R Training Courses

R Training

R Programming Language, R Software Environment for statistical computing and graphics courses

Client Testimonials

Neural Network in R

new insights in deep machine learning

Josip Arneric - Faculty of Economics and Business Zagreb

Forecasting with R

Overview and understanding how big the topic is

British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA

A practical introduction to Data Analysis and Big Data

Overall the Content was good.

Sameer Rohadia - Continental AG / Abteilung: CF IT Finance

Neural Network in R

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Tea Poklepovic - Faculty of Economics and Business Zagreb

A practical introduction to Data Analysis and Big Data

Willingness to share more

Balaram Chandra Paul - MOL Information Technology Asia Limited

A practical introduction to Data Analysis and Big Data

presentation of technologies

Continental AG / Abteilung: CF IT Finance

Forecasting with R

A lot of knowldege - theoretical and practical

Anna Alechno - British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA

Introduction to R

What did you like the most about the training?:

Able to do hands-on and get help from trainer on how to go about working on difficult questions.

Faeiza Ab Rahman - Seagate Singapore International Headquarters Pte. Ltd

Introduction to R

Working with 1:1 with Gunnar.

Bryant Ives - EY

A practical introduction to Data Analysis and Big Data

It covered a broad range of information.

Continental AG / Abteilung: CF IT Finance

Market Forecasting

What did you like the most about the training?:

Trainer's subject knowledge was such that complex subjects seemed easy, and he genuinely inspired me with enthusiasm for the topic. My session worked out as 1:1, and it was a real privilege to get the benefit of such a high calibre of trainer in the context of personal tuition.

Harry Frost - Babcock International

Introduction to R

Hands-on learning by doing

Guihong Chen - TCF

Forecasting with R

his knowlede and practical exemples

Irina Tulgara - British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA

Neural Network in R

Graphs in R :)))

- Faculty of Economics and Business Zagreb

Introduction to R

I really appreciated that he (the trainer) was a practitioner and had that perspective.

Bradley Olson - TCF

Introduction to R

Trainer was flexible -- he tried to address our issues.

The course was well organized and the trainer did have the breadth of knowledge, both in the R program and finance industry, necessary to help us.

George Rezek - TCF

Market Forecasting

What did you like the most about the training?:

Trainer's subject knowledge was such that complex subjects seemed easy, and he genuinely inspired me with enthusiasm for the topic. My session worked out as 1:1, and it was a real privilege to get the benefit of such a high calibre of trainer in the context of personal tuition.

Harry Frost - Babcock International

Introduction to R

Hands on examples were the most helpful.

Sean Kaukas - NGAM Advisors, L.P.

Introduction to R

He (the trainer) is excellent. Very patient, can listen well to our questions.

We liked his patience and pace..

Ellen Lentz- Genentech Inc

Market Forecasting

Real-world, practical application of statistical modeling; keeping theory at bay and business needs in the forefront.

Thanks Bernard and Iza. It was a very good course and I appreciate your work.

Shelly Kunkle - Michelin, North America

Data Mining with R

very tailored to needs

Yashan Wang - MoneyGram International

Introduction to R

He (the trainer) was excellent.

I liked the pace and the focus on principles at the beginning, not skipping over the detail.

Geoff Copps - Mediabrands

R Course Outlines

Code Name Duration Overview
kdd Knowledge Discover in Databases (KDD) 21 hours Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience     Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course     After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
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
BigData_ A practical introduction to Data Analysis and Big Data 35 hours Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress.
webappsr Building Web Applications in R with Shiny 7 hours 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.
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
mrkfct Market Forecasting 14 hours 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 ...)
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.
rneuralnet Neural Network in R 14 hours This course is an introduction to applying neural networks in real world problems using R-project software.
rprogadv Advanced R Programming 7 hours 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
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
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.
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.
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
dsbda Data Science for Big Data Analytics 35 hours Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
datamodeling Pattern Recognition 35 hours This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners  
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

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