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 trainings in Olsztyn can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Olsztyn
sale szkoleniowe NobleProg, ul. Gietkowska 6a, Olsztyn, poland, 10-170
Training and conference room with full audio-visual equipment.
Functional furniture provides the possibility of setting them in a conference layout, school, workshop & cinema, or cinema, depending on the client's needs.
This instructor-led, live training in Olsztyn (online or onsite) is aimed at data analysts and anyone who is interested to learn how to use and integrate Tableau, Python, R, and SQL for data visualization and analysis.
By the end of this training, participants will be able to:
Perform data analysis using Python, R, and SQL.
Create insights through data visualization with Tableau.
Make data-driven decisions for business operations.
This instructor-led, live training in Olsztyn (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.
By the end of this training, participants will be able to:
Understand the basics of Python, R, Power Query, and Power BI for data analysis.
Clean and organize datasets using Python and Power Query.
Perform statistical analysis and projections with R.
Create professional dashboards and reports with Power BI.
Integrate and analyze data from multiple sources effectively.
This instructor-led, live training in Olsztyn (online or onsite) is aimed at intermediate-level to advanced-level computer science professionals who wish to leverage Stata for statistical analysis and integrate it with R.
By the end of this training, participants will be able to:
Effectively use Stata for data analysis and statistical modeling.
Compare Stata’s functionalities with SPSS and R.
Integrate Stata with R for seamless statistical computing.
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 Olsztyn (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 Olsztyn (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.
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 Olsztyn (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.
By the end of this training, participants will be able to:
This instructor-led, live training in Olsztyn (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.
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 Olsztyn (online or onsite) is aimed at beginner-level professionals who wish to gain a mastery of the fundamentals of R and how to work with data.By the end of this training, participants will be able to:
Understand the R programming environment and RStudio interface.
Import, manipulate, and explore datasets using R commands and packages.
Perform basic statistical analysis and data summarization.
Generate visualizations using both base R and ggplot2.
Manage workspaces, scripts, and packages effectively.
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 Olsztyn (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.
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 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
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
This instructor-led, live training in (online or onsite) 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 Olsztyn (online or onsite) 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
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.
This instructor-led, live training in Olsztyn (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.By the end of this training, participants will be able to:
Understand the fundamentals of forecasting techniques in R.
Apply exponential smoothing and ARIMA models for time series analysis.
Utilize the ‘forecast’ package to generate accurate forecasting models.
Automate forecasting workflows for business and research applications.
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 (17)
it was informative and useful
Brenton - Lotterywest
Course - Building Web Applications in R with Shiny
Many examples and exercises related to the topic of the training.
Tomasz - Ministerstwo Zdrowia
Course - Advanced R Programming
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
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
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course - Introduction to R with Time Series Analysis
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
I really enjoyed the knowledge of the trainer.
Stephanie Seiermann
Course - R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
A lot of knowledge - theoretical and practical.
Anna Alechno
Course - Forecasting with R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
Luke Pontin
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
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
I genuinely enjoyed working 1:1 with Gunner.
Bryant Ives
Course - Introduction to R
The trainer was so knowledgeable and included areas I was interested in.
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