Data science course

1. Presented by:
Doaa Mohey Eldin
PhD researcher in information Systems
Faculty of Computers and Artifical intelligence – Cairo University
IEEE Society Member
d.Mohey2020@gmail.com

2. Agenda
• What is Data science?
• Why use Data science?
• How to use Data Science in real life?
• Data science Applications
• How to interpret data science model?
• Data science Techniques
• Data science challenges
• Data science trends
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3. What is Data Science?
• Data Science is
– “an informative science that extracts knowledge from
various domains. That requires using many
algorithms, methods, systems or techniques for
scrapping this data and interpret it”.
– related to data mining, machine learning, and big
data.
– Based on using statistics, analysis, or informatics, and
their related methods.
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4. What is Data Science?
• Data Science is
– “Data Science is an interdisciplinary field that allows
you to extract knowledge from structured or
unstructured data.” as a formal definition.
– The area of study involves extracting insights from
vast amounts of data by the use of various scientific
methods, algorithms, and processes.
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5. What is Data Science?
5
Computer
science/IT
Business/
Domain
Knowledge
Math/
statistics
Data
Science
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6. Why use Data Science?
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Why use
data
science?
Effective
interpretation
of business
problems
Improve
decision
making in
various
domains
Powerful
predictive
systems
Managing
many users
requirements
for each
system
Develop
models for
real data

7. How to use Data Science in Real Life?
• Data Science is considered a key of business and real life.
It uses for solving prediction problems, analytics
problems and risk analysis problems.
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Prediction
problems
Analytics
problems
Risk
problems

8. How to use Data Science in Real Life?
• It interprets real life applications,
Based on various properties.
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Characteristics
Conditions
Techniques
Visualization
issues
Challenges
Roles
users
Scale
(large & small)
Each application based
on various:

9. Data Science Applications
• Identifying, classification Diseases, and predicting the evolution
of diseases progression.
• Healthcare recommendations systems.
• Predicting incarceration rates.
• Business controlling and classifying products.
• Automating digital ad placement.
• Managing smart environments.
• Classifying and interpreting text analysis (such as news) and
fraud data.
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10. Data Science Applications
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11. How to interpret data science model?
Define
problem
Determine
model zone
Select
solution
technique
Experiment
Results
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12. Data Science Techniques
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Data Science
Techniques
Linear
Regression
Decision tree
Support
vector
machine
Neural
networks
Classification
Logistic
regression
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13. Data Science Challenges
• Big data of high variety information.
• Hardness access to data.
• Explaining the hardness of data science in interpreting
various domains.
• Privacy issues.
• Lack of significant domain expert.
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14. Data Science Trends
Data
Science
Trends
Artificial
intelligence
Internet-
of-things
Behavioral
analytics
Machine
intelligence
Graph
analytics
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