base, advertise your skills and attracted
opportunities by sharing knowledge on the internet.

34. How can I switch careers to
Data Science?
Are you lacking the broad skills or deep expertise?

35. Q & A

36. If you are considering a MS in Data Science,
please take a look at:
Learn more here

38. Bonus
Data Scientist: The Definitive Guide to Becoming a Data Scientist
by Zacharias Voulgari