R Language Training Courses
R (R Language) is an open-source 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 has a strong followers among statisticians, engineers and scientists without computer programming skills who find it easy to use.
NobleProg onsite live R training courses demonstrate through hands-on practice various aspects of the R language, including the fundamentals of R programming, advanced R programming and R for Data Analysis and Data Visualization. Our training exercises touch on real-world problems and solutions in areas such as Finance, Banking and Insurance. NobleProg R training courses range from beginner courses to advanced courses and are popular among companies wishing to adopt R for developing Machine Learning and Deep Learning applications.
R training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local R training can be carried out live on customer premises or in NobleProg local training centers.
Client Testimonials
R Language Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| rintrob | Introductory R for Biologists | 28 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. |
| danagr | Data and Analytics - from the ground up | 42 hours | 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. |
| 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 |
| 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 |
| bigddbsysfun | Big Data & Database Systems Fundamentals | 14 hours | The course is part of the Data Scientist skill set (Domain: Data and Technology). |
| 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 |
| rprogda | R Programming for Data Analysis | 14 hours | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| 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. |
| dmmlr | Data Mining & Machine Learning 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. |
| 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 |
| predmodr | Predictive Modelling 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. |
| 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 |
| 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. |
| intror | Introduction to R with Time Series Analysis | 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 a wide variety of packages for data mining. |
| 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 |
| rneuralnet | Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software. |
| advr | Advanced R | 7 hours | This course covers advanced topics in R programming. |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 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. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python 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 Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| 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 ...) |
| 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 |
| 67795 | Numerical Methods | 14 hours | This course is for data scientists and statisticians that have some familiarity with numerical methods and have at least one programming language from R, Python, Octave, and some C++ options. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose of this course is to give a practical introduction in numerical methods to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| 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. |
| 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 |
| 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. |
| deepmclrg | Machine Learning & Deep Learning with Python and R | 14 hours | |
| foundr | Foundation R | 7 hours | The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data. |
| bigdatar | Programming with Big Data in R | 21 hours | |
| 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 |
| 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. |
| 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. |
| rlang | 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. |
| 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 |
| bspkrintro | R Fundamentals | 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. |
| 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. |
| DatSci7 | Data Science Programme | 245 hours | The explosion of information and data in today’s world is un-paralleled, our ability to innovate and push the boundaries of the possible is growing faster than it ever has. The role of Data Scientist is one of the highest in-demand skills across industry today. We offer much more than learning through theory; we deliver practical, marketable skills that bridge the gap between the world of academia and the demands of industry. This 7 week curriculum can be tailored to your specific Industry requirements, please contact us for further information or visit the Nobleprog Institute website www.inobleprog.co.uk Audience: This programme is aimed post level graduates as well as anyone with the required pre-requisite skills which will be determined by an assessment and interview. Delivery: Delivery of the course will be a mixture of Instructor Led Classroom and Instructor Led Online; typically the 1st week will be 'classroom led', weeks 2 - 6 'virtual classroom' and week 7 back to 'classroom led'. |
| 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 |
Upcoming Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| NLP: Natural Language Processing with R - Cardiff | Wed, 2018-05-09 09:30 | £3900 / £4800 |
| Machine Learning Fundamentals with R - Bristol, Temple Gate | Wed, 2018-05-09 09:30 | £2600 / £3100 |
| Numerical Methods - Portsmouth Technopole | Wed, 2018-05-09 09:30 | £2200 / £2500 |
| Foundation R - Leeds | Thu, 2018-05-10 09:30 | £1300 / £1500 |
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