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Every organization in every industry needs professionals and business leaders who can work with data

Data Science & Machine Learning Workshop
25 - 27 April, 2016, Media Rotana, Dubai

A comprehensive 3-day training program for hands-on introduction to big data, data science, and machine learning models, methods and algorithms.

The workshop will take participants through the conceptual and applied foundations of the subject. Topics covered include:

   Data science and big data engineering
   Machine learning theory, types of learning
   Techniques, models and methods

Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of data science and machine learning.


This specialised field demands multiple skills not easy to obtain through conventional curricula.

So, be part of the data revolution by attending this workshop to learn the fundamentals of data science and machine learning and leave armed with practical skills to extract value from data. With the knowledge and skills gained from this workshop, you will be able to tackle complicated big data and machine learning challenges. Attend the workshop and develop the foundation level competence in data science and finding, manipulating, managing, interpreting and visualizing data.

"If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what's getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data: databases, machine learning, econometrics, statistics, visualization, and so on"
- Hal Varian, Chief Economist at Google

Top 10 Reasons to Attend

  • Understand the art and science of discovering patterns and making intelligent predictions from big data.

  • Define machine learning, why it matters, and discuss its relationship to analytics, data science, and big data.

  • Machine learning fundamentals, the importance of algorithms, and machine learning as a service.

  • Basics of R platform, programming language concepts, common and useful R commands, and applying machine learning methods.

  • Doing machine learning - Understanding the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.

  • Practically learn the most commonly used machine learning methods, covering both supervised and unsupervised learning.

  • Develop understanding of which algorithm to choose based on the analytics challenge and the data you have.

  • Develop understanding of which algorithm to choose based on the analytics challenge and the data you have.

  • Discover how to understand, interpret and convey the results of data science life cycle.

  • Be able to appreciate the trade-offs involved in choosing particular techniques for particular problems.

Who Should Attend?

  Government Services & Private Sector



  Media & Entertainment

  Tourism & Hospitality

  Banking & FInancial


  Power & Utilities


Senior level executives and decision-makers of medium to large organizations from Banking, IT, Government, Media, Telecoms, Hospitality, Retail, Travel, Healthcare, and Entertainment sectors including:

  CIO’s & Heads of IT

  Chief Technology Officers

  System and Business Analysts

  Database Administrators

  Project Managers

  Heads of Data Analytics, Modeling and Mining

  Data Architects and Scientists

  Customer and Business Intelligence Specialists

The art and science of making sense of data is a highly sought after skill in today’s data driven world.

Data science isn’t just for data scientists. In massively connected data driven world, it is imperative that the workforce of today and tomorrow is able to understand what data is available and use scientific methods to analyze and interpret it.

Data science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, telecom, manufacturing, advertising, retail, and others.

Launch your data science career with this practical workshop. Build a solid foundation in machine learning using R and start exploring data-related careers.


The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the 3 days, participants will be ready to implement the machine learning algorithms using data science on your own data, and immediately generate value.

The workshop will take participants through the conceptual and applied foundations of the subject. Topics covered include:

   R for Statistical Analysis and Machine Learning

   Machine learning theory, types of learning

   Techniques, models and methods

Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of data science and machine learning.

Lab 1 - R IDE Setup and Basic Commands

Lab 2 – Data Frames, Functions, using Packages and Basic Plotting

Lab 3 – Feature Selection

Often we have far too many features to work with. In this lab, we see how to use pairwise statistical tests to select high-information features and discard low-information features.

Lab 4 – Feature Transformation

Principle component analysis creates a new set of features as linear combinations of the original features. These are ordered by the amount of variance each contains and selecting a subset of high-variance principle components provides a powerful way to both reduce the number of features used and ensure that those used have high information content.

The ability to derive value from data is the objective. The process for extracting the value is fundamentally related to the ability to define and manipulate the data which in turn is related to the ability to access, harness and consume the data. Traditionally, this has required extensive statistical capabilities in both technology and people skills. This session will walk through the capabilities that Microsoft is providing across the platform for Advanced Analytics

Lab 5 - Linear Regression

Use ordinary least squares to model the relationship between (X1) the education requirements of a career and (X2) its remuneration and (Y) the prestige in which the vocation is held. This provides experience with an important foundational algorithm.

Lab 6 - Poisson Regression

Use Poisson regression to model the relationship between (X1) wind and (Y) ozone levels. This looks at one of the typical cases where linear regression is unsuitable and provides an introduction to the use of generalize linear models in R.

Lab 7 - Polynomial Regression

Use polynomial regression to model clearly non-linear synthetic data. This provides a clear example of basis transformation.

Lab 8 - Logistic Regression

Use logistic regression to model proportional and binary data. In the first case we model the proportion of women who have reached menarche versus their age in years, and in the second, the probability of a seed germinating versus its age in days. This gives us a second look at how to use generalized linear models in R, and provides experience with another important foundational algorithm.

Lab 9 - Linear Discriminant Analysis and Quadratic Discriminant Analysis

We use LDA and QDA to classify synthetic data. These provide a simple introduction to the use of Gaussian distributions, as well as exposure to these surprisingly well performing techniques.

Lab 10 - Random Forests and Adaboost

Use these tree-based ensemble techniques to model passenger survival in the Titanic disaster. Simple but very high performing, these methods are favorites of data scientists. These exercises provide experience using these important techniques in R.

Lab 11 - Putting it all together

Study Guide: The Essentials of Data Analytics and Machine Learning

As a participant in this workshop, you will receive an exclusive copy of this study guide. The guide provides both a deep understanding of the techniques and practices of machine learning and exposes a wide set of resources capable of being wielded by the data scientist and analysts in their work. Readers will encounter explanations of the theory behind the algorithms and models they are exposed to, giving them an understanding of the strengths and weaknesses of each which they should be able to use to reason about suitable approaches to real life problem – and to communicate such reasoning to other stakeholders in such problems.


The School of Data Science trainers are passionate about meeting each participants learning needs. They have been chosen both for their extensive practical data science and machine learning experience and for their ability to educate and interact with natural empathy.

Dr. Mike Ashcroft

Senior Data Science Researcher & Machine Learning Expert
University of Uppsala, Sweden and The School of Data Science, UK

Dr. Mike Ashcroft is a senior researcher and data scientist. He is an experienced machine learning practitioner, with particular interests in graphical models for causal diagnostics, predictive analytics and system control. He completed his doctorate at the University of Melbourne, and his post-doc with Fudan University in Shanghai and the Australian Government. He now runs lectures at Uppsala University in Sweden. He is a member in the European Network for Industrial and Business Statistics (ENBIS) and the Swedish Artificial Intelligence Society (SAIS). He also has experience in software development for data science tools, including remote, distributed and high-performance real-time systems.

Ali Syed

Chief Data Scientist and Strategist
Persontyle, UK and European Data Science Academy, EU

As a global data and analytics thought leader, Ali is collaborating with thinkers, researchers, designers, makers, doers, and business leaders. He has more than 16 years of professional experience and success assisting public and commercial organisations in using data analytics, insights and machine intelligence as a value amplifier. He works with people to understand and translate their aspirations into data and analytical solutions that enhance their ability to make choices, better decisions, realise performance gains and uncover opportunities. Before founding Persontyle, Ali worked with some of the leading technology and consulting organisations of the world namely PwC, KPMG BearingPoint, Sapient, EMC, UBS, NHS UK, and Capgemini.

Kevin Ashby

Application Platform Product Marketing Manager
Microsoft Gulf

Kevin has been involved with Application platforms, specializing in Database technologies, Business Intelligence and Cloud, for more than 25 years. Kevin is and has been a strong supporter of professional communities, being a long standing contributor to the UK Oracle Users group, Chairing and Co-Chairing special interest groups and supporting the SQL Server User community in the UAE. Kevin has been with Microsoft in the UAE since 2012 as the Application Platform Product Manager with a focus on SQL Server and Microsoft’s Cloud, Windows Azure.

Dr. Louis Dorard

General Chair

Dr. Louis Dorard is General Chair of the PAPIs conferences, author of Bootstrapping Machine Learning, and an independent consultant. Louis holds a PhD in Machine Learning from University College London.


Hessa St - Dubai
United Arab Emirates
Ph :+971 4 435 0000


Data Science & Machine Learning Workshop
25 - 27 April, 2016, Dubai


Are you looking to showcase big data, data science and analytics technologies and services in front of the professional industry gathering? If the answer is yes then get in touch!

Be part of this amazing workshop to stand out from your competition and gain valuable brand exposure. Sponsoring the Data Science and Machine Learning Workshop gives you following advantages:

Make an Impact
Showcase your big data, analytics and BI products and solutions.

Position Your Brand
Present your brand and capture new leads and opportunities.

Spread Awareness
Share your knowledge and expertise with decision makers and help people connect with your products and services.

Get in touch to tell us what you're trying to achieve: we can then outline some of the marketing possibilities and tailor a package to suit your needs and budget.

To enquire about sponsoring and exhibiting, contact our team!

Email at
or call us on +971 50 602 4248


Persontyle is a global consulting and machine intelligence products company, providing a broad range of services and solutions in strategy, digital transformation, data science, machine learning, IoT, and digital talent development. Persontyle brings together deeply experienced business specialists, programmers, data scientists, machine learning experts, data analysts, UX designers and data engineers with diverse backgrounds to tackle important business innovation challenges. We are a team of creative, passionate and honest people who band together on challenges from data engineering to machine learning. Persontyle is headquartered in London, UK.

The School of Data Science is an education initiative to help meet the world’s demand for professionals and leaders skilled in developing and utilizing automated and intelligent methods of using data a strategic resource. We aim to bring accessible, affordable, practical, and interactive data science and engineering education to the world. We offer training programs and tailored corporate learning solutions to cover the concepts, technology and applied practices you'll need throughout the entire lifecycle, from asking the relevant questions to making predictions using machine learning models and visualizing results.

Brilliant Basics "bb"​ a design-led global product design studio. We’re in the business of products. In 2012, three seasoned agency, consulting and design studio professionals came together to create the kind of business that disrupted the traditional agency ‘projects’ model - something more than just another digital shop. We create products that make life simply better. We started with a simple belief that still holds true: brilliance comes from getting the basics right. We work across every area of our clients' business and digital ecosystem with the same rigour, from concept delivery, interactive prototypes and mobile strategies to digital transformation roadmaps in industry verticals such as financial services, utilities, higher education, private equity partnerships, retail, healthcare & lifestyle, and TMT - telecommunications, media and technology.
We are present in London, Norwich, Dubai, Singapore, Hong Kong and New York.

Media Partners


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By calling us on +971 50 602 4248
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Data Science Middle East

Data Science Middle East (DSME) is an open collaboration programme for data talent and digital leadership development. For details, collaborations and industry partnership opportunities, please email

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