BDW Chicago 2016 - Jennifer Boyce, Data Scientist, Sprout Social - The Road to Data Science Practical Advice for Aspiring Data Scientists

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The rise of big data has created a wealth of career opportunities in data science and predictive analytics. But how do you break into the field? Job descriptions include lengthy lists of skill sets, varying educational requirements, and wildly different responsibilities. Do you need everything on the list? Is a graduate degree essential? Where should you even start? This session offers practical, first-hand advice for aspiring data scientists.

Technology

The Road to Data Science
Practical Advice for Aspiring Data Scientists
Jen Boyce
Data Scientist, Sprout Social
@jenboyce

Data
Science
Math & Stats
Compute
r Science
Domain
Knowledge

Domain
Knowledge
• Existing knowledge
• What types of questions you
work on
• Variability of problems
• People to help
• Company resources
• Educational requirements
• Work environment

Math
&
Statistics
• Specificity of the questions
• Computational knowledge
• Existing analysis techniques
• Amount of time typically
dedicated to a problem

Computer
Science
• Technical skills
• Tools and technologies
• Data storage and quality
• Company infrastructure

Math &
Stats
Computer
Science
Domain
Knowledge

Choose Your Own Data Science
 Existing knowledge
 Types of questions you
work on.
 Variability of problems
 People to help
 Company resources
 Educational
requirements
 Work environment
Domain
Knowledge
Computer
Science
Math &
Stats
 Specificity of the
questions
 Computational knowledge
 Existing analysis
techniques
 Amount of time typically
dedicated to a problem
 Technical skills
 Tools and technologies
 Data storage and quality
 Company infrastructure

The Lucky Ones
“I want to work in the medical industry to analyze
health care costs.”
“I want to work for a company like Facebook to
build models that predict whether two individuals
attended the same school.”

Research Sources
Blogs Meetups
Company
Websites
Job Ads

Health Care List
• Experience with healthcare data and large
databases
• Advanced SAS, including SAS MACROS, Stored
Procedures, SAS/ACCESS to Hadoop, and the SQL
Pass Through Facility
• BS, BA, or Masters in Public Health, Epidemiology,
Statistics, Economics, Computer Science
• Experience with national healthcare performance
measures including HEDIS and PQRS a plus
• Experience with Unified Expression Language,
MicroStrategy, and HiveQL a plus

Facebook List
• Working in Hadoop and Hive primarily, sometimes
mySQL, Oracle, and Vertica
• Authoring pipelines via SQL and Python-based ETL
framework
• BA/BS in technical field. Advanced degrees
preferred.
• Development experience in at least one scripting
language (PHP, Python, Perl, etc.).
• Experience with a statistical package such as R,
MATLAB, SPSS, SAS, Stata, etc.

Domain
Knowledge
• Existing knowledge
Social media & marketing
• What types of questions
People-focused
• Variability of problems
Variety is good!
• People to help
I’ll need mentorship.
• Educational requirements
Grad school is okay.
• Work environment
Small/medium company
Team oriented

Math
&
Statistics
• Specificity of the questions
General: wide, not deep
• Computational knowledge
Moderate
• Existing analysis techniques?
Yes, please.
• Amount of time typically
dedicated to a problem
6-8 months max

Computer
Science
• Technical skills
Moderate
• Tools and technologies
No preference
• Data storage and quality
Decent quality
• Company infrastructure
Established team

How it added up
“I want to work as a generalist in the marketing or
startup world, for a small to medium sized business
with an existing data science team and established
technology infrastructure.”

Learning Plan
• Python, SQL
• Pandas and scikit-learn
• Spark/Hadoop
• Example machine learning projects:
• Content recommenders
• Churn prediction
• Customer segmentation
• Time series modeling
• Master’s degree- not required, but my choice

Jen Boyce
jenboyce@gmail.com
linkedin.com/in/jennboyce
@jenboyce

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