The document discusses the rise of data science and the skills needed for data scientists. It defines data science as the intersection of engineering, statistics, and communication. Data scientists analyze large datasets to answer important business questions. The document uses LinkedIn in 2006 as a case study, outlining how a data scientist there framed questions, collected and processed user data, explored patterns, and communicated results to improve the user experience and growth. It highlights tools like SQL, analytics software, and machine learning that data scientists use and stresses the importance of curiosity, technical skills, and strong communication for those interested in the field.

1. Data Science:
How did we get here and where are we going?
April 2017
http://bit.ly/data-la
Wifi: CrossCamp.us Events

2. About us
We train developers and data
scientists through 1-on-1
mentorship and career prep

3. About me
• Noel Duarte
• Los Angeles Area General Manager
• UC Berkeley ’15 — worked primarily with R for population
genetics analysis, at Thinkful since January 2016

4. About you
Why are you here?
• I already have a career in data
• I’m curious about switching to a career in data
• I’m currently transitioning into a career in data
• I want to learn what data science is and why it’s
important

5. Today’s goals
• Why is data science important?
• What is a data scientist and what do they do?
• How and why has the field emerged?
• How can one become a data scientist? (And why
would you want to?)

6. Why is data science important?
By 2018, the United States alone could face a shortage
of 140,000 to 190,000 people with deep analytical skills
as well as 1.5 million managers and analysts with the
know-how to use the analysis of big data to make
effective decisions.
- McKinsey Global Institute (MGI)

7. Data Scientist: Definition #1

8. Data Scientist: Definition #2
Nate Silver
FiveThirtyEight.com
“I think data-scientist is a sexed up term for a
statistician”

9. Data Scientist: Definition #3

10. Case study: LinkedIn (2006)
“[LinkedIn] was like arriving at a conference reception
and realizing you don’t know anyone. So you just stand
in the corner sipping your drink—and you probably
leave early.”
-LinkedIn Manager, June 2006

11. The new guy
• Joined LinkedIn in 2006,
only 8M users (450M in
2016)
• Started experiments to
predict people’s networks
• Engineers were dismissive:
“you can already import
your address book”

12. The result

13. Data, data everywhere 🚀
• Uber — Where drivers should hang out
• Netflix — movie recommendations
• Ebola epidemic — Mobile mapping in Senegal to
fight disease

14. Data, data everywhere 🚀

15. Big Data — what exactly does it mean?
Big Data: datasets whose size is beyond the ability of
typical database software tools to capture, store,
manage, and analyze

16. Big Data — brief history
• Trend “started” in 2005 (Hadoop!)
• Web 2.0 - Majority of content is created by users
• Mobile accelerates this — data/person skyrockets

17. Big Data — 3 Vs

18. Big Data — tldr;
90% of the data in the world today has been created
in the last two years alone.
- IBM, May 2013

19. In come data scientists!

20. Intersection of engineering, statistics, & communication

21. The data science process
Let’s come back to LinkedIn’s evolution in 2006 and
examine it using a typical* data science approach.
• Frame the question
• Collect the raw data
• Process the data
• Explore the data
• Communicate results

22. Case: Frame the question
What questions do we want to answer?

23. Case: Frame the question
• What connections (type and number) lead to higher
user engagement?
• Which connections do people want to make but are
currently limited from making?
• How might we predict these types of connections
with limited data from the user?

24. Case: Collect the data
What data do we need to answer these questions?

25. Case: Collect the data
• Connection data (who is who connected to?)
• Demographic data (what is profile of connection?)
• Retention data (how do people stay or leave?)
• Engagement data (how do they use the site?)

26. Case: Process the data
How is the data “dirty” and how can we clean it?

27. Case: Process the data
• User input
• Redundancies
• Feature changes
• Data model changes

28. Case: Explore the data
What are the meaningful patterns in the data?

29. Case: Explore the data
• Triangle closing
• Time overlaps
• Geographic clustering

30. Case: Communicate results
How do we communicate this? To whom?

31. Case: Communicate results
• Tell story at the right technical level for each audience
• Make sure to focus on Whats In It For You (WIIFY!)
• Be objective, don’t lie with statistics
• Be visual! Show, don’t just tell

32. Tools to explore “big data”
• SQL Queries
• Business Analytics Software
• Machine Learning Algorithms

33. Tool #1: SQL queries
SQL is the standard querying language to access and
manipulate databases

34. SQL example
friends
id full_name age
1 Dan Friedman 24
2 Jared Jones 27
3 Paul Gu 22
4 Noel Duarte 73
SELECT full_name FROM friends WHERE age=73

35. Tool #2: Analytics software
Business analytics software for your database enabling
you to easily find and communicate insights visually

36. Tableau example

37. Tool #3: Machine Learning Algorithms
Machine learning algorithms provide computers
with the ability to learn without being explicitly
programmed — “programming by example”

38. Iris data set example

39. Iris data set example

40. Use cases for machine learning
• Classification — Predict categories
• Regression — Predict values
• Anomaly Detection — Find unusual occurrences
• Clustering — Discover structure

41. If this excites you…

42. This is what you’ll need
• Knowledge of statistics, algorithms, & software
• Comfort with languages & tools (Python, SQL,
Tableau)
• Inquisitiveness and intellectual curiosity
• Strong communication skills

43. Ways to keep learningLevelofsupport
Structure efficiency

44. 1-on-1 mentorship enables flexibility
325+ mentors with an average of 10
years of experience in the field

45. Support ‘round the clock

46. Want to try us/data science out?
• Three-week program,
includes six mentor
sessions for $250
• Overview of Python,
Python’s data science
toolkit, stats
• Option to continue into
full data science
bootcamp
• Talk to me (or email
noel@thinkful.com) if
you’re interested

47. October 2015
Questions?
noel@thinkful.com