Introductory R (Basic to Intermediate) Training Course

This instructor-led, live training in South Korea (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 South Korea (online or onsite) is designed for business analysts aiming to automate 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 specialized increments.
• Decrease trading costs through the use of algorithmic trading.
• Automatically monitor stock prices and execute trades.

Big Data refers to technologies designed for storing and processing large-scale datasets. Originally developed by Google, these Big Data solutions have evolved and inspired numerous similar open-source projects. R is a widely used programming language in the financial sector.

This instructor-led, live training in South Korea (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 has become an essential asset for modern businesses. This course emphasizes the development of practical, hands-on data analysis skills. The primary objective is to equip participants with the ability to provide evidence-based responses to key business inquiries:

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 in South Korea (online or onsite) targets beginner-level professionals seeking to clean and analyze data, perform 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 widely popular, open-source environment designed for statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering its fundamental concepts, essential libraries, and advanced topics. Participants will engage in advanced data analytics and visualization techniques using real-world datasets.

Audience

Developers and data analysts

Duration

3 days

Format

Lectures combined with hands-on practice

This instructor-led, live training in South Korea (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 South Korea (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 South Korea (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% of all data, with a significant portion appearing in textual formats. This volume continues to expand rapidly through blog posts, tweets, social media interactions, and other digital content.

This instructor-led, live course focuses on extracting meaningful insights from this data. By leveraging the R language alongside Natural Language Processing (NLP) libraries, we integrate principles and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically decipher the meaning within text. Data samples can be provided in various languages depending on customer needs.

Upon completion of this training, participants will be capable of preparing datasets of varying sizes from diverse sources and applying appropriate algorithms to analyze and report on their significance.

Course Format

• A blend of lectures, discussions, and intensive hands-on practice, supplemented by occasional assessments to evaluate 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.

Audience

• managers
• developers
• scientists
• students

Format of the course

on-line 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 platforms to risk management systems.

In this instructor-led live training, participants will learn how to leverage R to develop practical solutions for various finance-specific challenges.

Upon completing this training, participants will be able to:

• Master the fundamentals of the R programming language
• Select and apply R packages and techniques to organize, visualize, and analyze financial data from diverse sources (CSV, Excel, databases, web APIs, etc.)
• Construct applications that address issues such as asset allocation, risk analysis, investment performance evaluation, and more
• Troubleshoot, integrate, deploy, and optimize R applications

Audience

• Developers
• 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 faced by finance professionals. If there is a specific topic, tool, or technique you wish to include or explore in greater depth, please contact us to make arrangements.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. Its user base is expanding among managers and data analysts in both corporate and academic settings. Additionally, R has gained popularity among statisticians, engineers, and scientists who lack computer programming expertise due to its user-friendly nature. This growing adoption is driven by the increasing reliance on data mining for objectives such as setting optimal prices, accelerating drug discovery, and refining financial models. R boasts a comprehensive suite of packages tailored for data mining tasks.

This course focuses on manipulating objects in R, including data ingestion, package management, function creation, and generating insightful visualizations. It also covers data analysis using standard statistical models. Participants will learn to utilize the R software (https://www.r-project.org) through both command-line interfaces and graphical user interfaces (GUIs).