![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](https://image.slidesharecdn.com/deck-92-1463-171220004612/85/Deck-92-146-3-9-320.jpg)
Deck 92-146 (3)
- 1. Getting Started with Data Science December 2017 http://bit.ly/data-science-sd Deskhub-main - stake2017!
- 2. Jordan Zurowski Thinkful Community Manager MA in Industrial/Organizational Psychology About me
- 3. About you You already have a career in data I'm interested in switching into a data career I just want to see what all the fuss is about
- 4. About Thinkful Thinkful helps people become developers or data scientists through 1-on-1 mentorship and project-based learning These workshops are built using this approach.
- 5. Today's Goals What is Data Science? How and why has the field emerged? What do they do? Next steps
- 9. 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
- 10. Enter: Data Scientist 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”
- 11. The Result
- 12. Other Examples Uber — Where drivers should hang out Tala — Microfinance loan approval
- 13. Why now? Big Data: datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze
- 14. Brief history of "big data" Trend "started" in 2005 Web 2.0 - Majority of content is created by users Mobile accelerates this — data/person skyrockets
- 15. Big Data 90% of the data in the world today has been created in the last two years alone - IBM, May 2013
- 16. The Problem
- 17. The Solution
- 18. Data Scientists - Jack of All Trades
- 19. Data Science is just the beginning “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
- 20. The Process - LinkedIn Example Frame the question Collect the raw data Process the data Explore the data Communicate results
- 21. Case: Frame the Question What questions do we want to answer?
- 22. 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?
- 23. Case: Collect the Data What data do we need to answer these questions?
- 24. Case: Collect the Data Connection data (who is who connected to?) Demographic data (what is the profile of the connection) Engagement data (how do they use the site)
- 25. Case: Process the Data How is the data “dirty” and how can we clean it?
- 26. Case: Process the Data User input Redundancies Feature changes Data model changes
- 27. Case: Explore the Data What are the meaningful patterns in the data?
- 28. Case: Explore the Data Triangle closing Time overlaps Geographic overlaps
- 29. Case: Communicate Findings How do we communicate this? To whom?
- 30. Case: Communicate Findings “People You Know” feature increased clickthrough by 30% (generating millions more page views)
- 31. Tools SQL Queries Business Analytics Software Machine Learning Algorithms
- 32. #1 SQL Queries SQL is the standard querying language to access and manipulate databases
- 33. #1 SQL Queries SELECT full_name FROM friends WHERE age>22
- 34. #2: Visualization Software Business analytics software for your database enabling you to easily find and communicate insights visually
- 36. #3: Machine Learning Algorithms Machine learning algorithms provide computers with the ability to learn without being explicitly programmed — “programming by example”
- 37. Iris Data Set
- 38. Iris Data Set
- 39. Iris Data Set
- 40. Use Cases for Machine Learning Classification — Predict categories Regression — Predict values Anomaly Fraud Detection — Find unusual occurrences Clustering — Discover structure
- 41. It may seem like a daunting opportunity
- 42. But if you're interested... Knowledge of statistics, algorithms, & software Comfort with languages & tools (Python, SQL, Tableau) Inquisitiveness and intellectual curiosity Strong communication skills It’s all Teachable!
- 43. Ways to keep learning
- 44. For aspiring developers... Source: Bureau of Labor Statistics
- 45. 92%of grads placed in full-time tech jobs job guarantee Link for the third party audit jobs report: https://www.thinkful.com/outcomes Thinkful's track record of getting students jobs
- 46. Our students receive unprecedented support 1-on-1 Learning Mentor 1-on-1 Career MentorProgram Manager San Diego Community You
- 47. 1-on-1 mentorship enables flexible learning Learn anywhere, anytime, and at your own schedule You don't have to quit your job to start career transition
- 48. Thinkful's Free Resource Introduction to Python, Data Visualization, and Stats. Unlimited mentor-led Q&A sessions Personal Program Manager bit.ly/tf-ds-free- course