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:
5
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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Cloudera
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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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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

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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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Third experiment: comparing two models
AutomobileFullModuleTwoModel02-03-2015
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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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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