Predictive Modelling with R Training Course

In this instructor-led, live training in Uzbekistan, 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.

Audience

If you are trying to make sense of the data you have access to or wish to analyze unstructured data available on the internet (such as Twitter, LinkedIn, etc.), this course is designed for you.

The primary focus is on decision-makers and individuals who need to determine which data is worth collecting and analyzing.

While the course is not intended for those configuring the solution, they can still benefit from understanding the broader context.

Delivery Mode

During the course, participants will be presented with practical examples of mostly open-source technologies.

Short lectures will be followed by presentations and simple exercises for the attendees to complete.

Content and Software Used

All software used in the course is updated each time it is delivered, ensuring we use the latest versions available.

The content covers the entire process from data acquisition, formatting, processing, and analysis, explaining how to automate decision-making processes using machine learning.

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

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

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

• Grasp the significance of ethics in AI development and deployment.

• Acquire the skills to identify and address ethical risks related to AI.

• Gain a deeper understanding of regulatory and compliance requirements.

• Formulate strategies for promoting an ethical AI culture within your organisation.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab setting.

Course Customisation Options

• For a tailored training experience, please contact us to discuss your specific needs.

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

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

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

R is an open-source programming language that is freely available for statistical computing, data analysis, and graphical representation. It is increasingly being adopted by both corporate and academic professionals. R offers a vast array of packages that facilitate data mining.

Predictive analytics involves using data analysis to forecast future outcomes. This process combines data with techniques such as data mining, statistics, and machine learning to develop a model that can predict future events.

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

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

• Create predictive models to identify patterns in historical and transactional data
• Use predictive modeling to recognize potential risks and opportunities
• Develop mathematical models that capture significant trends
• Leverage data from devices and business systems to optimize processes, save time, or reduce costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

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

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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 quick application prototyping and development. It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led, live training, participants will learn how to use 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
• Mash up 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 extensive hands-on practice

Note

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

Audience

• managers
• developers
• scientists
• students

Course Format

The course will be conducted through online instruction and discussion, or in-person workshops.

R is an open-source programming language used for statistical computing, data analysis, and creating graphics. It has gained increasing popularity among managers and data analysts in both corporate and academic settings. R has also attracted users such as statisticians, engineers, and scientists who lack formal programming skills due to its user-friendly nature. The growing use of data mining for various purposes, such as setting ad prices, accelerating drug discovery, or refining financial models, has further contributed to its popularity. R offers a vast array of packages specifically designed for data mining.

This course focuses on the manipulation of objects in R, including reading data, accessing R packages, writing functions, and generating informative graphs. It also covers analyzing data using common statistical models. The course provides comprehensive instruction on how to use the R software (https://www.r-project.org) through both command-line interfaces and graphical user interfaces (GUIs).

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level analysts, researchers, and professionals who wish to use SPSS for data preparation, statistical analysis, and predictive modeling.

Audience

Format of the Course

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

In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.

The Tidyverse is a collection of versatile R packages designed for cleaning, processing, modeling, and visualizing data. Some of these packages include: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Beginners to the R language
• Beginners to data analysis and visualization

• The course combines lectures with discussions, exercises, and extensive hands-on practice.

• Perform data analysis and create compelling visualizations
• Draw meaningful insights from various datasets of sample data
• Filter, sort, and summarize data to answer exploratory questions
• Transform processed data into informative line plots, bar plots, and histograms
• Import and filter data from a variety of sources, including Excel, CSV, and SPSS files