This document provides an overview of data science and how to get started in the field. It defines a data scientist as a jack-of-all-trades who frames questions, collects and processes data, explores patterns in the data, and communicates findings. The field has emerged due to big data and a need to make sense of large datasets. Learning data science involves skills like SQL, data visualization, and machine learning algorithms. Thinkful offers 1-on-1 mentorship and career preparation programs to help people transition into data science careers.

1. June 2017
Getting Started with Data Science

2. About me
• Jasjit Singh
• Self-taught developer
• Worked in finance & tech
• Co-Founder Hotspot
• Thinkful General Manager

3. About you
I 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 us
We train developers and data scientists
through 1-on-1 mentorship and career
prep programs

5. Today’s Goals
What is a data scientist?
How and why has the field emerged?
What do they do?
Next steps

7. Nate Silver
FiveThirtyEight.com
“I think data-scientist is a sexed up term for a statistician”

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
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”
Jonathan Goldman

11. The Result

12. Other Examples
Uber — Where drivers should hang out
Netflix — $1M prize for better
recommendations
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

16. The problem

17. The solution

18. Data Scientists - Jack of all Trades

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

29. Case: Communicate Findings
How do we communicate this? To whom?

30. Case: Communicate Findings
“People You Know” feature increased click-
through by 30% (generating X million more
page views)

31. Tools
SQL Queries
Business Analytics Software
Machine Learning Algorithms

32. #1 - SQL Queries
SQL is the standard querying language
to access and manipulate databases

33. #1 - SQL Queries
friends
id full_name age
1 Dan Friedman 24
2 Tyler Brewer 27
3 David Coulter 22
4 TJ Stalcup 33
SELECT full_name FROM friends WHERE age>22

34. #2: Visualization Software
Business analytics software for your database
enabling you to easily find and communicate
insights visually

35. #2: Visualization Software

36. #3: Machine Learning Algorithms
Machine learning algorithms provide computers
with the ability to learn without being explicitly
programmed — “programming by example”

37. Iris Data Set

38. Iris Data Set

39. Iris Data Set
?

40. Use Cases for Machine Learning
Classification — Predict categories
Regression — Predict values
Anomaly Detection — Find unusual occurrences
Clustering — Discover structure

41. It may seem like a daunting opportunity

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!

43. Learning data science with ThinkfulLevelofsupport
Learning methods

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. Our results
Job Titles after GraduationMonths until Employed

47. Try us out!
• Initial 3-week prep course
includes six mentor sessions
for $250
• Learn Python, Python’s data
science toolkit, Statistics intro
• Option to continue onto Data
Science bootcamp
• Talk to me (or email
jas@thinkful.com) if you’re
interested