Statistics Training Courses in the United Arab Emirates

Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts who wish to program in R for Excel.

By the end of this training, participants will be able to:

• Toggle and move data between Excel and R.
• Use R Tidyverse and R features for data analytic solutions in Excel.
• Extend their data analytical skills by learning R.

R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn the fundamentals of R programming as they walk through coding in R using financial examples.

By the end of this training, participants will be able to:

• Understand the basics of R programming
• Use R to manipulate their data to perform basic financial operations

Audience

• Programmers
• Finance professionals
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn the basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.

By the end of this training, participants will be able to:

• Understand the fundamental concepts in trading
• Create and implement their first trading strategy using R
• Analyze the performance of their strategy using R

Audience

• Programmers
• Finance professionals
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples.

By the end of this training, participants will be able to:

• Implement advanced R programming techniques
• Use R to manipulate their data to perform more advanced financial operations

Audience

• Programmers
• Finance professionals
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Shiny is an open source R package that provides a web framework for building interactive web applications using R.

In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML.

By the end of this training, participants will be able to:

• Build interactive web applications with R using Shiny

Audience

• Data scientists
• Web developers
• Statisticians

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This course is an introduction to applying neural networks in real world problems using R-project software.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and data analysts who wish to program in R and Python for outlier detection.

By the end of this training, participants will be able to:

• Identify whether data is an anomaly or is an expected value.
• Implement algorithms for anomaly detection.
• Use various techniques and methods to detect anomalies.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (https://www.r-project.org) both on a command line and in a graphical user interface (GUI).

Audience

Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals.

Overview

The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech.

Format

Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course.

This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills.

The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

Description: 

This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS.

Objective:

Covers the basics of how Shiny apps work.

Covers all commonly used input/output/rendering/paneling functions from the Shiny library.

This course is part of the Data Scientist skill set (Domain: Data and Technology)

This course covers advanced topics in R programming.

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines.

It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs.

In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...)

In the case of public courses, mixed examples are used.

Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion.

This course can be delivered as a blended course i.e. with homework and assignments.

Audience

Financial or market analysts, managers, accountants

Course Objectives

Facilitate and automate all kinds of financial analysis with Microsoft Excel

This course allows delegate to fully automate the process of forecasting with R

Audience:

The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment

Goal:

Deep knowledge on Hadoop cluster administration.

Audience

• managers
• developers
• scientists
• students

Format of the course

on-line instruction and discussion OR face-to-face workshops

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

This course has been created for people who require general statistics skills. This course can be tailored to a specific area of expertise like market research, biology, manufacturing, public sector research, etc...

This training course covers advanced statistics. It explains most of the tools commonly used in research, analysis and forecasting. It provides short explanations of the theory behind the formulas.

This course does not relate to any specific field of knowledge, but can be tailored if all the delegates have the same background and goals.

Some basic computer tools are used during this course (notably Excel and OpenOffice)

The course is aimed at anyone interested in statistical analysis. It provides familiarity with Minitab and will increase the effectiveness and efficiency of your data analysis and improve your knowledge of statistics.

Goal:

Mastering the skill work independently with the program SPSS for advanced use, dialog boxes, and command language syntax for the selected analytical techniques.

The addressees:

Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and advanced level and learn the selected statistical models. Training takes universal analysis problems and it is dedicated to a specific industry

Organization – Lab work and practical examples on real data

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts who wish to apply Minitab statistical analysis to Six Sigma projects.

By the end of this training, participants will be able to:

• Use Minitab for performing advanced statistical analysis.
• Apply Six Sigma methodology to specific projects.
• Gain knowledge on Six Sigma projects across industries.

Objective:

Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

Goal:

Learning to work with SPSS at the level of independence

The addressees:

Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.

Audience

Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel.

Course Objectives

This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research.

This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them.

Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: 

What has happened?

• processing and analyzing data
• producing informative data visualizations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimizing processes

The course itself can be delivered either as a 6 day classroom course or remotely over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.

R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.

By the end of this training, participants will be able to:

• Understand the fundamentals of the R programming language
• Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
• Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
• Troubleshoot, integrate deploy and optimize an R application

Audience

• Developers
• Analysts
• Quants

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts and anyone who is interested to learn how to use and integrate Tableau, Python, R, and SQL for data visualization and analysis.

By the end of this training, participants will be able to:

• Perform data analysis using Python, R, and SQL.
• Create insights through data visualization with Tableau.
• Make data-driven decisions for business operations.

R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students.  It covers language fundamentals, libraries and advanced concepts.  Advanced data analytics and graphing with real world data.

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.

Audience

• Developers
• Data scientists
• Banking professionals with a technical background

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

By the end of this training, participants will be able to:

• Understand the fundamental concepts in machine learning
• Learn the applications and uses of machine learning in finance
• Develop their own algorithmic trading strategy using machine learning with R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use R to create deep learning models for finance
• Build their own deep learning stock price prediction model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use R to create deep learning models for banking
• Build their own deep learning credit risk model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

Audience

This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting.

Description

This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data.

It uses standard tools like Microsoft Excel or some Open Source programs (notably R project).

The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...)

The objective of the course is to enable participants to gain a mastery of the basics of modelling and forecasting for government.

SPSS is software for editing and analyzing data.

The objective of the course is to enable participants to gain a mastery of the fundamentals of statistical and econometric modelling.

This instructor-led, live training in UAE (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

By the end of this training, participants will be able to:

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts who wish to use Stata and R to analyze big data for statistical analysis.

By the end of this training, participants will be able to:

• Create statistic models for predicting key interest variables and events.
• Generate descriptive visualizations, summary tables, frequencies, and more.
• Manage and structure large databases to preapare for data analysis.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

• Plan, build, and deploy machine learning models in KNIME.
• Make data driven decisions for operations.
• Implement end to end data science projects.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.

This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions

Tableau helps people see and understand data.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to program in R for Tableau.

By the end of this training, participants will be able to:

• Implement Tableau analytics with R.
• Return values to Tableau with learning algorithms in R.
• Structure and visualize R functions in Tableau.
• Make data driven decisions for business operations.

Tableau helps people see and understand data.

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.

By the end of this training, participants will be able to:

• Use cluster analysis for data mining
• Master R syntax for clustering solutions.
• Implement hierarchical and non-hierarchical clustering.
• Make data-driven decisions to help to improve business operations.

This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization.

For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not.

If you need longer course which covers the very basics of statistical thinking have a look at 5 day "Statistics for Managers" training.

The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.

Mathematica consists of 20 years of creating the most powerful specialized mathematical software engine in the world.   

Its versatility makes it useful not only for doing basic academic calculations but also completing complicated calculations, like programming or numerical data presentations. 

Mathematica integrates software engines doing numerical and symbolic computation, as well as graph analysis software, programming language, document formats and the possibility of publishing your work results. 

Thanks to multiplicity of its functions it’s a priceless tool for mathematicians, physicists, biologists, chemists, financial analysts, sociologists and many more professions that deal with data.

Participants will gain skills to

• perform calculations efficiently
• understanding program commands
• creating text documents
• building charts and graphs
• data presentations

Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.

In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.

By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.

Audience

• Data scientists and engineers, especially with interest in image processing and facial recognition

Format of the course

• Part lecture, part discussion, exercises and intensive hands-on practice, with a final project

The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.

By the end of this training, participants will be able to:

• Perform data analysis and create appealing visualizations
• Draw useful conclusions from various datasets of sample data
• Filter, sort and summarize data to answer exploratory questions
• Turn processed data into informative line plots, bar plots, histograms
• Import and filter data from diverse data sources, including Excel, CSV, and SPSS files

Audience

• Beginners to the R language
• Beginners to data analysis and data visualization

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice