The document outlines the integration of data science with cloud computing, specifically focusing on Azure Machine Learning as a tool for addressing big data challenges. It details the data science workflow, emphasizing the importance of data collection, model development, and performance improvement. Additionally, it discusses the advantages of modern data infrastructures and machine learning techniques in enhancing predictive accuracy and efficiency.

1. Data Science as a Service
Dr. Pouria Amirian (Pouria.Amirian@ndm.ox.ac.uk)
Big Data Project Coordinator, The Global Health Network, University of Oxford
Intersection Of Cloud Computing And Data Science

2. outline
 Data Science
 What data science is
 Steps in a Data Science project
 Experiments
 Using AzureML
 Big Data issues
 In a data science project
 Methods in analysis
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3. What is Data Science?
 Practice of obtaining useful insights from data
 3 Vs of Big Data:
 Volume
 Variety
 Velocity
 + other Vs
 It applies to large volume data (volume)
 It applies to semi-structured and unstructured data (variety)
 It sometimes applies to real-time or fast changing data
(velocity)
 It applies to small and traditional static data
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4. Data Science as a team sport
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Math
Statistical Learning
Linguistics
Machine Learning
Signal Processing
Programming
Storage/Data StructureOperations Research
Distributed and High
Performance Computing

5. Data Science from analytics point of view
 Analytics Spectrum:
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Descriptive
Diagnostic
Predictive
Prescriptive
What happened?
Why did it happen?
What will happen?
What should I do?

6. Data Science Vs. Business Intelligence
 Analytics Spectrum:
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What happened?
Why did it happen?
What will happen?
What should I do?
Traditional BI
Descriptive
Diagnostic
Predictive
Prescriptive

7. Why is it so popular? why it matters?
 A) More Available and Usable Data
 McKensey: Organizations that use data science to make
decisions are more productive and deliver higher ROI
 Gartner: Organizations that invest in modern data infrastructure
will outperform their peers by up to 20%
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8. Why is it so popular? why it matters?
 B) Increased Awareness of Machine Learning Techniques
 A subset of machine learning algorithms are now more widely
understood since they have been tried and tested by early
adopters such as Netflix and Amazon (Recommendation engines).
 while many people may not know details of the algorithms used,
they now increasingly understand their research/business value.
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9. Why is it so popular? why it matters?
 C) More Accuracte Analysis
 The large volumes of data being collected also enables you to
build more accurate predictive models.
 The larger sample size, the smaller the margin of error. This in turn
increases the accuracy of predictions from your model.
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10. Why is it so popular? why it matters?
 D) Faster and Cheaper Computation
 Today, a smartphone’s processor is up to five times more
powerful than that of a desktop computer 20 years ago.
 Price of computation is decreased
 Capacity of computation is increased
 dramatic gains in technology, productivity, innovations etc.
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11. The Data Science Workflow
Problem Definition
Data Collection and
Preparation
Model
Development
Model
Deployment
Performance
Improvement
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Critical
Very Important
Time Consuming
Fun :D
Iterative
Cumbersome :(
Critical

12. The Data Science Workflow
Problem Definition
Data Collection and
Preparation
Model
Development
Model
Deployment
Performance
Improvement
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• Domain Knowledge
• Separation of Concerns
• Prioritize each problem
• Selection or right data
• Data Transformation
• Missing Values
• Exploratory analysis
• Right algorithm
• Test accuracy
• Test other algorithms
• Validate
• Turning data scientist model
to developer code
(R to C#)
• Monitor the performance of
deployed model
• Re-Training model
• Re-Deploying model
• Re-monitoring

13. The Data Science Workflow
Big Data
Issues (I)
Problem Definition
Data Collection and
Preparation
Model
Development
Model
Deployment
Performance
Improvement
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14. Solutions to overcome the big data issues
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 1- Use advanced research computing
(http://www.arc.ox.ac.uk/)

15. Solutions to overcome the big data issues
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 2-Create and use a Hadoop Cluster
 Open source (Apache)
 It is based on two components
HDFS
MapReduce

16. MapReduce
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17. HortonWorks
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18. Cloudera
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19. MapR
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20. open source won't prevent vendor
lock-in!!!
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21. Third Solution
 Microsoft’s
Cloud Computing
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22. AzureML (Azure Machine Learning)
 Azure ML provides an easy-to-use and powerful set of cloud-
based data transformation and machine learning
tools.
 AzureML Studio (or Studio for short)
 It has many modules for data transformation, analysis,
visualization,…
 It supports R and Python
 It is under heavy development
 www.studio.azureml.net
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23. AzureML Workflow
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Data Input
Data Transformation (Project)
Split Data(training and test)Learning Algorithm
Train the Learning Algorithm
Validate the Algorithm(Score)
Evaluate Model Performance

24. First Experiment:
Predicting Price of Car
AutomobileFullModuleModel02-03-2015
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25. Second Experiment: Using R in ML Studio
AutomobileRTransformation02-03-2015
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26. Third experiment: comparing two models
AutomobileFullModuleTwoModel02-03-2015
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27. Fourth experiment: Creating Web service
 Very easy just some clicks!!!!
 Make: bmw
 Engine-size: 164
 Horse-power: 121
 highway—mpg: 25
 Its actual price is 24,565
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28. Tips
 Data input can come from a variety of data interfaces,
including HTTP connections (any filesharing service like
dropbox, googleDrive, oneDrive), SQLAzure, and Hive Query.
 You can use functionality in all supported R modules (410)
 You can write your utility functions and upload it as another
module
 It is under heavy development
 Two weeks ago the process for web service publication changed
 Two months ago there was no support for Python
 Two months ago around 400 R packages were supported
 …
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29. Big Data Issues (II)
 High dimensional data or wide data
 Using various methods needs knowledge of those methods
 Traditional methods are not efficient enough (unstable)
 Least Squares for example
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30. Advantages of AzureML
 Solutions can be quickly deployed as web services.
 Models run in a highly scalable cloud environment.
 using the R and Python language for solution-specific
functionality.
 It creates minimum code for consuming the web service
in R and Python (and C#)
 It can be run from anywhere
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31. “
”
Big Data is not about Data.
The value in big data is in
Analytics.
GARY KING
Thanks for your attention
Time for Q/A