2. About me
• Jasjit Singh
• Worked in finance & tech
• Co-Founder Hotspot
• Thinkful General Manager
3. About us
Thinkful prepares students for web development &
data science jobs with 1-on-1 mentorship programs
4. About you
•I already have a career in data
•I’m serious about switching into a career in data
•I’m curious about switching into a career in data
•Ugh I just want to see what all the fuss is about
•Data is my favorite character in Star Trek
5. Today’s goals
•What is a data scientist and what do they do?
•How and why has the field emerged?
•How can one become a data scientist?
6. Agenda for tonight
• What is the market landscape for dev jobs?
• What programming language should I learn?
• What are the best ways to learn to code?
• What are the first jobs / trajectories?
• How do I break into the field?
7. Why do we care?
“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
12. 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
13. The new guy
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”
15. Other examples
•Uber — Where drivers should hang out
•Netflix — $1M movie recommendations contest
•Ebola — Mobile mapping in Senegal to fight disease
16. “Big Data” changed the game
Big Data: datasets whose size is beyond the ability of
typical database software tools to capture, store,
manage, and analyze
17. Brief history of “Big Data”
•Trend “started” in 2005 (Hadoop!)
•Web 2.0 - Majority of content is created by users
•Mobile accelerates this — data/person skyrockets
25. 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?
27. 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)
33. Communicating the findings
•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
39. Tool #3: Machine learning algorithms
Machine learning algorithms provide computers with
the ability to learn without being explicitly
programmed — “programming by example”
44. 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
45. Data science bootcamp
Syllabus: Python Toolkit, Statistics & Probability,
Experimentation, Machine Learning, Communicating
Data, Algorithms and Big Data
46. More about Thinkful
• Anyone who’s committed can learn to code
• 1-on-1 mentorship is the best way to learn
• Flexibility matters — learn anywhere, anytime
• We only make money when you get a job
47. Our Program
You’ll learn concepts, practice with drills, and build capstone projects
for your own portfolio — all guided by a personal mentor
50. Special Prep Course Offer
• 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 me) if you’re interested
