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![“[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](https://image.slidesharecdn.com/tf-gsds-180807203226/75/Tf-gsds-5-2048.jpg)
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Tf gsds
- 2. Introductions ➔ What's yourname? ➔ What brought you here today? ➔ What is your programming experience?
- 3. About Thinkful We trainweb developers and data scientists through 1x1 mentorship and project-based learning. Guaranteed.
- 5. “[LinkedIn] was likearriving 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
- 6. ➔ Joined LinkedInin 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
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- 8. ➔ Uber: wheredrivers should hang out ➔ Tala: microfinance loan approval Other Examples
- 9. ➔ Big Data:datasets whose size is beyond the ability of typical database software to capture, store, manage, and analyze Why Now?
- 10. ➔ Trend "started"in 2005 ➔ Web 2.0 - Majority of content is created by users ➔ Mobile accelerates this — data/person skyrockets Brief History of Big Data
- 11. “90% of thedata in the world today has been created in the last two years alone” — IBM, May 2013 Big Data
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- 15. “The United Statesalone 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 Just the Beginning
- 16. ➔ Frame thequestion ➔ Collect the raw data ➔ Process the data ➔ Explore the data ➔ Communicate results The Process: LinkedIn Example
- 17. ➔ What questionsdo we want to answer? ◆ Who? ◆ What? ◆ When? ◆ Where? ◆ Why? ◆ How? Case: Frame the Question
- 18. ➔ 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
- 19. ➔ What datado we need to answer these questions? Case: Collect the Data
- 20. ➔ 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
- 21. ➔ How isthe data “dirty” and how can we clean it? Case: Process the Data
- 22. ➔ User input ➔Redundancies ➔ Feature changes ➔ Data model changes Case: Process the Data
- 23. ➔ What arethe meaningful patterns in the data? Case: Explore the Data
- 24. ➔ Triangle closing ➔Time Overlaps ➔ Geographic Overlaps Case: Explore the Data
- 25. ➔ How dowe communicate this? ➔ To whom? Case: Communicate Findings
- 26. ➔ 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
- 27. ➔ SQL Queries ➔Business Analytics Software ➔ Machine Learning Algorithms Tools
- 28. SQL is thestandard querying language to access and manipulate databases SQL Queries
- 29. SELECT full_name FROM friends WHEREage>22 SQL Queries
- 30. Business analytics software foryour database enabling you to easily find and communicate insights visually Visualization Software
- 31.
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- 33. Machine Learning Algorithms Machine learning algorithmsprovide computers with the ability to learn without being explicitly programmed — “programming by example”
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- 37. ➔ Classification ➔ Regression ➔Fraud Detection ➔ Clustering Use Cases for Machine Learning
- 38. ➔ Classification ◆ Whatdefined set does this new piece of data belong to ◆ Examples? Use Cases for Machine Learning
- 39. ➔ Classification ◆ Isthis Email Spam or Not Spam? ◆ Pattern Recognition ◆ Handwriting Recognition ◆ Speech Recognition ◆ Iris Species ◆ Supervised Learning Use Cases for Machine Learning
- 40. ➔ Regression ◆ UsingPast Data to determine the future ◆ Examples? Use Cases for Machine Learning
- 41. ➔ Regression ◆ UsingPast Data to determine the future ◆ Stock Market Prices ◆ House Prices ◆ Meetup Attendance Use Cases for Machine Learning
- 42. ➔ Fraud Detection ◆Finding Uncommon Patterns to Route to a Human ◆ Examples? Use Cases for Machine Learning
- 43. ➔ Fraud Detection ◆Finding Uncommon Patterns to Route to a Human ◆ Credit Card Purchases ◆ Online Reviews ◆ Gaming ◆ DDOS Use Cases for Machine Learning
- 44. ➔ Clustering ◆ Groupingdata inputs into groups that differentiate them from other groups ◆ Examples? Use Cases for Machine Learning
- 45. ➔ Clustering ◆ Groupingdata inputs into groups that differentiate them from other groups ◆ Image Analysis & Identification - Cat Breeds ◆ Customer/Donor Segmentation & Strategy ◆ Unsupervised Learning Use Cases for Machine Learning
- 46. ➔ Python forProgramming ◆ Great for Data Science ◆ Robotics ◆ Web Development (Python / Django) ◆ Automation Python
- 47. def greet(name): print 'Hello',name greet('Jack') greet('Jill') greet('Bob') Python
- 48.
- 49. ➔ Knowledge ofstatistics, algorithms, & software ➔ Comfort with languages & tools (Python, SQL, Tableau) ➔ Inquisitiveness and intellectual curiosity ➔ Strong communication skills ➔ It’s all teachable and learnable! But If You’re Interested...
- 50.
- 51. ➔ Start withPython and Statistics ➔ Personal Program Manager ➔ Unlimited Q&A Sessions ➔ Student Slack Community ➔ bit.ly/freetrial-ds Thinkful Two-Week Free Trial
- 52. The Student Experience Marnie Boyer, ThinkfulGraduate Capstone Wolfgang Hall, Thinkful Graduate Capstone
- 53.