Data scientists drive data as a platform to answer previously unimaginable questions. These multi-talented data professionals are in demand like never before because they identify or create some of the most exciting and potentially profitable business opportunities across industries. However, a scarcity of existing external talent will require companies of all sizes to find, develop, and train their people with backgrounds in software engineering, statistics, or traditional business intelligence as the next generation of data scientists. In this video, Cloudera's Senior Director of Data Science, Josh Wills, discusses what data scientists do, how they think about problems, the relationship between data science and Hadoop, and how Cloudera training can help you join this increasingly important profession. Following the video, Josh answers questions about machine learning, analytics platforms, applications of data science in different industries, and Cloudera's Introduction to Data Science course.

1. Training a New Generation of
Data Scientists
Josh Wills | Senior Director of Data Science

2. About Me

3. What Do Data Scientists Do?

4. What I Think I Do

5. What Other People Think I Do

6. What I Actually Do

7. The Emergence of Data Science

8. Data Storage in 2001: Databases
• Structured schemas
• Intensive processing
done where data is
stored
• Somewhat reliable
• Expensive at scale

9. Data Storage in 2001: Filers
• No schemas, stores any
kind of file
• No data processing
capability
• Reliable
• Expensive at scale

10. And Then, This Happened

11. Data Economics, Return on Byte

12. Big Data Economics
• No individual record is Value = f(Bytes)
particularly valuable
• Having every record is
incredibly valuable
• Web index
• Recommendation systems
• Sensor data
• Market basket analysis
• Online advertising

13. Enter Hadoop

14. The Hadoop Distributed File System
• Based on the Google File
System
• Data stored in large files
• Large block size: 64MB to
256MB per block
• Blocks are replicated to
multiple nodes in the
cluster

15. Simple, Reliable, Distributed Processing: MapReduce
• Map Stage
• Embarrassingly parallel
• Shuffle Stage: Large-scale distributed sort
• Reduce Stage
• Process all the values that have the same key in a single step
• Process the data where it is stored
• Write once and you’re done.

16. Thinking Like a Data Scientist

17. Solving Problems vs. Finding Insights

18. Parallelize Everything

19. Abundance vs. Scarcity

20. Building Data Products

21. Create a Data Science Team

22. Choose Good Problems

23. Design the Model

24. Mind the Gap

25. Amortize Costs

26. Measure Everything

27. Rinse and Repeat

28. Work Like a Data Scientist

29. Train Like a Data Scientist
Introduction
to Data
Hive and Pig Science
Training
Hadoop
Developer
Training

30. Introduction to Data Science:
Building Recommender Systems
http://university.cloudera.com/

32. • Submit questions in the Q&A panel
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