Some comments on data science in general. This is a super basic introduction to the topic. I shared these slides with my team members @reBuy to help them understand how can I, as the data scientist, can help them.

1. Get to know Data Science
Dror Atariah, Ph.D.

4. Agenda
● What is data science?
● What drives data science?
● What can you ask?
● Did you say big data?
● @reBuy

5. What is it?

6. Wiki’s version
“...is an interdisciplinary field about scientific methods, processes, and
systems to extract knowledge or insights from data in various forms,
either structured or unstructured…” (link)

7. What are the fields?
● Mathematics
● Computer science
● Visualization
● Business domain knowledge
● Communication/presentation skills

8. What kind of knowledge/insight?
● Ads matching
● Stock market behavior
● Accidents avoidance
● Strawberries grading
● Energy management
● Fraud detection
● …

9. What are the possible tracks?

10. Data science tracks
● Descriptive analytics: “What happened?”
● Predictive analytics: “What will happen?”
● Prescriptive analytics: “How can we make it happen?”

11. What is the fuel?

14. What should you ask a data
scientist?

15. Is this A or B (or C)?

16. Is this A or B (or C)? (cont’)
● Will a given user convert or not?
● Does 5€ voucher is better over 10% discount?
● Is it a dragon or a unicorn?
⇨ Classification

17. Is this weird?

18. Is this weird? (cont’)
● Is this transaction fraudulent?
● Is this server load typical?
⇨ Anomaly detection

19. How much? How many?

20. How much? How many?

21. How much? How many?

22. How much? How many? (cont’)
● What will be the CM1 in Q4 2018?
● How many iPhones will be sold in the next 7 days?
⇨ Regression

23. How is it organized?

24. How is it organized? (cont’)
● To which group of user is this new user similar?
● Where is the user’s home/work?
⇨ Clustering

25. What should I do now?
● Should the price of Harry Potter increase or decrease?
● Should server resources be boosted, kept or reduced?
⇨ (e.g) Reinforcement

26. What about BIG DATA?

27. 4 V’s of big data
● Volume
● Variety (Vielfalt)
● Veracity (Aufrichtigkeit or Wahrhaftigkeit)
● Velocity (Geschwindigkeit)

28. DS @ reBuy…

29. Two examples
● Multi channel marketing attribution models
● Dynamic conflicts resolution