This document discusses an introductory session on data science. It begins with introductions and an outline of the session's goals, which are to define what a data scientist is, how the field has emerged, and how to become one. It then discusses the growing demand and high salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn, Netflix, and for fighting Ebola. Key aspects of data science like big data, Hadoop, MapReduce, and machine learning algorithms are explained. The document concludes by discussing the data science process and tools used, and encourages the audience that it is possible for them to become data scientists with the right knowledge, skills, and learning approach.

1. June 2017
Intro to Data Science

2. Me
• Rahul Singh
• Thinkful Data Science Mentor
• CEO @ Anant Corporation

3. You
Name
Your Data Background
Goals with tonight’s session

4. 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?

5. 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

6. Why do we care?
Also… average salaries are $115,000 a year

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

10. 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

11. 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

12. The Result

13. Other Examples
Uber — Where drivers should hang out
Netflix — $1M movie recommendations
contest
Ebola — Mobile mapping in Senegal to fight
disease

14. Big Data
Big Data: datasets whose size is beyond the
ability of typical database software tools to
capture, store, manage, and analyze

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

16. Hadoop?
HDFS
MapReduce

17. Hadoop Distributed File System
File is too big….Distribute!
Too many files….Distribute!
Yahoo has over 10,000 servers running
Hadoop

18. MapReduce
Data + Processing Software
Distributed Processing
Map all of the data, reduce it

19. MapReduce

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

21. Big Data

22. Data Scientists - We Can Be Heroes

23. Data Scientists - Jack of all Trades

24. The Process - LinkedIn Example
Frame the question
Collect the raw data
Process the data
Explore the data
Communicate results

25. Case: Frame the Question
What questions do we want to answer?

26. 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?

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

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

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

30. Case: Process the Data
User input - 80/20
Redundancies - 2 emails
Feature changes
Data model changes

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

32. Case: Explore the Data
Triangle closing
Time overlaps
Geographic clustering

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

34. Case: Communicate 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

35. Tools
SQL Queries
Business Analytics Software
Machine Learning Algorithms

36. #1 - SQL Queries
SQL is the standard querying language
to access and manipulate databases

37. #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

38. #2: Visualization Software
Business analytics software for your database
enabling you to easily find and communicate
insights visually

39. #2: Visualization Software

40. #3: Machine Learning Algorithms
Machine learning algorithms provide computers
with the ability to learn without being explicitly
programmed — “programming by example”

41. #3: Machine Learning Algorithms

42. Iris Data Set

43. Iris Data Set

44. Iris Data Set
?

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

46. It’s not easy but someone has to do it

47. That someone might be you
Knowledge of statistics, algorithms, &
software
Comfort with languages & tools (Python,
SQL, Tableau)
Inquisitiveness and intellectual curiosity
Strong communication skills
It’s all Teachable!

48. Ways to keep learningLevelofsupport
Learning methods

49. 1-on-1 mentorship enables flexibility
325+ mentors with an average of 10
years of experience in the field

50. Support ‘round the clock

51. Our results
Job Titles after GraduationMonths until Employed

52. 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
tj@thinkful.com) if you’re
interested