Client Testimonials

★★★★★

Course Outlines

Code	Name	Duration	Overview
mrkanar	Marketing Analytics using R	21 hours	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.
rneuralnet	Neural Network in R	14 hours	This course is an introduction to applying neural networks in real world problems using R-project software.
dataminr	Data Mining with R	14 hours	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.
bigdatar	Programming with Big Data in R	21 hours	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.
rprogda	R Programming for Data Analysis	14 hours	This course is part of the Data Scientist skill set (Domain: Data and Technology)
MLFWR1	Machine Learning Fundamentals with R	14 hours	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.
dataar	Data Analytics With R	21 hours	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
nlpwithr	NLP: Natural Language Processing with R	21 hours	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 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. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
radvml	Advanced Machine Learning with R	21 hours	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: Use techniques as hyper-parameter tuning and deep learning Understand and implement unsupervised learning techniques Put a model into production for use in a larger application Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
tidyverse	Introduction to Data Visualization with Tidyverse and R	7 hours	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
mlbankingr	Machine Learning for Banking (with R)	28 hours	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
rforfinance	R Programming for Finance	28 hours	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.
intermediaterforfinance	Intermediate R for Finance	21 hours	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
mlfinancer	Machine Learning for Finance (with R)	28 hours	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
dlfinancewithr	Deep Learning for Finance (with R)	28 hours	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
dlforbankingwithr	Deep Learning for Banking (with R)	28 hours	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
shinyrhtml	Shiny, R and HTML: Merging Data Science and Web Development	7 hours	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
rintro	Introduction to R	21 hours	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 (http://www.r-project.org) both on a command line and in a graphical user interface (GUI).
rdataana	R for Data Analysis and Research	7 hours	Audience managers developers scientists students Format of the course on-line instruction and discussion OR face-to-face workshops
frcr	Forecasting with R	14 hours	This course allows delegate to fully automate the process of forecasting with R

Course

Course Date

Course Price [Remote / Classroom]

Neural Network in R - Dubai

Tue, 2018-09-11 09:30

4000USD / 5600USD

Course Discounts

Course	Venue	Course Date	Course Price [Remote / Classroom]
BPMN 2.0 for Business Analysts	Dubai	Mon, 2018-08-13 09:30	5400USD / 7500USD
Apache Mahout for Developers	Dubai	Wed, 2018-08-22 09:30	3600USD / 5200USD
Apache Web Server Administration	Dubai	Sun, 2018-10-14 09:30	5760USD / 8360USD
Building Web Applications with Drupal 7, Views, Fields and Panels	Dubai	Sun, 2018-10-14 09:30	2880USD / 4480USD
Market Forecasting	Dubai	Tue, 2018-11-06 09:30	2880USD / 4480USD

Some of our clients

R Language Training Courses

Client Testimonials

Course Outlines

Audience:

Overview:

Format:

Audience

Duration

Format

Audience

Format of the course

Upcoming Courses

Other regions

Other countries

Consulting

Course Discounts

Some of our clients

Course Catalogue

R Language Training Courses

Client Testimonials

Course Outlines

Audience:

Overview:

Format:

Audience

Duration

Format

Audience

Format of the course

Upcoming Courses

Other regions

Other countries

Consulting

Course Discounts

Course Discounts Newsletter

Some of our clients

Course Catalogue