Predictive Modelling with R Training Course

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

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

• Utilize algorithms to rapidly buy and sell securities at specialized increments.
• Lower costs associated with trading through the application of algorithmic methods.
• Automatically monitor stock prices and execute trades.

In this instructor-led, live training in Slovakia, participants will develop the mindset necessary to approach Big Data technologies, evaluate their impact on existing processes and policies, and deploy these technologies to identify criminal activity and prevent crime. We will examine case studies from law enforcement organizations worldwide to gain insights into their adoption strategies, challenges, and outcomes.

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

• Integrate Big Data technology with traditional data gathering processes to reconstruct narratives during investigations.
• Implement industrial-grade big data storage and processing solutions for data analysis.
• Develop a proposal for adopting the most suitable tools and processes to enable a data-driven approach to criminal investigation.

The term Big Data encompasses solutions designed for the storage and processing of massive datasets. Originally pioneered by Google, these Big Data solutions have evolved and inspired numerous similar projects, many of which are released as open-source software. R has established itself as a prominent programming language within the financial sector.

Audience

This course is designed for those seeking to interpret accessible data or analyze unstructured information found online (such as data from Twitter, LinkedIn, etc.).

The content primarily targets decision-makers and professionals responsible for determining which data should be collected and analyzed for value.

While not intended for individuals configuring technical solutions, those roles will still benefit from gaining a broader strategic perspective.

Delivery Mode

Participants will be introduced to working examples of mostly open-source technologies throughout the course.

The format includes concise lectures followed by presentations and practical exercises conducted by the delegates.

Content and Software used

All software employed is updated to the latest possible versions each time the course is conducted.

The curriculum covers the entire data lifecycle—from acquisition and formatting to processing and analysis—culminating in an explanation of how to automate decision-making processes using machine learning.

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

Upon completing this training, participants will be able to:

• Load datasets into DataRobot to analyze, assess, and perform quality checks on their data.
• Build and train models to identify key variables and achieve prediction goals.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimized prediction performance.

This instructor-led, live training in Slovakia (online or onsite) targets intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

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

• Understand the fundamentals of predictive AI and generative models.
• Utilize AI-powered tools for predictive coding, forecasting, and automation.
• Implement LLMs (Large Language Models) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendations.
• Develop and fine-tune AI models for real-world applications.
• Evaluate ethical considerations and best practices in AI deployment.

This instructor-led, live training in Slovakia (online or onsite) is designed for beginner-level IT professionals who want to master the basics of Predictive AI.

Upon completing this training, participants will be able to:

• Grasp the core concepts of Predictive AI and its practical applications.
• Gather, clean, and prepare data for predictive analysis.
• Explore and visualize data to reveal key insights.
• Develop fundamental statistical models to generate predictions.
• Assess the performance of predictive models.
• Apply Predictive AI principles 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 within both corporate environments and academic institutions. R offers a comprehensive array of packages tailored for data mining and advanced analytics.

Predictive analytics involves leveraging data analytics to forecast future outcomes. This approach combines data with data mining, statistical methods, and machine learning techniques to construct predictive models that anticipate future events.

In this instructor-led live training, participants will learn how to utilize Matlab to build predictive models and apply them to large-scale datasets to predict future occurrences based on analyzed data.

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

• Develop predictive models to analyze patterns in historical and transactional data
• Employ predictive modeling to identify risks and opportunities
• Construct mathematical models that capture significant trends
• Utilize data from devices and business systems to minimize waste, save time, or reduce costs

Audience

• Developers
• Engineers
• Domain experts

Course Format

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

This instructor-led, live training in Slovakia (online or onsite) targets intermediate-level data professionals seeking to apply machine learning techniques to data-driven business issues, including sales forecasting and predictive modeling using neural networks.

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

• Grasping the fundamental concepts and categories of machine learning.
• Implementing essential algorithms for classification, regression, clustering, and association analysis.
• Conducting exploratory data analysis and preparing data using Python.
• Applying neural networks for nonlinear modeling tasks.
• Executing predictive analytics for business forecasting, including sales data.
• Assessing and optimizing model performance through visual and statistical methods.

This instructor-led, live training in Slovakia (online or onsite) is aimed at 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:

• Implement predictive analytics models to forecast and solve challenges in the DevOps pipeline.
• Utilize AI-driven tools for enhanced monitoring and operations.
• Apply machine learning techniques to improve software delivery workflows.
• Design AI strategies for proactive issue resolution and optimization.
• Navigate the ethical considerations of using AI in DevOps.

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

In this instructor-led live training, participants will learn 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 using 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

• A combination of lectures, discussions, exercises, and extensive 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 analysts, researchers, and professionals eager to leverage SPSS for data preparation, statistical analysis, and predictive modeling.

Target Audience

Course Format

Upon completion of this training, participants will be equipped with the skills to:

This instructor-led, live training enables participants to master the manipulation and visualization of data using the comprehensive tools available within the Tidyverse ecosystem.

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

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

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

• Execute data analysis and produce compelling 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, such as Excel, CSV, and SPSS files