• Udacity blog by Dave Holtz: “8 Skills You Need to Be a Data Scientist”
• Insight blog by Emily Thompson: “Academia to Industry: Data Science Myths and Truths”
• DataCamp article by Karlijn Willems: “The Data Science Industry: Who Does What (Infographic)”
• Forbes article by Rawn Shah: “Data Science Falls Into Many Roles”
  • This article also discusses the salary ranges for different types of data scientist positions

An effort by the National Institute of Health (NIH) to train a biomedical data science workforce, called the Big Data to Knowledge (BD2K) training program, was created to bridge the gap between biomedical training and the increasing need for biomedical data science skills. Read more about their efforts in this PLoS Biology article.

Analytics Manager

Big Data Engineer

Business:

• Analyst
• Analytics Specialist
• Intelligence Engineer
• Intelligence Solutions Architect
• Intelligence Specialist

Data:

• Analyst
• Architect
• Engineer
• Scientist
• Visualization Developer

Data and Analytics Manager

Database:

• Administrator
• Specialist

Machine Learning:

• Engineer
• Specialist

Statistician

References:

• RCR Wireless
• Data Science Central
• KDnuggets
• Sisense
• KDnuggets Infographic

Good question! Find out by completing an assessment.

• If you're in basic or biomedical sciences, use AAAS's My Individual Development Plan Assessment Tool, which OCPD co-created. Answer three assessments and it will calculate and compare your skills/interests/values responses to key factors for 60 different career paths within 20 different career categories that are options for you as a scientist.
• If you're in social or population sciences, use the ImaginePhD Skills, Interest, and Values Assessments. Make an account for free and then click the Assessments tab at the top. They will match your skills, interests, and values to families of jobs and provide resources for learning more about the jobs in the families that are best suited to you.

Come to our event to hear PhDs in data science careers talk about how their values played into their career decisions and help them find satisfaction in their current roles. You'll reflect on your own values and then can ask follow-up questions to determine whether work this field aligns with your values.

• Insight blog by Douglas Mason with Valerie Bisharat: “PhD to Data Scientist: 5 tips for making the transition”
• NatureJobs Blog post by Jack Leeming: “From Doctorate to Data Science: A very short guide”
• NatureJobs Blog post by Catherine De Lange: “So you want to be a data scientist?”

• National Institutes of Health (NIH)
• Central Intelligence Agency (CIA)
• Research institutions such as the Mayo Clinic, UCSF, and University of Michigan
• Biotech companies such as AstraZeneca, Bigfoot Biomedical, and Leidos
• Stripe
• Consulting firms
• Organizations work with consumers that also have large amounts of data:
  • Social Networking companies (Facebook, LinkedIn, Twitter), Gaming (Kixeye, Kabam), Travel (Airbnb, Uber, 99.co), Apparel (Stitch Fix, Gilt, Trunk Club), Internet security (Vectra, Cisco)
  • Education companies (Khan Academy, Creative Live, Remind)
  • References:
    • Udacity
    • Insight Data Science

Courses/training in data science:

• UCSF Data Science Initiative- They host workshops and events at UCSF teaching Python, Tableau, R, Machine Learning, and other useful data science related techniques. They host many different events each month.
• Gladstone Data Science Training Program
• Insight Data Science Fellows Program
• The Data Incubator Data Science Fellowship
• Coursera Data Science specialization course
• Science to Data Science program
• Innovation labs supported by the BD2K Training Coordination Center
• Insight Data Product Management Fellows Program

Career development award supported by the BD2K program

• UCSF Data Science Initiative and newsletter
• Meetup groups:
  • Bay Area Women in Machine Learning and Data Science
  • SF Data Science
  • Data Science Professional Development San Francisco
  • Data Science search
• Women in Data Science conference from Stanford
• Data Science Central Newsletter
• NIH Big Data to Knowledge BD2K
• SF Data
• Data Science Trek
• Bits in Bio

Check out how employers hire/make decisions:

• First Round Review by Jeremy Stanley and Daniel Tunkelang: “Doing Data Science Right — Your Most Common Questions Answered”
• First Round Review by Jeremy Stanley: “How to Consistently Hire Remarkable Data Scientists”

Advice for landing the job:

• Trey Causey: “What it's like to be on the data science job market”
• Erin Shellman: “Crushed it! Landing a data science job”