Online R Language Training in New Zealand

R Markdown is an authoring framework that combines executable R code with narrative text to create dynamic and reproducible documents.

This instructor-led, live training (available online or on-site) is designed for beginner to intermediate R users who want to produce automated, dynamic reports using R Markdown for internal documentation, research publications, or web publishing.

By the end of this training, participants will be able to:

• Create R Markdown documents with embedded R code chunks and formatted text.
• Generate outputs in multiple formats, including HTML, PDF, and Word.
• Use parameterised reports and conditional logic to generate dynamic content.
• Customise document appearance using themes, templates, and LaTeX for professional presentation.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and hands-on practice.
• Real-time implementation in a live-lab environment.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or on-site) is designed for data analysts and anyone keen to learn how to use and integrate Tableau, Python, R, and SQL for data visualisation and analysis.

By the end of this training, participants will be able to:

• Conduct data analysis using Python, R, and SQL.
• Generate actionable insights through data visualisation with Tableau.
• Make data-driven decisions to enhance business operations.

This instructor-led, live training in New Zealand (online or on-site) is designed for beginner-level professionals who wish to clean and analyse data, make statistical projections, and create insightful visualisations 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 organise datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyse data from multiple sources effectively.

Shiny is an R package that enables users to build interactive and dynamic web applications directly from R, without requiring knowledge of JavaScript or complex web frameworks.

This instructor-led, live training (online or onsite) is aimed at intermediate-level R users who wish to build and deploy custom web applications using Shiny for data visualisation, user interaction, and reporting.

By the end of this training, participants will be able to:

• Create interactive web applications using Shiny and R.
• Design user interfaces with reactive elements and dynamic inputs.
• Incorporate data visualisations and reporting features into Shiny apps.
• Deploy and share Shiny apps locally or online via Shiny Server or Posit Cloud.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (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 modelling.
• Compare Stata’s functionalities with SPSS and R.
• Integrate Stata with R for seamless statistical computing.
• Develop and automate workflows using Stata and R.

R is a widely used, open-source environment for statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering language fundamentals, libraries, and advanced concepts. Participants will explore advanced data analytics and visualisation using real-world datasets.

Audience

Developers and data analytics professionals

Duration

3 days

Format

Lectures and hands-on sessions

Data analytics is a vital tool in today's business landscape. Throughout this course, we focus on building practical, hands-on skills for data analysis. The goal is to empower participants to provide evidence-based answers to key questions: 

What has happened?

• processing and analysing data
• creating insightful data visualisations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimising processes

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 centres on extracting insights and meaning from this data. Using the R language and Natural Language Processing (NLP) libraries, we blend concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically interpret the meaning behind text data. Data samples are available in various languages, depending on customer requirements.

By the end of this training, participants will be able to prepare data sets (both large and small) from disparate sources and apply the appropriate algorithms to analyse and report on their significance.

Course Format

• Part lecture, part discussion, with extensive hands-on practice and occasional tests to assess understanding

R is a widely used programming language within the financial sector. It powers a broad spectrum of financial applications, from core trading systems to risk management platforms.

In this instructor-led, live training, participants will learn how to harness R to build practical solutions for a range of specific finance-related challenges.

By the end of this training, participants will be able to:

• Grasp the fundamentals of the R programming language
• Select and apply R packages and techniques to organise, visualise, and analyse financial data from diverse sources (CSV, Excel, databases, web, etc.)
• Develop applications that address issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimise an R application

Audience

• Developers
• Analysts
• Quants

Course Format

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

• This training is designed to address some of the key challenges faced by finance professionals. However, if you have a specific topic, tool, or technique you would like to explore in greater depth or add to the curriculum, please contact us to arrange this.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level advanced R users who wish to leverage R to build faster workflows, enhance code quality, and tackle more complex analytical tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimise code, and produce reproducible reports.

This instructor-led, live training in New Zealand (online or onsite) is aimed at business analysts who wish to automate trading using algorithmic strategies, Python, and R.

By the end of this training, participants will be able to:

• Use algorithms to buy and sell securities rapidly in specialised increments.
• Reduce trading-related costs through the application of algorithmic trading.
• Automatically monitor stock prices and execute trades.

Big Data is a term referring to solutions designed for storing and processing large-scale datasets. Initially developed by Google, these Big Data solutions have evolved and inspired numerous similar projects, many of which are available as open-source. R is a widely used programming language within the financial industry.

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for beginner-level data analysts who wish to use R to manipulate data, perform foundational data analysis, and create compelling visualisations to derive insights.

By the end of this training, participants will be able to:

• Understand the fundamentals of R programming.
• Apply core data science processes.
• Create visual representations of 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 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 New Zealand (available online or on-site) is designed for data analysts who wish to programme with R in SAS for cluster analysis.

By the end of this training, participants will be able to:

• Apply cluster analysis for data mining purposes.
• Master R syntax for developing clustering solutions.
• Implement both hierarchical and non-hierarchical clustering techniques.
• Make data-driven decisions to help improve business operations.

This instructor-led, live training in New Zealand (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.

This instructor-led, live training in New Zealand (online or on-site) is designed for data scientists who wish to program in Python and R within KNIME.

By the end of this training, participants will be able to:

• Plan, build, and deploy machine learning models in KNIME.
• Make data-driven decisions to support operations.
• Implement end-to-end data science projects.

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.
• Visualise data to quickly 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 designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts within both corporate and academic settings. R has also gained popularity among statisticians, engineers, and scientists who lack formal programming training, finding it intuitive and easy to use. Its growing prominence stems from the expanding use of data mining for diverse objectives, such as setting advertising prices, accelerating drug discovery, and refining financial models. R offers a wide range of packages specifically tailored for data mining tasks.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts across corporations and academic institutions. R has also gained popularity among statisticians, engineers, and scientists who may not have extensive programming backgrounds, thanks to its user-friendly design. Its growing appeal stems from the widespread use of data mining for diverse purposes, such as setting advertising prices, accelerating drug discovery, and refining financial models. R offers a vast array of packages tailored for data mining tasks.

This course is designed for data scientists and statisticians who already possess foundational skills in R and C++ coding and are seeking to advance their R programming capabilities.

The goal is to deliver a practical, advanced R programming course tailored for participants eager to apply these methods in their professional work.

Sector-specific examples are incorporated to ensure the training is directly relevant to the audience's industry context.

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 (available online or on-site) is designed for data analysts who wish to leverage Stata and R to analyse big data for statistical purposes.

By the end of this training, participants will be able to:

• Create statistical models to predict key variables and events of interest.
• Generate descriptive visualisations, summary tables, frequency distributions, and more.
• Manage and structure large databases in preparation for data analysis.

By the conclusion of this training, participants will be able to:

This instructor-led, live training in New Zealand (online or on-site) is tailored for data scientists who wish to program in R for use with Tableau.

• Implement Tableau analytics using R.
• Return values to Tableau using machine learning algorithms in R.
• Structure and visualise R functions in Tableau.
• Make data-driven decisions to support business operations.

Description:

This course is designed to teach R users how to create web applications without needing to learn cross-browser HTML, JavaScript, and CSS.

Objective:

It covers the fundamentals of how Shiny applications operate.

It also explores all commonly used input, output, rendering, and paneling functions available in the Shiny library.

This instructor-led, live training in New Zealand (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.
• Utilise the 'forecast' package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

Audience

• Managers
• Developers
• Scientists
• Students

Course Format

Online instruction and discussion, or in-person workshops

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts within both corporate and academic environments. R has also gained popularity among statisticians, engineers, and scientists who lack programming expertise but find the language intuitive to use. Its growing appeal stems from the expanding role of data mining in achieving diverse objectives, such as setting optimal advertising prices, accelerating drug discovery, and refining financial models. R offers a vast array of packages tailored for data mining tasks.

This course explores the manipulation of objects in R, including reading data, accessing R packages, writing custom functions, and creating informative visualisations. It also covers data analysis using common statistical models. Participants will learn to utilise the R software (https://www.r-project.org) both via the command line and through a graphical user interface (GUI).