Data Science presentation for intro to how data science, its evolution and how we should think about it

1. Intro to Data Science
Part 1 - Intro and
reproducibility
http://bit.ly/ReuterDataSci

2. Data Science

3. Data Science
Why?

4. Data Science
Why?

5. Data science process

6. Hadley Wickham
http://hadley.nz/
Chief Scientist at

7. The Flow

9. Reproducibility

10. Reproducibility
Different definitions...
The ability to reproduce your results (figures, tables, analysis
etc.).
Starts at raw data and ends at a “communication”.
NOT “repeating” your experiment

13. Reproducibility

14. Reproducibility

16. Reproducibility - #fail

17. Reproducibility
How will you describe what you did in your paper?
How will you be able to check for mistakes?
What happens if when you find one?
How will you tell your professor what you did?
How will you remember what you did?

18. Reproducibility - the solution!
Scripting languages.
“Code that shit.”
R, Python, SAS, Julia...
C, C++, S, Fortran, BASIC...

19. 2 take-home points

20. Think in terms of the
data pipeline

21. Strive to improve
reproducibility
continuously

22. Next time...

23. Seminar series
Week 1 - Data pipeline and reproducibility
Week 2 - Installing R, packages and math
Week 3 - RStudio and reading in data
Week 4 - Tidyr and dplyr
Week 5 - Visualizing data
Week 6 - ?

24. Bring:
● Laptop (internet access)

25. Feedback
http://bit.ly/ReuterPart1Survey