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![➔ 90% of the data in the world today has been created
in the last two years alone [IBM, May 2013]
➔ Google, on average, processes more the 40,000
searched PER SECOND [Forbes, May 2018]
➔ Every minute: [Forbes, May 2018]
◆ Users watch 4,146,600 YouTube videos
◆ We send 16 million text messages
◆ There are 990,000 Tinder swipes
◆ Venmo processes $51,892 peer-to-peer transactions
BIG
Data](https://image.slidesharecdn.com/tf-wiads-180925004147/85/Tf-wiads-5-320.jpg)
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Tf wiads
- 1.
- 2. Introductions ➔ What's yourname? ➔ What brought you here today? ➔ What is your programming experience?
- 3. We train developersand data scientists through 1x1 mentorship and project-based learning. Guaranteed. About Thinkful
- 4. There is moredata available than ever before. But just how much more…? Why is Data Science a Thing?
- 5. ➔ 90% ofthe data in the world today has been created in the last two years alone [IBM, May 2013] ➔ Google, on average, processes more the 40,000 searched PER SECOND [Forbes, May 2018] ➔ Every minute: [Forbes, May 2018] ◆ Users watch 4,146,600 YouTube videos ◆ We send 16 million text messages ◆ There are 990,000 Tinder swipes ◆ Venmo processes $51,892 peer-to-peer transactions BIG Data
- 6. ➔ Data scienceis the process of taking all that information and turns it into something valuable. ➔ What can that be? ◆ Anything that helps improve the function or knowledge of a company, individual, or other entity ● Data Wrangling ● Analytics ● Predictions What is Data Science?
- 7.
- 8. ➔ It’s reallyabout making data organized and workable ➔ Data’s often things like: ◆ Web pages ◆ Text ◆ Pictures ◆ ‘Event Streams’ ➔ You need it to be ◆ A table Data Wrangling
- 9. Who? What? When?Where? ➔ Essential questions for ‘What’s going on?’ ➔ After the fact analysis ➔ Involves a lot of ◆ Statistics ◆ Summaries ◆ Visualization ➔ This is an essential step for almost every data science project Analytics
- 10. How? What willhappen next? ➔ Modeling and Machine Learning ➔ Algorithms ➔ Artificial Intelligence This is the fun stuff… But it only works if you do the other things well. Modeling + Prediction
- 11. ➔ The Researcher ➔The AI or Automation Guru ➔ The Statistician ➔ The Super Analyst These are just general types. In reality a lot of people mix these skills together, but these are four of the strongest ‘archetypes’ The Four Types of Data Scientist
- 12. ➔ The Researcher ➔The AI or Automation Guru ➔ The Statistician ➔ The Super Analyst These are just general types. In reality a lot of people mix these skills together, but these are four of the strongest ‘archetypes’ The Researcher: Who Are They?
- 13. ➔ Research atbig companies ◆ Google, Microsoft, etc all have research departments ➔ Still in academia ➔ Government agencies ➔ Very specific startups or consultancies The Researcher: Where Are They?
- 14. ➔ Building highlyspecialized products on the cutting edge ◆ The first facial recognition software ◆ Self driving cars The Researcher: What Do They Do?
- 15. ➔ Get aPhD or other graduate degree (but probably a PhD) ➔ Then learn to code The Researcher: How Can I Do It?
- 16. ➔ They loveto code ◆ Not just how to use the package ◆ Build from scratch ➔ Know multiple programming languages ◆ Python and R ➔ Say things like: ‘Let me automate that for you…’ The AI Guru: Who Are They?
- 17. ➔ AI for_____ Startups ◆ There are over 3,000 Artificial Intelligence startups on AngelList ➔ Tied to specific consumer products that are ‘smart’ The AI Guru: Where Are They?
- 18. ➔ Chatbots andautomated assistants ◆ Siri, Alexa, etc ➔ Automated Support ◆ Really just automated anything ➔ Replacing humans, adding efficiency The AI Guru: What Do They Do?
- 19. ➔ Write codefor everything ➔ Try to automate your own life or routines ➔ Embrace Python and Deep Learning packages like Tensor Flow ➔ Learn multiple languages The AI Guru: How Can I Do It?
- 20. ➔ Formal mathematicsor statistics training ➔ Lots of Masters in Statistics (shocking) ➔ Not necessarily experienced programmers ◆ May use tools like STATA or R The Statistician: Who Are They?
- 21. ➔ Statistical consultants ◆Political polling as an example ➔ Experimentation experts ◆ A/B testing and far, far beyond ◆ Optimizely’s development team The Statistician: Where Are They?
- 22. ➔ Easy experimentalframeworks for marketing ➔ Statistical testing for growth ➔ Answer statistical questions ◆ Are these things different? ◆ Which is best? ➔ Usually don’t build products The Statistician: What Do They Do?
- 23. ➔ Study themath ◆ Elements of Statistical Learning is a great place to start ➔ Maybe go get a Master’s The Statistician: How Can I Do It?
- 24. ➔ This isthe fastest growing section of data science ➔ Bachelor’s degrees, some Master's ➔ Love number and products, and want to put the two together ➔ Expert problem solvers ◆ and can communicate those solutions The footsoldiers of the data science revolution... The Super Analyst: Who Are They?
- 25. ➔ Every companythat generates data ◆ Every app and website records enormous amounts of data ◆ These are the data scientists that work with those places to find out what can be learned from that data ➔ So basically everywhere... The Super Analyst: Where Are They?
- 26. ➔ Learn tocode ➔ Immerse yourself in data ➔ Learn some basic Machine Learning ➔ Get building! The Super Analyst: How Can I Do It?
- 27. ➔ Biology andthe sciences use a lot of these skills ◆ But it’s not quite as big of a field ◆ And it almost always requires some background in the field ➔ That said, Data Science tools are super valuable for them too BioStats and Data Science
- 28. ➔ No oneis just one of these things ➔ But… Data Scientists are often T-Shaped ➔ That means they can do a little of everything, but they are more like one of these types than the others The Truth
- 29. Yes! But you wantto be smart about what kind of data scientist you want to be... Can I Be a Data Scientist?
- 30.
- 31. ➔ Free two-weektrial ➔ Start with Python and Statistics ➔ Personal Program Manager ➔ Unlimited Q&A Sessions ➔ Student Slack Community ➔ Thinkful Two-Week Free Trial
- 32. The Student Experience Marnie Boyer, ThinkfulGraduate Capstone Wolfgang Hall, Thinkful Graduate Capstone