Predictive Modelling with R Training Course

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

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

• Use algorithms to buy and sell securities rapidly at specialized increments.
• Reduce trading-related costs through the use of algorithmic trading.
• Automatically monitor stock prices and execute trades.

In this instructor-led live training in Uzbekistan, participants will develop the mindset needed to engage with 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 globally to understand their adoption strategies, challenges, and outcomes.

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

• Integrating Big Data technology with traditional data collection methods to construct comprehensive narratives during investigations.
• Deploying industrial-grade big data storage and processing solutions for data analysis.
• Drafting proposals for adopting the most suitable tools and processes to enable a data-driven approach to criminal investigations.

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with 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 analyse, assess, and perform quality checks on data.
• Build and train models to identify key variables and meet prediction targets.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to maintain optimised prediction performance.

• Understand the importance of ethics in AI development and deployment.

• Learn to identify and mitigate ethical risks associated with AI.

• Gain insights into regulatory and compliance requirements.

• Develop strategies for fostering an ethical AI culture within their organisations.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to make arrangements.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for intermediate-level developers who aim 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.
• Leverage 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 recommendation systems.
• 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, free programming language designed for statistical computing, data analysis, and data visualization. It is increasingly adopted by corporate managers and data analysts in both business and academic settings. R offers a comprehensive ecosystem of packages tailored for data mining and analysis.

Predictive analytics is the process of using data analytics to forecast future events. This approach combines data with data mining, statistics, and machine learning techniques to develop predictive models that anticipate upcoming outcomes.

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 forecast future events based on historical data.

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

• Build predictive models to identify patterns in historical and transactional data
• Apply predictive modeling to detect risks and uncover opportunities
• Construct mathematical models that capture key trends
• Leverage data from devices and business systems to minimize waste, save time, or reduce costs

Audience

• Developers
• Engineers
• Domain experts

Course Format

• A blend of lectures, discussions, 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 rapid 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 session, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and deploying 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 build predictive models
• Operationalize predictive analytics within business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Course Format

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

Note

• To request a customized training session for this course, please contact us to make arrangements.

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

Target Audience

Course Format

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

This instructor-led, live training guides participants through the process of manipulating and visualizing data using the tools provided by the Tidyverse.

The Tidyverse is a comprehensive collection of powerful R packages designed for cleaning, processing, modeling, and visualizing data. Key packages included in the Tidyverse are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

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

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

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