Data Science for Growth

Because of all this potential people have been really eager to invest in data science.
It’s easy to think about it as magic. Perform a bit of analysis and derive novel insights.
Sprinkle some deep learning and make your product more engaging.
But this is not necessarily helpful, to startups or to the field.
Instead we need to understand what tools and techniques data science can provide and the circumstances in which they are appropriate.

Transcript

1. Data Science for Growth Gaza Sky Geeks Sean Patrick Braithwaite
2. • Data Science as a Discipline • Data Science as a Process • Techniques for Growth • Understanding Users • Understanding Groups • Understanding Change Agenda
3. Data Science as a Discipline
4. Data Science as a Mix of Disciplines
5. Data Science as Hype
6. Data Science is a Process
7. Data Science is an Active Process that drives Incremental Change
8. Navigation goes Wrong
9. Data Science is a Process that helps navigate building products
10. Data Science is a Process that makes other processes Efficient
11. When to use Data Science
12. Data Science for Growth Speed & Efficiency, Speed != Efficiency
13. Small data has Challenges
14. Small data has Advantages
15. Techniques for Growth
16. Understanding your Users Username Age Channel $ Spent Active Omar 21 Twitter 2 Yes Nour 45 Facebook 4 No Fatima 30 Organic 7 Yes … … … … …
17. Understanding your Users Username Age Channel $ Spent Active Omar 21 Twitter 2 Yes Nour 45 Facebook 4 No Fatima 30 Organic 7 Yes … … … … … as mixes of Numeric and Categorical fields Numeric Categorical
18. Understanding your Users Username Age Channel $ Spent Active Omar 21 Twitter 2.4 Yes Nour 45 Facebook 4.2 No Fatima 30 Organic 2.7 Yes … … … … … as mixes of Observed and Derived fields Derived Observed
19. Understanding your Users through plotting as Vectors Age $ Spent Channel Fatima Twitter Organic Facebook
20. Understanding your Users through Correlation Age $ Spent Channel Twitter Organic Facebook
21. Understanding your Users Through Exploring Correlations $ Spent orders Cost Impressions Clicks $ Spent orders Cost Clicks Impressions
22. Understanding your Users as Groups of Users Campaign $ Spent 1 2 3 4 1 2 3 4 5 6 Acquisition Cost $
23. Understanding your Users In practice, using tools • Excel: Surprisingly powerful! Works at small scale • Mixpanel: Cheap to get started, straight forward integration • IPython: Learning curve, works well at small scale (http://python-for-multivariate-analysis.readthedocs.io/)
24. Understanding Differences with Multivariate Testing Or
25. Understanding Differences with Multivariate Testing B A\
26. Understanding Differences in the outcomes of experiments Null: Option A and option B are equally good Alternative: Option B is significantly better than option A
27. Understanding Differences that are Significant Conversion Rate Probability density 0.15 0.225 0.1 Probability > 0.19 0.19 Most likely conversion rate 5%
28. Understanding Differences In the Distribution of performance
29. Understanding Differences By understanding p-values: Conversion Rate P(Conversion Rate) Observed Conversion: 0.19 Expected Conversion: 0.167
30. Understanding Differences Avoiding problems • Respect assumptions: Independent, normally distributed • Collect enough data: Low powered tests • Test as necessary: A-A tests when cost of being wrong is high
31. Understanding Differences In practice, using tools • Google Analytics: Basic free • Optimizely: More features, paid • Custom: Necessary at scale, expensive to build/maintain (http://www.evanmiller.org/ab-testing/)
32. Data Science is Many Things
33. Data Science is a Process That helps you grow Faster and Efficiently
34. Thank you!