The document discusses trends in data science. It provides background on data science, describing its multidisciplinary nature and noting key definitions and frameworks from 1974-2013. Real-world applications of data science are discussed in marketing, banking, healthcare, social media and more. The presentation then focuses on Opileak, a company that performs opinion mining and sentiment analysis on text. It describes Opileak's features and the presenter's PhD research on improving polarity analysis by accounting for conditional opinions. The presentation concludes that data science has many applications and Opileak's technology is promising.

1. @FernanOrtega
Trends in data science
Fernando O. Gallego
fogallego@us.es
@FernanOrtega

2. @FernanOrtega
About me
• Co-Founder of Opileak
• CRO of Opileak
• PhD candidate at US
• Advisor: R. Corchuelo
• Member of TDG-Group
• Lecturer at D&T subject
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What people think about science
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What people think about science
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The truth is…
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What people think about data science
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Why data science is important
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Worldwide searches of data science
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Spain searches of data science
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Data science courses
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Data science jobs
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But…what is data science about?
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Lighten up my friend!
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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

16. @FernanOrtega
Brief history
• 1974 – Peter Naur – Datalogy & Data science
• 2002 – Committee on Data for Science & Technology
• 2003 – Journal of Data Science
• 2010 – Drew Conway – The data science Venn diagram
• 2010 – Mike Loukadis – What is Data science?
• 2011 – Irizarry, Peng & Leek – The keyword in “Data Science”
• 2013 – Vasant Dhar – “Data Science and Prediction”

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Drew Conway, 2010

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Mike Loukides, 2010
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“Data science enables the creation of data
products”

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Irizarry, Peng & Leek, 2013
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“The key word in data science is not ‘data’; but
‘science’. Data science is only useful when the
data are used to answer a question”

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Vasant Dhar, 2013
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“Data science is the study of the
generalizable extraction of knowledge
from data”

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The general idea
Data
Model
Knowledge
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Knowledge is power!
1. Answer questions
2. Questions to products
3. Products to profit
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And…big data
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•Untrusted
•Uncleansed
•Speed of
generation
•Rate of analysis
•Unstructured
•Semi-structured
•Structured
•Log
•Event
•Social media
•Click stream
Volume Variety
VeracityVelocity
Data scienceBig data

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And…business intelligence
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Data scienceBusiness intelligence
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data5.Communicate
6.Implement
product

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And…data mining
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Data scienceData mining

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Remember Drew Conway
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Multidisciplinary
Maths Statistics
Machine
learning
Software
engineering
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Everything is about hypes
Cloud
computing
IOT Big data
Data mining Data science
Business
intelligence
Opinion
mining
Social media
analysis
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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

31. @FernanOrtega
Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

32. @FernanOrtega
Ask a question
• Skills:
– Science
– Domain expertise
– Curiosity
• Tools:
– Your brain
– Talking to experts
– Experience
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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

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Get the data
• Skills:
– Web scraping
– Data cleaning
– Querying databases
• Tools:
– Web parsers
– SQL
– Python (pandas)
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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

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Explore the data
• Skills:
– Get to know data
– Develop hypotheses
– Detect pattern or
anomalies
• Tools:
– D3.js
– Matplotlib
– Excel!
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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

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Model the data
• Skills:
– Regression
– Machine learning
– Big data
• Tools:
– Spark (MLlib)
– Hadoop
– Mrjob
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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

40. @FernanOrtega
Communicate
• Skills:
– Presentation
– Speaking
– Writing
• Tools:
– Chart tools
– LaTeX
– Powerpoint
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Data science general process
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

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Implement product
• Skills:
– Product management
– Communication
– Programming
• Tools:
– Programming languages
– Servers
– Project management
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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

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Marketing
• Product
recommendation
• Channel optimisation
• Discount targeting

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Bank
• Financial products
recommendation
• Credit risk
• Fraud detection
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Customer support
• Call routing
• Message optimisation
• Customer satisfaction
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Human resources
• Ranking candidates
• Predict leaving
employees
• Training
recommendation
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Health
• Predict flu activity
• Cancer defeating
• Early detection of
Alzheimer’s dissease
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Social media
• Face recognition
• Contact suggestion
• Topic opinion
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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

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The team (1/3)
CEO Team Leader CRO

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The team (2/3)
CCO Comm. Technician Systems Admin

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The team (3/3)
Front-end Back-end DevOps

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At the beginning
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The right way
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The inspiration
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The first approach
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But the problem is
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Solution
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Retail
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Press
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Town councils
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Opileak technology features
• Volumetric measures
• Topics and summary
• Opinion mining
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My PhD thesis
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Polarity analysis
Attribute Polarity
“zoom quality” Positive
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Attribute Polarity
“resolution” Neutral
“Build quality” Negative

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The problem with polarity analysis
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Conditionals definition
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Wait! the opinion is
only true in a certain
situation
“This camera doesn’t work
well at night”

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Our proposal
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Torii!
Opinion-mining process
that computes
overviews making the
conditions of opinions
and factual information
explicit.

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Roadmap
Introduction
Background
Real-world applications
Opileak
Conclusions

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Data science
1.Ask a
question
2.Get the data
3.Explore the
data
4.Model the
data
5.Communicate
6.Implement
product

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Lesson learned today
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Multidisciplinary Many applications Our technology

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Opileak rocks!
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Thanks
Fernando O. Gallego
fogallego@us.es
@FernanOrtega