Introductory R (Basic to Intermediate) Training Course

This instructor-led, live training in Taiwan (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 Taiwan (online or onsite) is designed for business analysts who wish to automate their trading using algorithmic trading, Python, and R.

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

• Utilize algorithms to rapidly buy and sell securities at specific increments.
• Lower trading costs by leveraging algorithmic trading techniques.
• Automatically monitor stock prices and execute trades without manual intervention.

Big Data refers to solutions designed for the storage and processing of large-scale data sets. Initially developed by Google, these Big Data solutions have evolved and inspired numerous similar projects, many of which are available as open-source software. R is a widely used programming language in the financial industry.

This instructor-led, live training in Taiwan (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.

Data analytics is a vital asset for modern businesses. This course emphasizes the development of practical, hands-on data analysis skills. Its goal is to equip participants with the ability to provide evidence-based responses to key 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

This instructor-led live training, offered both online and onsite, targets beginner-level professionals eager 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.

R is a highly popular, open-source environment dedicated to statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering language fundamentals, essential libraries, and advanced concepts, along with practical advanced data analytics and graphing techniques using real-world datasets.

Audience

Developers and data analytics professionals

Duration

3 days

Format

Lectures combined with hands-on practice

This instructor-led, live training in Taiwan (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 Taiwan (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.

This instructor-led, live training in Taiwan (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.

Unstructured data is estimated to constitute over 90 percent of all data, with text serving as the predominant form. This volume continues to expand daily through blog posts, tweets, social media activity, and other digital publications.

This instructor-led, live course focuses on extracting meaningful insights from such data. By leveraging the R programming language alongside Natural Language Processing (NLP) libraries, we integrate principles and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically interpret the meaning embedded within text. Data samples are available in various languages, tailored to meet specific client requirements.

Upon completing this training, participants will be equipped to prepare datasets (both large and small) sourced from disparate origins, and to apply appropriate algorithms to analyze and report on their significance.

Format of the Course

• A blend of lectures and discussions, with a strong emphasis on hands-on practice and occasional tests to assess 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.

Target Audience

• managers
• developers
• scientists
• students

Course Format

online instruction and discussion OR face-to-face workshops

R is a widely adopted 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 to leverage R to build practical applications that address specific financial challenges.

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

• Grasping the fundamental concepts of the R programming language
• Choosing and applying R packages and techniques to organize, visualize, and analyze financial data sourced from various platforms (such as CSV, Excel, databases, and web APIs)
• Developing applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshooting, integrating, deploying, and optimizing R applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Course Format

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

Note

• This training is designed to provide solutions for key challenges encountered by finance professionals. If you have specific topics, tools, or techniques you wish to include or explore in greater depth, please contact us to arrange the session accordingly.

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 sectors. R has also gained popularity among statisticians, engineers, and scientists who may lack advanced programming skills but appreciate its ease of use. Its widespread adoption stems from the growing demand for data mining to achieve objectives such as optimizing pricing strategies, accelerating drug discovery, or refining financial models. R offers a rich ecosystem of packages tailored for data mining.

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