Local, instructor-led live R (R Language) 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 exercizes 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 as "onsite live training" or "remote live training". Charlotte onsite live R Language trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
Learn R Language in our training center in Charlotte. The venue is located just next door to the First United Methodist Church and across the street from Woodie's Auto Service. Read more
Testimonials
★★★★★
★★★★★
The trainer, the food, and the space were all great.
Canada Revenue Agency
Course: R
I genuinely enjoyed the hands passed exercizes.
Yunfa Zhu - Environmental and Climate Change Canada
Course: Foundation R
I like actually writing code with sample data and annotating the script for future reference.
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 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.
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.
This instructor-led, live training in Charlotte (onsite or remote) 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 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 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.
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.
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 instructor-led, live training in Charlotte (onsite or remote) 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 (onsite or remote) 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 Charlotte (onsite or remote) 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.
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
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
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.
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.
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
This instructor-led, live training in Charlotte (onsite or remote) 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.
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 Charlotte (onsite or remote) 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.
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
- turning data into evidence-based business decisions - optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
[R](http://www.r-project.org/) 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.
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
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. 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
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