Predictive Modelling with R 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.

In this instructor-led live training in Taiwan, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

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.

Audience

If you seek to derive meaning from the data you have access to, or wish to analyze unstructured data found on the web (such as Twitter, LinkedIn, etc.), this course is designed for you.

It is primarily aimed at decision-makers and individuals who must determine which data is valuable to collect and what is worthwhile to analyze.

While not targeted at those configuring the solution, those involved in setup will still benefit from gaining a holistic perspective.

Delivery Mode

During the course, delegates will be presented with working examples of predominantly open-source technologies.

Short lectures will be followed by presentations and simple exercises conducted by the participants.

Content and Software used

All software utilized is updated with each iteration of the course, ensuring we employ the most recent versions available.

The curriculum covers the entire process from obtaining, formatting, processing, and analyzing data, to explaining how to automate the decision-making process through machine learning.

This instructor-led live training in Taiwan (online or on-site) is designed for data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Import datasets into DataRobot to analyze, evaluate, and perform quality checks.
• Develop and train models to identify key variables and achieve prediction objectives.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led live training in Taiwan (online or on-site) is tailored for intermediate developers aiming to build AI-driven applications using predictive analytics and generative models.

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

• Grasp the core concepts of predictive AI and generative models.
• Utilize AI-enhanced tools for predictive coding, forecasting, and automation.
• Deploy Large Language Models (LLMs) and transformers for text and code generation.
• Implement time-series forecasting and AI-driven recommendation systems.
• Develop and fine-tune AI models for practical, real-world use cases.
• Assess ethical implications and adhere to best practices for AI deployment.

This instructor-led, live training in Taiwan (online or onsite) is designed for beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

Upon completing this training, participants will be able to:

• Grasp the core concepts of Predictive AI and its practical applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualize data to uncover valuable insights.
• Build basic statistical models to make informed predictions.
• Evaluate the performance of predictive models.
• Apply Predictive AI concepts to real-world scenarios.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphical visualization. It is increasingly utilized by managers and data analysts across both corporate and academic sectors. R boasts a rich ecosystem of packages dedicated to data mining.

Predictive analytics involves leveraging data analytics to forecast future outcomes. This approach utilizes data, combined with data mining, statistical analysis, and machine learning techniques, to construct predictive models for anticipating future events.

In this instructor-led live training, participants will learn how to use Matlab to build predictive models and apply them to large sample datasets to predict future events based on the data.

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

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

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

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Taiwan (online or onsite) is tailored for intermediate-level DevOps professionals who wish to integrate predictive AI into their DevOps practices.

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

• Deploy predictive analytics models to anticipate and resolve challenges within the DevOps pipeline.
• Employ AI-driven tools to enhance monitoring and operational efficiency.
• Apply machine learning techniques to optimize software delivery workflows.
• Design AI strategies aimed at proactive issue resolution and process optimization.
• Address and navigate the ethical considerations associated with using AI in DevOps environments.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It provides an integrated environment encompassing data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

This instructor-led live training (available online or onsite) is designed for beginner to intermediate-level analysts, researchers, and professionals who aim to leverage SPSS for data preparation, statistical analysis, and predictive modeling.

Audience

Course Format

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

In this instructor-led live session, attendees will learn how to manipulate and visualize data using the tools within the Tidyverse.

The Tidyverse is a suite of versatile R packages designed for cleaning, processing, modeling, and visualizing data. Key packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R language
• Beginners in data analysis and data visualization

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

• Conduct data analysis and produce engaging visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Transform processed data into informative line plots, bar plots, and histograms
• Import and filter data from diverse sources, including Excel, CSV, and SPSS files