Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. Why is data science important? Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.

1. DATA SCIENCE
ECOSYSTEM
M. TAMER ÖZSU NANCY REID RAYMOND NG
U. WATERLOO U. TORONTO UBC

2. Canadian Data Science Workshop
DATA SCIENCE/BIG DATA IN THE NEWS…
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3. Canadian Data Science Workshop
DATA SCIENCE EVERYWHERE!...
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4. Canadian Data Science Workshop
DATA SCIENCE EVERYWHERE!...
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5. Canadian Data Science Workshop
DATA SCIENCE EVERYWHERE!...
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6. Canadian Data Science Workshop
DATA SCIENCE VOCABULARY
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7. Canadian Data Science Workshop
WHAT IS DATA SCIENCE?
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8. Canadian Data Science Workshop
WHAT IS DATA SCIENCE?
• “Data science, also known as data-driven science, is an
interdisciplinary field of scientific methods, processes,
algorithms and systems to extract knowledge or insights
from data in various forms, either structured or
unstructured, similar to data mining.”
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9. Canadian Data Science Workshop
WHAT IS DATA SCIENCE?
• “Data science, also known as data-driven science, is an
interdisciplinary field of scientific methods, processes,
algorithms and systems to extract knowledge or insights
from data in various forms, either structured or
unstructured, similar to data mining.”
• “Data science intends to analyze and understand actual
phenomena with ‘data’. In other words, the aim of data science
is to reveal the features or the hidden structure of complicated
natural, human, and social phenomena with data from a
different point of view from the established or traditional theory
and method.”
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10. Canadian Data Science Workshop
WHAT IS DATA SCIENCE?
• Fourth paradigm
• “… change of all sciences moving from observational, to
theoretical, to computational and now to the 4th Paradigm –
Data-Intensive Scientific Discovery”
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11. Canadian Data Science Workshop
WHAT IS IMPORTANT?
Need to solve a real problem using data…
No applications, no data science.
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12. Canadian Data Science Workshop
DATA SCIENCE AS A UNIFIER
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Data
Science
Humanities
Machine/
Statistical
Learning
Application
Domain
Expertise
Visualization
Mathematical
Optimization
Social
Science
Law
Data
Management

13. Canadian Data Science Workshop
DATA SCIENCE AND BIG DATA
• They are not the “same thing”
• Big data = crude oil
• Big data is about extracting “crude oil”, transporting it in “mega tankers”,
siphoning it through “pipelines”, and storing it in “massive silos”
• Data science is about refining the “crude oil”
Carlos Samohano
Founder, Data Science London
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14. Canadian Data Science Workshop
DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
Data
Science
Artificial
Intelligence
ML/DM/
Analytics
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15. Canadian Data Science Workshop
DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
Data
Science
Artificial
Intelligence
ML/DM/
Analytics
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“Data science produces insights.
Machine learning produces predictions”

16. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• Fraud detection
• Investigate fraud patterns in past data
• Early detection is important
• Before damage propagates
• Harder than late detection
• Precision is important
• False positive and false negative are both
bad
• Real-time analytics
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17. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• Recommender systems
• The ability to offer unique
personalized service
• Increase sales, click-through rates,
conversions, …
• Netflix recommender system valued at
$1B per year
• Amazon recommender system drives a
20-35% lift in sales annually
• Collaborative filtering at scale
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18. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• Predicting why patients are being
readmitted
• Reduce costs
• Improve population health
• Find the “why” behind specific
populations being readmitted
• Data lakes of multiple data sources
• Investigate ties between readmission and
socioeconomic data points, patient
history, genetics, …
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19. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• “Smart cities”
• Not well-defined
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20. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• “Smart cities”
• Not well-defined
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21. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• “Smart cities”
• Not well-defined
• Generally refers to using data and
ICT to
• Better plan communities
• Better manage assets
• Reduce costs
• Deploy open data to better engage
with community
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22. Canadian Data Science Workshop
DATA SCIENCE APPLICATION EXAMPLES
• Moneyball
• How to build a baseball team on a very
low budget by relying on data
• Sabermetrics: the statistical analysis of
baseball data to objectively evaluate
performance
• 2002 record of 103-59 was joint best in
MLB
• Team salary budget: $40 million
• Other team: Yankees
• Team salary budget: $120 million
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23. Canadian Data Science Workshop
HOLISTIC APPROACH TO DATA SCIENCE
Dissemination &
Visualization
Ethics, Policy & Social Impact
Core
Data
Acquisition
Data
Preservation
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Modeling &
Analysis
Management of
Big Data
Making Data
Trustable &
Usable
Data Security & Privacy
Application
Application
Application
Application

24. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
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25. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
• Data cleaning
• Sampling
• Data provenance
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26. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
• Data cleaning
• Sampling
• Data provenance
• Data lakes
• Batch & online access
• Platforms
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27. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
• Data cleaning
• Sampling
• Data provenance
• Data lakes
• Batch & online access
• Platforms
• Models & methods for data
lakes
• Unsupervised
classification & AI
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28. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
• Data cleaning
• Sampling
• Data provenance
• Data lakes
• Batch & online access
• Platforms
• Models & methods for data
lakes
• Unsupervised
classification & AI
• Visualization for wider
audience
• Visualization for data
exploration
• Open data technologies
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29. Canadian Data Science Workshop
CORE RESEARCH ISSUES & INTERACTIONS
Making Data
Trustable &
Usable
Modelling &
Analysis
Data
Visualization &
Dissemination
Big Data
Management
• Data cleaning
• Sampling
• Data provenance
• Data lakes
• Batch & online access
• Platforms
• Models & methods for data
lakes
• Unsupervised
classification & AI
• Visualization for wider
audience
• Visualization for data
exploration
• Open data technologies
• DM support for
provenance
• Data preparation for big
data management
• Cleaning for data
analysis
• DM for ML
• ML for DM
• Visual analytics
…
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