6. “[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
Example:
LinkedIn
2006
7. ➔ 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”
Enter:
Data
Scientist
8. ➔ Frame the question
➔ Collect the raw data
➔ Process the data
➔ Explore the data
➔ Communicate results
The
Process:
LinkedIn
Example
9. ➔ What questions do we want to answer?
◆ Who?
◆ What?
◆ When?
◆ Where?
◆ Why?
◆ How?
Case:
Frame
the
Question
10. ➔ 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?
Case:
Frame
the
Question
11. ➔ What data do we need to
answer these questions?
Case:
Collect
the
Data
12. ➔ Connection data (who is who connected to?)
➔ Demographic data (what is the profile of
the connection)
➔ Engagement data (how do they use the site)
Case:
Collect
the
Data
13. ➔ How is the data
“dirty” and how can
we clean it?
Case:
Process
the
Data
14. ➔ User input
➔ Redundancies
➔ Feature changes
➔ Data model changes
Case:
Process
the
Data
15. ➔ What are the meaningful
patterns in the data?
Case:
Explore
the
Data
17. ➔ How do we communicate this?
➔ To whom?
Case:
Communicate
Findings
18. ➔ Marketing - sell X more ad space, results in X more
impressions per day
➔ Product - build X more features
➔ Development - grow our team by X
➔ Sales - attract X more premium accounts
➔ C-Level - more revenue, 8M - 450M in 10 years
Case:
Communicate
Findings
22. ➔ Our model is going to be a Decision Tree
➔ Decision Trees predict the most likely outcome
based on input
➔ Like a computer building a version of 20
questions
The
Model
24. ➔ We’ll be using a
Google-hosted Python notebook
to build this model called
Colaboratory
➔ Go to:
Colab.research.google.com
➔ Click New Python 3 Notebook
The
Notebook
25. from sklearn import tree
➔ Import Tree functionality from
the SKLearn Python Package
➔ bit.ly/sklearn-python
Code
Block 1
26. X = [[181,80], [177,70], [160,60], [154,54], [166,65],
[190,90], [175,64], [177,70], [159,55], [171,75], [181,85]]
Y = ['male','female','female','female','male','male','male','female',
'male','female','male']
➔ Load in our seed data
➔ X is an array of inputs, each input is itself
an array that contains Height (in cm) and
Weight (in kg)
➔ Y is an array of strings that map to the
inputs in X so we can train the model
Code
Block 2
27. clf = tree.DecisionTreeClassifier()
clf = clf.fit(X,Y)
#print tree.export_graphviz(clf,None)
➔ We create an empty DecisionTreeClassifier and
assign it to the variable clf
➔ We fit the decision tree with our X and Y
seed data
➔ SKLearn is automatically creating our
Decision Tree questions for us (Example: Is
height > 177? Yes - Male)
➔ Uncomment the last line and paste the return
string into: webgraphviz.com
Code
Block 3
31. ➔ Start with Python and Statistics
➔ Personal Program Manager
➔ Unlimited Q&A Sessions
➔ Student Slack Community
➔ bit.ly/freetrial-ds
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