Online or onsite, 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 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 as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live R Language training can be carried out locally on customer premises in South Carolina or in NobleProg corporate training centers in South Carolina.
NobleProg -- Your Local Training Provider
SC, Charleston - Faber Center
4000 S. Faber Place Drive Suite 300, Charleston, united states, 29405
The venue is located in the Farber Center where you can also find the North American Merchant Services, Lima One Capital and ...
The venue is located in the Farber Center where you can also find the North American Merchant Services, Lima One Capital and Charleston Country Economic Development.
SC, Charleston - Meeting Street
170 Meeting Street, Charleston, united states, 29401
The venue is located in the hear of vibrant cultural area of downtown Charleston. Only 20 minutes away from Charleston Intern...
The venue is located in the hear of vibrant cultural area of downtown Charleston. Only 20 minutes away from Charleston International Airport with short walk to Museums and parks.
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
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.
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 South Carolina (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 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.
This instructor-led, live training in South Carolina (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 South Carolina (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.
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 South Carolina (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.
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
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.
This instructor-led, live training in South Carolina (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.
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
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.
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.
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 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 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 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.
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).
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Testimonials (14)
Well thought out and high grade planning materials.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Course - Forecasting with R
the clarity with which he explained the entire course, as well as the willingness to return to the syllabus when necessary
Carlos Eloy - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
Machine Translated
Wasn't boring, the trainer could keep the attention, the topics were covered in depth.
Marta - Ministerstwo Zdrowia
Course - Advanced R Programming
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.
Victor Prado - Global Knowledge Network Training Ltd
Course - R
The exercises.
Elena Velkova - CEED Bulgaria
Course - Predictive Modelling with R
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
Course - Introduction to R with Time Series Analysis
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna - Birmingham City University
Course - Foundation R
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.
Sonja Steiner - BearingPoint GmbH
Course - R Programming for Data Analysis
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
I really was benefit from the real life practical examples.
Wioleta
Course - Data and Analytics - from the ground up
The flexible and friendly style. Learning exactly what was useful and relevant for me.
Jenny
Course - Advanced R
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The trainer was so knowledgeable and included areas I was interested in.
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