Introductory R (Basic to Intermediate) Training Course

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

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

This instructor-led live training in Australia (online or onsite) is designed for business analysts wishing to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to rapidly buy and sell securities at specific increments.
• Reduce trading costs by employing algorithmic trading techniques.
• Automatically monitor stock prices and execute trades.

Big Data denotes technologies designed for the storage and processing of extensive data sets. Originally pioneered by Google, these Big Data solutions have evolved and inspired numerous similar initiatives, many of which are available as open-source software. R is a widely used programming language within the financial sector.

This instructor-led, live training in Australia (online or on-site) is aimed at data analysts who wish to programme 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 an essential instrument for businesses today. Our focus throughout the course is on cultivating practical, hands-on data analysis skills. The goal is to equip participants with the ability to provide evidence-based answers to key questions:

What has occurred?

• processing and analysing data
• creating informative data visualisations

What will occur?

• forecasting future performance
• evaluating forecasts

What should occur?

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

This instructor-led, live training in Australia (online or onsite) is tailored for beginner-level professionals looking to clean and analyse data, perform statistical forecasting, and generate insightful visualisations using these tools.

By the conclusion of this training, participants will be capable of:

• Gaining a foundational understanding of Python, R, Power Query, and Power BI for data analysis.
• Cleaning and organising datasets using Python and Power Query.
• Conducting statistical analysis and forecasting with R.
• Developing professional dashboards and reports with Power BI.
• Effectively integrating and analysing data from diverse sources.

R is a widely adopted, open-source platform for statistical computing, data analytics, and graphics. This course provides an introduction to the R programming language for students, covering foundational language elements, key libraries, and advanced concepts. Participants will engage in advanced data analytics and visualisation techniques using real-world datasets.

Audience

Developers and data analysts

Duration

3 days

Format

Lectures and Hands-on Exercises

This instructor-led, live training in Australia (online or onsite) is tailored for intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

Upon completion of this training, participants will be able to:

• Grasp 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.

This instructor-led, live training in Australia (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.
• Make data driven decisions for operations.
• Implement end to end data science projects.

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 around extracting insights and meaning from this data. Utilising 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 analyse and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

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 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 a widely adopted programming language within the financial sector. It powers a broad spectrum of financial applications, from core trading systems to sophisticated risk management tools.

In this instructor-led live training, participants will discover how to leverage R to build practical applications that address specific challenges in finance.

Upon completion of this course, participants will be able to:

• Grasp the fundamental concepts of the R programming language
• Choose and apply R packages and techniques to organise, visualise, and analyse financial data sourced from various platforms (CSV, Excel, databases, web APIs, etc.)
• Construct applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimise R applications

Audience

• Developers
• Analysts
• Quants

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training is designed to offer solutions for key problems encountered by finance professionals. However, if you have a specific topic, tool, or technique you wish to include or elaborate on, please contact us to arrange.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly being adopted by managers and data analysts within both corporate sectors and academia. R has also gained popularity among statisticians, engineers, and scientists who lack computer programming skills, owing to its user-friendly nature. Its growing appeal stems from the expanding application of data mining for objectives such as setting competitive prices, accelerating drug discovery, and refining financial models. R offers a vast array of packages tailored for data mining tasks.

R Markdown serves as an authoring framework that integrates executable R code with narrative text to produce dynamic and reproducible documents.

Delivered via an instructor-led live training session (available online or onsite), this course targets beginner to intermediate R users seeking to generate automated, dynamic reports for internal documentation, research publications, or web publishing using R Markdown.

Upon completion of this training, participants will be capable of:

• Creating R Markdown documents containing embedded R code chunks and formatted text.
• Generating outputs in various formats, including HTML, PDF, and Word.
• Utilising parameterised reports and conditional logic to produce dynamic content.
• Customising document appearance through themes, templates, and LaTeX for a professional presentation.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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

This course is designed for data scientists and statisticians who possess foundational R and C++ coding skills and require advanced R programming capabilities.

The primary objective is to deliver a practical, advanced R programming training experience to participants keen on applying these methods in their professional roles.

Industry-specific examples are utilised to ensure the training resonates with the audience.