Intro to Python for Data Science

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

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

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

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

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About ThinkfulAbout Thinkful
Local DC Crew

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

What is a Data Scientist?What is a Data Scientist?

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

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”

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

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?

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)

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

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

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

The ResultThe Result

Career Whack-A-Mole

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

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

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

The SolutionThe Solution
There goes my hero....

watch 'em as they code....

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

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!

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

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

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.

Decision Trees - Golf?Decision Trees - Golf?

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

ShortcomingsShortcomings
Our model has a few weaknesses:

-Limited inputs
-Assumptions

Coming Soon....Coming Soon....
Intro to SQL
Intro to Tableau
Intro to Statistics

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Data Science @ ThinkfulData Science @ Thinkful
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scientist you want to be
You don’t just learn skills, you get to make things
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Intro to Python for Data Science

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