Measurecamp March 2017 deck

1. You don’t have to be a Data
Scientist to do Data Science
@carmenmardiros (not a data scientist)

2. “Sexiest job of the 21st
century”
Why do I, a mere analyst, care?

3. The appeal of Data Science (for me as an analyst)
Increase
confidence
My own and others’ in my analyses as the complexity
of data and business ecosystem increases.
Become more
productive
Speed up the analysis cycle from exploration to
hypothesis to experimentation.
Add value in
new ways
As the business and technology landscape changes.
Operationalise analysis outcomes as data products.

4. “It’s just not for me...”
“I don’t have a degree in statistics or programming.”

5. No confidence to attend the
sessions.
Worried I would not understand
the content.
Worried I’d be spotted as a fraud.
(3m into my data science foray)
Understood much of the content
and terminology.
Mentally thought questions
others asked.
I knew more than I thought I did.
Predictive Analytics Summit 2013 Predictive Analytics Summit 2016

6. Doing data science requires a
PhD/going back to school.
Can’t do data science until you
can write an algorithm.
Bottom-up is the only way.
Doing data science requires
enthusiasm and confidence in
ourselves.
Can and should do data science
once we’ve conceptually
understood how and why the
algorithm works.
Top-down works.
Provide value, learn as you go.
Myth Truth

7. Adapt. Grow. Stay relevant.

8. Digital Analytics is changing fast
Increasingly
scientific
approaches
Essential as we move towards prescriptive analytics
at speed.
Become familiar
with data
science toolkit
We will be key to bridging the gap between PhDs,
machines and management.
May even use it ourselves for our day-to-day work.
Future-proof
ourselves
MS Office for Machine Learning coming soon at a
cloud near you.

9. 3 Transformative
Data Science techniques

10. #1 Resampling

11. The Bootstrap
Number of observations: 100
Sample is representative (to the best of
our knowledge).
Observed mean: 17.54 months

12. The Bootstrap
Draw 100 random samples with
replacement.
Calculate for each one the mean:
[17.61, 16.21, 17.13, 14.08, 19.58 … ] # 100
Plot all means, the 2.5 and 97.5
percentiles and original observed mean.
Bootstrap is extremely versatile:
● Fewer assumptions than parametric
methods.
● Can be used on any statistic.

13. Simulations & Sensitivity Analysis
Simple simulation:
Given existing distribution of order values and a
given range of possible conversion rates , how
much £££ would we make if we doubled the
traffic to our website?
Sensitivity analysis
(or how to open up black boxes):
Given a predictive model, randomly generate
new data points for each input based on
observed distributions, create predictions using
the model and interpret distribution of
outcome scenarios.

14. Cross Validation
Iterations
1 Train fold Train fold Train fold Train fold Test fold
2 Train fold Train fold Train fold Test fold Train fold
3 Train fold Train fold Test fold Train fold Train fold
4 Train fold Test fold Train fold Train fold Train fold
5 Test fold Train fold Train fold Train fold Train fold
Assesses how well a predictive model generalises to unseen data.

15. Resampling
Protects you
from unsound
inference
Acknowledges and mitigates effects of variance and
noise in the data.
You already do this when you use confidence
intervals. Quantify uncertainty more often.
Paints possible
future scenarios
Leverages randomness and probability to give you
glimpses into possible future outcomes.
Embrace randomness. It's your ally into prescriptive
analytics.

16. #2 Faceted visualisation

17. Segmented view, side-by-side
Outstanding tools for exploratory data analysis: Seaborn in Python and ggplot in R

18. Segmented view, side-by-side
Outstanding tools for exploratory data analysis: Seaborn in Python and ggplot in R

19. Segmented view, side-by-side
Outstanding tools for exploratory data analysis: Seaborn in Python and ggplot in R

20. #3 Feature Engineering
What?!

21. #3 Feature Engineering
#3 Calculated Metrics or
Content Groupings?
Back on familiar territory.

22. Feature Engineering Examples
Unique content
views per user
by content type
# politics content views, # business content views
# short/long-form content views
Distribution of
content seen
per user
% politics content views in total content viewed
adjusted for uncertainty of small samples
Result: fat user-level table of attributes and
behaviour for analysis and modelling.

23. Feature Engineering Examples
Infer trading
calendar
activities
from data
(for time series
analysis)
# new marketing campaigns (first date with sessions)
# new brands launched (first date with pageviews)
# voucher codes at peak redeem-rate (date with
highest redeems)
# AB tests started (date with first events tracked)
# VIPs active on each date, etc
Result: fat date-level table of leading KPIs and
activities (model the ecosystem).

24. Feature Engineering
New ways of
capturing
underlying
phenomena
Seasoned data scientists: Feature engineering often
yields higher rewards than pushing the latest
algorithms.
You likely already do this, likely in Excel.
It’s painful and limiting.
Your analytical creativity needs better tools.
SQL: The single most valuable tool in our toolkit.
We become self-sufficient analysts.

25. Resources

26. Inspired?
Learn Python https://try.jupyter.org/ -- start learning python for
data science right now (no setup!).
https://learncodethehardway.org/python/
Learn Machine
Learning
http://machinelearningmastery.com/
Understand how algorithms using spreadsheets.
Top-down approach. No programming required.
Learn SQL https://learncodethehardway.org/sql/