1) The document introduces an introductory session on learning Python for data science. It provides the WiFi password and a link to more information. 2) It discusses Thinkful's online bootcamp program and 1:1 mentoring approach for learning data science skills like Python through hands-on projects. 3) The session will cover what data science is, an introduction to Python, and how to use it for a real-world project while learning how to learn more.

1. Intro to Python for DSIntro to Python for DS
2 birds. One stone.
WIFI: MakeO ces 5Ghz
Password: Internet!23
http://bit.ly/thinkful-dc-python

2. TJ Stalcup
Lead DC Mentor @Thinkful
API Evangelist @540
Pokemon Master
About UsAbout Us

3. What's your name?
What do you do?
Why are you interested in data science or python?
About youAbout you

4. Online Bootcamp since 2012. We have worked
with over 6000 students around the world
paired up with over 400 mentors.
1:1 Mentoring, learn by building projects and
turn YOUR ideas into portfolio pieces
Guaranteed
About ThinkfulAbout Thinkful
Local DC Crew

5.
Learn why DS is a thing
What is Python
How do we use it with a real world project?
How do I learn more?
TONIGHT: Learn Python by DoingTONIGHT: Learn Python by Doing

6. What is a Data Scientist?What is a Data Scientist?

7. Example: LinkedIn 2006Example: 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

8. Enter: Jonathan GoldmanEnter: Jonathan Goldman
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”

9. DS ProcessDS Process
Frame the question
Collect the raw data
Process the data
Explore the data
Communicate results

10. Frame the QuestionFrame the Question
What questions do we want to answer?
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?

11. Collect the DataCollect the Data
What data do we need to answer these questions?
Connection data (who is who connected to?)
Demographic data (what is the pro le of the
connection)
Engagement data (how do they use the site)

12. Process the DataProcess the Data
How is the data “dirty” and how can we clean it?
• User input
• Redundancies
• Feature changes
• Data model changes

13. Explore the DataExplore the Data
What are the meaningful patterns in the data?
• Triangle closing
• Time overlaps
• Geographic overlaps

14. Communicate FindingsCommunicate Findings
How do we communicate this? To whom?
Marketing - this will enable us to sell X more ad space. Results in X
more impressions per day
Product - this will allow us to build X more features
Development - this will allow us to grow our team by X
Sales - this will attract X more premium accounts
C-Level - this will result in $$$ more revenue
8M - 450M in 10 years

15. The ResultThe Result
Career Whack-A-Mole

16. Why DS now?Why DS now?
Big Data: datasets whose size is beyond the
ability of typical database software tools to
capture, store, manage, and analyze

17. Big DataBig Data
Trend "started" in 2005
Web 2.0 - Majority of content is created by users
Mobile accelerates this — data/person skyrockets

18. The Data ProblemThe Data Problem
We are generating more data
every year than existed
before.........

19. The SolutionThe Solution
There goes my hero....
watch 'em as they code....

20. Just need to do everything....Just need to do everything....

21. Just need to do everything....Just need to do everything....
Knowledge of statistics, algorithms, & software
Comfort with languages & tools (Python, SQL, Tableau)
Inquisitiveness and intellectual curiosity
Strong communication skills
It’s all Teachable!

22. Let's Learn Python TonightLet's Learn Python Tonight
Python for Programming
Great for Data Science
Robotics
Web Development (Python/Django)
Automation

23. Let's Learn Python TonightLet's Learn Python Tonight
firstName = 'TJ'
lastName = "Stalcup"
age = 34 // wow, much old
print firstName // TJ
print firstName + lastName // TJStalcup
print firstName + ' ' + lastName // TJ Stalcup
print lastName + ', ' + firstName // Stalcup, TJ
print age * 2 // 68, hopefully retired
def greet(name):
print 'Hello', name
greet('Jack') // Hello, Jack
greet('Jill') // Hello, Jill
greet('Bob') // Hello, Bob
greet(firstName) // Hello, TJ
greet(firstName + ' ' + lastName) // Hello, TJ Stalcup

24. The ModelThe Model
Our model is going to be a Decision Tree.
Decision trees predict the most likely outcome based on input.
You can think of it like a computer building a version of 20 questions.

25. Decision Trees - Golf?Decision Trees - Golf?

26. The NotebookThe Notebook
We're going to use a Google hosted Python notebook to build this
model. This app is called Colaboratory (Collaboration + Laboratory)
http://colab.research.google.com
New Notebook > New Python3 Notebook

27. ShortcomingsShortcomings
Our model has a few weaknesses:
-Limited inputs
-Assumptions

28. Coming Soon....Coming Soon....
Intro to SQL
Intro to Tableau
Intro to Statistics
http://meetup.com/Thinkful-DC

29. Data Science @ ThinkfulData Science @ Thinkful
Flexible, project-based curriculum to help you become the data
scientist you want to be
You don’t just learn skills, you get to make things
Mentor support from experts in the industry
Also, there's a job guarantee

30. Link for the third party audit jobs report:
https://www.thinkful.com/bootcamp-jobs-stats
Thinkful Graduates Job Guaranteed

31. Learning Mentor
Career MentorProgram Manager
Local Community
You
Unprecedented SupportUnprecedented Support

32. http://bit.ly/dc-ds-trial
Initial 2-week trial course
Start with Python and Statistics
Unlimited Q&A Sessions
Option to continue with full bootcamp
Financing & scholarships available
Aaron Lamphere
Trial Program Manager
Thinkful Two Week TrialThinkful Two Week Trial