Overview of recent developments in datascience and machine learning from Google Cloud and Amazon Web Services will accelerate the digital ingestion data. This includes an overview of Frameworks that convert Data to Insights to Action.

1. Jojo Anonuevo for J&J, Acacia Hotel 10 Jan 2019
Data Science and the Data Revolution

3. Definition - What does Data Science mean?
Data science is a broad field that refers to the collective
processes, theories, concepts, tools and technologies
that enable the review, analysis and extraction of
valuable knowledge and information from raw data. It is
geared toward helping individuals and organizations
make better decisions from stored, consumed and
managed data.
Source:

4. Data to Insight to Decision
Information Technologists
Data Engineers
Data and Business Analysts
Data Scientists, Analysts
Decision-Makers

5. What types of decisions?
● Prediction (predict a value based on inputs)
● Classification (e.g., spam or not spam)
● Recommendations (e.g., Amazon and Netflix recommendations)
● Pattern detection and grouping (e.g., classification without known
classes)
● Anomaly detection (e.g., fraud detection)
● Recognition (image, text, audio, video, facial, …)
● Actionable insights (via dashboards, reports, visualizations, …)
● Automated processes and decision-making (e.g., credit card
approval)
● Scoring and ranking (e.g., FICO/Credit score)
● Segmentation (e.g., demographic-based marketing)
● Optimization (e.g., risk management)
● Forecasts (e.g., sales and revenue)

6. Data to Decision Frameworks
Where’s the Data?

7. Diverse Data Sources converging into Datawarehouse
GeoViz
Latency

8. Examples of Data to Decision Frameworks
Information Technologists
Data Engineers
Data and Business Analysts
Data Scientists, Analysts
Decision-Makers

9. Data to Decision frameworks
Gartner - 4 Analytics Capabilities
Machine Centered
Human Centered
Data
Decision
Predictive
What will happen Action
Prescriptive
What should I do
Decision Support
Decision Automation
Analytics
Descriptive
What happened
Diagnostic
Why did it happen

10. Exponential Growth of Data
Diverse Streams of Data
ANALYTICS PLATFORM
Terabytes of Data
/ Day

11. Mobile and Data First
Moderate Images
Extract Text, Images
Measure Upload Speed

12. Exponential Data Streams with IoT

13. Data Consumption

14. DIVERSITY OF INSIGHTS
Variance
Stage 1
Stage 2
Stage 3

15. Geovisualization

16. ANALYTICS PLATFORM

17. Average of
1.9 Hours
Beyond SLA of 3 Days: 8.43%
Tyranny of the 1 Number The tyranny of averages is a phrase used in
applied statistics to describe the often
overlooked fact that the mean does not provide
any information about the shape of the
probability distribution of a data set or skewness,
and that decisions or analysis based on only the
mean—as opposed to median and standard
deviation—may be faulty.
From Wikipedia, the free encyclopedia
Is an average
of 1.9 hours
ok if you’re targeting
95% to be
under 3 hours?

18. Eliminate the noise
Pareto
Principle

19. Pre-Processing and Transformation
(Extract Transform Load or ETL)

20. Challenges with Inconsistencies
Region name
instead of Province
Missing
Province
Invalid City
Names

21. Challenges with Inconsistencies
Manila

22. Importance of Standards in Data Quality
Street / Apt / Condo
MMXCode 01 001
2 Char Province
Metro Manila
2 Digit City
Caloocan City
3 Digit Barangay
Barangay 1
Latitude Longitude
XXXXX
Extended XCode for higher
granularity using LatLong
http://nap.psa.gov.ph/activestats/psgc/default.asp

23. Granular visibility with XCode (new Postal Code)

24. Data Modeling

25. Modeling Disciplines

26. Modeling and Data Science Disciplines
Economics
AI
Machine
Learning
Python
R
executive.berkeley.edu/programs/berkeley-program-data-science-analytics

27. Python + Libraries and Simple Regression Model

28. Building a Multiple Regression Model:
Case Study: Predict Demand for CapitalBikeshare
𝜇 rider count | temp, humidity, ..., month =
𝛽0 + 𝛽1 ∗ (temp)+ 𝛽2 ∗ (humidity) +...+ 𝛽𝑘∗(month)
www.capitalbikeshare.com/system-data
Response
Explanatory Variables

29. Case Study: Predict Demand for CapitalBikeshare
𝜇 rider count | temp, humidity, month = 𝛽0 + 𝛽1 ∗ (temp) + 𝛽2 ∗ (humidity) +...+ 𝛽𝑘∗(month)
www.capitalbikeshare.com/system-data

30. Datascience - Classroom and Corporate Tours

31. Netflix HQ Tour
Los Gatos, California

32. Key Take-away:
Data Science is a key pillar in product development ....

33. Data Science Disciplines and Data Scientist

34. Uber Data Scientist

35. Pandora Data Scientist

36. How we leverage Machine Learning - Improve Customer
Service

37. ERA OF INNOVATE OR BE DISRUPTED
The Industries That Are Being Disrupted the Most by Digital
The Science of Fail: Why the New Gap Logo Made Our Brains Angry

38. Thank you...