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Getting Started in Data Science
April 2017
http://bit.ly/tf-data-science
About me
• Jasjit Singh
• Worked in finance & tech
• Co-Founder Hotspot
• Thinkful General Manager
About us
Thinkful prepares students for web development &
data science jobs with 1-on-1 mentorship programs
About you
•I already have a career in data
•I’m serious about switching into a career in data
•I’m curious about switching into a career in data
•Ugh I just want to see what all the fuss is about
•Data is my favorite character in Star Trek
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?
Agenda for tonight
• What is the market landscape for dev jobs?
• What programming language should I learn?
• What are the best ways to learn to code?
• What are the first jobs / trajectories?
• How do I break into the field?
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
Which means…
…average salaries are $115,000 a year
Definition #1
Definition #2
Nate Silver
FiveThirtyEight.com
“I think data-scientist is a sexed up term for a statistician”
My favorite definition
Case study: 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
The new guy
Jonathan Goldman
•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”
The result
Other examples
•Uber — Where drivers should hang out
•Netflix — $1M movie recommendations contest
•Ebola — Mobile mapping in Senegal to fight disease
“Big Data” changed the game
Big Data: datasets whose size is beyond the ability of
typical database software tools to capture, store,
manage, and analyze
Brief history of “Big Data”
•Trend “started” in 2005 (Hadoop!)
•Web 2.0 - Majority of content is created by users
•Mobile accelerates this — data/person skyrockets
Explosion across 3V’s
Big data: tldr;
90% of the data in the world today has been created
in the last two years alone
- IBM, May 2013
We’re drowning in data
Data scientists are the solution
A jack of all trades
Data science process
•Frame the question
•Collect the raw data
•Process the data
•Explore the data
•Communicate results
Frame the question
What questions do we want to answer?
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?
Collect the data
What data do we need to answer these questions?
Collect the data
•Connection data (who is who connected to?)
•Demographic data (what is profile of connection)
•Retention data (how do people stay or leave)
•Engagement data (how do they use the site)
Process the data
How is the data “dirty” and how can we clean it?
Process the data
•User input
•Redundancies
•Feature changes
•Data model changes
Explore the data
What are the meaningful patterns in the data?
Explore the data
•Triangle closing
•Time overlaps
•Geographic clustering
Communicating the findings
How do we communicate this? To whom?
Communicating the 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
Tools to explore “big data”
•SQL Queries
•Business Analytics Software
•Machine Learning Algorithms
Tool #1: SQL queries
SQL is the standard querying language to access and
manipulate databases
SQL example
friends
id full_name age
1 Dan Friedman 24
2 Jared Jones 27
3 Paul Gu 22
4 Jasjit Singh 73
SELECT full_name FROM friends WHERE age=73
Tool #2: Analytics software
Business analytics software for your database enabling
you to easily find and communicate insights visually
Analytics software example
Tool #3: Machine learning algorithms
Machine learning algorithms provide computers with
the ability to learn without being explicitly
programmed — “programming by example”
Iris data set
Iris data set
Use cases for machine learning
•Classification — Predict categories
•Regression — Predict values
•Anomaly Detection — Find unusual occurrences
•Clustering — Discover structure
If this excites you…
This is what you’ll need
•Knowledge of statistics, algorithms, & software
•Comfort with languages & tools (Python, SQL, Tableau)
•Inquisitiveness and intellectual curiosity
•Strong communication skills
Data science bootcamp
Syllabus: Python Toolkit, Statistics & Probability,
Experimentation, Machine Learning, Communicating
Data, Algorithms and Big Data
More about Thinkful
• Anyone who’s committed can learn to code
• 1-on-1 mentorship is the best way to learn
• Flexibility matters — learn anywhere, anytime
• We only make money when you get a job
Our Program
You’ll learn concepts, practice with drills, and build capstone projects
for your own portfolio — all guided by a personal mentor
Our Mentors
Mentors have, on average, 10+ years of experience
Our Results
Job Titles after GraduationMonths until Employed
Special Prep Course Offer
• Three-week program, includes six mentor sessions for $250
• Overview of Python, Python’s data science toolkit, stats
• Option to continue into full data science bootcamp
• Talk to me (or email me) if you’re interested
October 2015
Questions?
jas@thinkful.com
schedule a call through thinkful.com

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Getting started in data science (4:3)

  • 1. Getting Started in Data Science April 2017 http://bit.ly/tf-data-science
  • 2. About me • Jasjit Singh • Worked in finance & tech • Co-Founder Hotspot • Thinkful General Manager
  • 3. About us Thinkful prepares students for web development & data science jobs with 1-on-1 mentorship programs
  • 4. About you •I already have a career in data •I’m serious about switching into a career in data •I’m curious about switching into a career in data •Ugh I just want to see what all the fuss is about •Data is my favorite character in Star Trek
  • 5. 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?
  • 6. Agenda for tonight • What is the market landscape for dev jobs? • What programming language should I learn? • What are the best ways to learn to code? • What are the first jobs / trajectories? • How do I break into the field?
  • 7. 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
  • 10. Definition #2 Nate Silver FiveThirtyEight.com “I think data-scientist is a sexed up term for a statistician”
  • 12. Case study: 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
  • 13. The new guy Jonathan Goldman •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”
  • 15. Other examples •Uber — Where drivers should hang out •Netflix — $1M movie recommendations contest •Ebola — Mobile mapping in Senegal to fight disease
  • 16. “Big Data” changed the game Big Data: datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze
  • 17. Brief history of “Big Data” •Trend “started” in 2005 (Hadoop!) •Web 2.0 - Majority of content is created by users •Mobile accelerates this — data/person skyrockets
  • 19. Big data: tldr; 90% of the data in the world today has been created in the last two years alone - IBM, May 2013
  • 21. Data scientists are the solution
  • 22. A jack of all trades
  • 23. Data science process •Frame the question •Collect the raw data •Process the data •Explore the data •Communicate results
  • 24. Frame the question What questions do we want to answer?
  • 25. 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?
  • 26. Collect the data What data do we need to answer these questions?
  • 27. Collect the data •Connection data (who is who connected to?) •Demographic data (what is profile of connection) •Retention data (how do people stay or leave) •Engagement data (how do they use the site)
  • 28. Process the data How is the data “dirty” and how can we clean it?
  • 29. Process the data •User input •Redundancies •Feature changes •Data model changes
  • 30. Explore the data What are the meaningful patterns in the data?
  • 31. Explore the data •Triangle closing •Time overlaps •Geographic clustering
  • 32. Communicating the findings How do we communicate this? To whom?
  • 33. Communicating the 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
  • 34. Tools to explore “big data” •SQL Queries •Business Analytics Software •Machine Learning Algorithms
  • 35. Tool #1: SQL queries SQL is the standard querying language to access and manipulate databases
  • 36. SQL example friends id full_name age 1 Dan Friedman 24 2 Jared Jones 27 3 Paul Gu 22 4 Jasjit Singh 73 SELECT full_name FROM friends WHERE age=73
  • 37. Tool #2: Analytics software Business analytics software for your database enabling you to easily find and communicate insights visually
  • 39. Tool #3: Machine learning algorithms Machine learning algorithms provide computers with the ability to learn without being explicitly programmed — “programming by example”
  • 42. Use cases for machine learning •Classification — Predict categories •Regression — Predict values •Anomaly Detection — Find unusual occurrences •Clustering — Discover structure
  • 43. If this excites you…
  • 44. This is what you’ll need •Knowledge of statistics, algorithms, & software •Comfort with languages & tools (Python, SQL, Tableau) •Inquisitiveness and intellectual curiosity •Strong communication skills
  • 45. Data science bootcamp Syllabus: Python Toolkit, Statistics & Probability, Experimentation, Machine Learning, Communicating Data, Algorithms and Big Data
  • 46. More about Thinkful • Anyone who’s committed can learn to code • 1-on-1 mentorship is the best way to learn • Flexibility matters — learn anywhere, anytime • We only make money when you get a job
  • 47. Our Program You’ll learn concepts, practice with drills, and build capstone projects for your own portfolio — all guided by a personal mentor
  • 48. Our Mentors Mentors have, on average, 10+ years of experience
  • 49. Our Results Job Titles after GraduationMonths until Employed
  • 50. Special Prep Course Offer • Three-week program, includes six mentor sessions for $250 • Overview of Python, Python’s data science toolkit, stats • Option to continue into full data science bootcamp • Talk to me (or email me) if you’re interested