3. About you
You already have a career in data
I'm interested in switching into a data career
I just want to see what all the fuss is about
4. About Thinkful
Thinkful helps people become developers or data
scientists through 1-on-1 mentorship and project-based
learning
These workshops are built using this approach.
5. Today's Goals
What is Data Science?
How and why has the field emerged?
What do they do?
Next steps
6.
7.
8.
9. Example: 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
10. Enter: Data Scientist
Jonathan Goldman
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. Other Examples
Uber — Where drivers should hang out
Tala — Microfinance loan approval
13. Why now?
Big Data: datasets whose size is
beyond the ability of typical database
software tools to capture, store,
manage, and analyze
14. Brief history of "big data"
Trend "started" in 2005
Web 2.0 - Majority of content is created
by users
Mobile accelerates this — data/person
skyrockets
15. Big Data
90% of the data in the world
today has been created in the
last two years alone
- IBM, May 2013
19. Data Science is just the beginning
“The United States alone faces a shortage
of 140,000 to 190,000 people with deep
analytical skills as well as 1.5 million
managers and analysts to analyze big
data and make decisions based on their
findings.”
- McKinsey
20. The Process - LinkedIn Example
Frame the question
Collect the raw data
Process the data
Explore the data
Communicate results
21. Case: Frame the Question
What questions do we want to answer?
22. 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?
23. Case: Collect the Data
What data do we need to answer these
questions?
24. Case: Collect the Data
Connection data (who is who connected to?)
Demographic data (what is the profile of the
connection)
Engagement data (how do they use the site)
25. Case: Process the Data
How is the data “dirty” and how can we clean
it?
26. Case: Process the Data
User input
Redundancies
Feature changes
Data model changes
27. Case: Explore the Data
What are the meaningful patterns in the
data?
28. Case: Explore the Data
Triangle closing
Time overlaps
Geographic overlaps
36. #3: Machine Learning Algorithms
Machine learning algorithms provide
computers with the ability to learn
without being explicitly programmed —
“programming by example”
42. But if you're interested...
Knowledge of statistics, algorithms, &
software
Comfort with languages & tools (Python,
SQL, Tableau)
Inquisitiveness and intellectual curiosity
Strong communication skills
It’s all Teachable!
45. 92%of grads placed in full-time tech jobs
job guarantee
Link for the third party audit jobs report:
https://www.thinkful.com/outcomes
Thinkful's track record of getting students jobs
46. Our students receive unprecedented support
1-on-1 Learning Mentor
1-on-1 Career MentorProgram Manager
San Diego Community
You
47. 1-on-1 mentorship enables flexible learning
Learn anywhere,
anytime, and at your
own schedule
You don't have to quit
your job to start career
transition
48. Thinkful's Free Resource
Introduction to Python, Data
Visualization, and Stats.
Unlimited mentor-led Q&A sessions
Personal Program Manager
bit.ly/tf-ds-free-
course
