1. Data Science:
Notes and Toolkits
Dr. Haralambos Marmanis
Waltham, MA
April, 2014
___________________________________
Web: http://www.marmanis.com
Email: h@marmanis.com
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2. What is Science?
• Science is the systematic, data based, pursuit of knowledge
through reason
• Science is not about what we believe, it is about how we arrived
at what we believe
• Science always relied on data, e.g. Copernicus’ and Kepler’s
theories needed Brahe’s data to grow and prosper
• The word “Science”, for most people, points to specific subject
areas such as Physics, Chemistry, etc.
• However, the methodology is not a priori restricted to these
fields; nearly everything that is taught in a university is the
outcome of a scientific endeavor
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3. What is Data Science?
The systematic
data based
pursuit of knowledge
through reason
in non-traditional fields
i.e. applying the same methodology that is applied in physics,
chemistry, biology, etc. to fields like e-Commerce, social networking,
finance, energy, marketing, and so on.
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4. Why should I care?
• Scientists rejoice! There was never a better time to be a data
scientist – click here to see what the business analysts say.
• If you are a scientist today, you can become
the next Newton,
the next Maxwell,
the next Einstein in your field!
• These slides will provide you with an overview of Notes and Tools
that are necessary, although not sufficient, for achieving your own
discoveries
• The content of the slides is taken from my (forthcoming) book:
“The Data Science Revolution:
An overview of the field and its applications”
• Benefits range from “pats on the back” to salary increase or a
generous bonus and from corporate recognition to international
fame! So, your mileage can vary but it’s all good!
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5. Where do I start?
1. The first thing that you need to start is a problem
2. The second is an understanding of the problem. An
understanding implies the following:
• Clear description of the problem
• Clear objectives
• Measurable success criteria
3. The third is a set of data related to the problem
4. The fourth is a set of hypotheses
5. The fifth is a set of tools that will allow us to assess the
validity of our hypotheses based on the available data
Copyright(c)2014H.Marmanis.
Allrightsreserved
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6. Where do I start?
1. The first thing that you need to start is a problem
2. The second is an understanding of the problem. An
understanding implies the following:
• Clear description of the problem
• Clear objectives
• Measurable success criteria
3. The third is a set of data related to the problem
4. The fourth is a set of hypotheses
5. The fifth is a set of tools that will allow us to assess the
validity of our hypotheses based on the available data
Copyright(c)2014H.Marmanis.
Allrightsreserved
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7. Buzzword overview
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• Big Data
• Data Analysis
• Intelligent Web
• Machine Learning
• Artificial Intelligence
• Statistical Analysis

8. What you really need …
Domain
Expertise
ScienceEngineering
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9. Domain expertise
• Each domain defines its own “universe” that, like our physical
universe, waits to be explored by scientific means
• You do not have to be a domain expert yourself but you
should be able to grasp all the fundamentals quickly and
accurately
• Examples (just a few – this is practically endless):
• Supply chain management
• Auctions for Ads
• Financial derivatives pricing
• Mortgage risk assessment
• Drug discovery
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10. Science
• A firm background in mathematics is essential; not just statistics!
• Applied Mathematics
• A firm understanding of the scientific method
1. Aggregate the questions/problems to be answered/solved
2. Conceptualize the problem’s domain
3. Formulate hypotheses  build models
4. Describe the problems based on the models
5. Solve the problems
6. Validate the solutions
7. Repeat steps 3 through 6, as needed
• Scientific computing
• Numerical Methods
• Visualization
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11. Engineering
• Engineering is the systematic application of knowledge for the
purpose of designing, implementing, and maintaining physical
or virtual constructs in a way that optimizes multiple
objectives (e.g. cost, functional effectiveness, operational
efficiency, etc.) while respecting all applicable constraints.
• In the context of Data Science, engineering skills are required
for effectively integrating the scientific solution into the real-
world system (e.g. an online retail store, a social networking
site, a financial tool)
• In particular, software engineering proficiency is crucial, since
all the “objects of observation” are effectively digital and
accessible only through some software system
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12. Computational environments
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Name Language Purpose License
MATLAB C, C++, Java MATLAB General Proprietary
SciLab C,C++, Java, Fortran, Scilab General CeCILL
(Open Source)
Octave General GNU GPL
R C, Fortran, R Statistical, Graphics GNU GPL
Julia C, C++, Scheme General MIT License
ScaVis Java General Mixed
SciPy C, Fortran, Python General BSD

13. Scientific Libraries
• Basic Linear Algebra Subprograms (BLAS) written in Fortran
• Linear Algebra Package (LAPACK) written in Fortran 90
• Numerical Algorithms Group (NAG) libraries
• GraphLab -- GraphLab API is written in C++
• MTJ -- Matrix Toolkit that integrates BLAS and LAPACK in Java
• EJML – linear algebra library written in Java
• Commons Math – Apache project that offers a lightweight,
self-contained, library for mathematics and statistics
• NumPy – support for matrices and high-level mathematical
functions for Python
• SciPy – it includes efficient numerical routines for numerical
integration and optimization
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14. Machine Learning libraries
• Jgap – Genetic algorithms library
• Encog – Neural networks library
• Opt4J – Evolutionary computation library
• Weka – Clustering and classification algorithms
• Yooreeka – Search, recommendations, clustering,
classification, and mathematical analysis
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15. Big Data technologies
• Hadoop – open-source software for reliable, scalable, distributed
computing
• OpenCL – open royalty-free standard for cross-platform, parallel
programming of modern processors found in personal computers,
servers and handheld/embedded devices
• Cloudify – Provision, configure, orchestrate, and monitor large
distributed systems on the cloud
• Spring XD -- a unified, distributed, and extensible system for data
ingestion, real time analytics, batch processing, and data export
• Proactive Parallel Suite -- an open source solution that enables the
orchestration of applications and seamlessly integrates with the
management of high-performance clouds
• Ibis -- an efficient Java-based platform for distributed computing
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16. Copyright(c)2014H.Marmanis.
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The Data Science Revolution:
An overview of the field and its applications