Building Web Applications in R with Shiny 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.

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.

This instructor-led live training in Slovakia (online or onsite) is tailored for beginner-level data analysts who aim to use R programming to manipulate data, perform fundamental analyses, and create compelling visualizations for insights.

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

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

R is a free, open-source programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly utilized by managers and data analysts within both corporate environments and academic institutions. R has also attracted statisticians, engineers, and scientists who may lack formal programming training but appreciate its ease of use. The language's popularity stems from the growing reliance on data mining to achieve diverse objectives, such as optimizing pricing strategies, accelerating drug discovery, and refining financial models. R offers an extensive array of packages tailored for data mining tasks.

This instructor-led, live training in Slovakia (online or on-site) is targeted at data analysts who intend 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 plays a vital role in modern business. This course focuses on developing practical, hands-on data analysis skills. The primary goal is to equip participants with the ability to provide evidence-based answers to key questions: 

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 Slovakia (online or onsite) is designed for beginner-level professionals looking to clean and analyze data, generate statistical forecasts, and produce insightful visualizations using these tools.

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

• Grasp the fundamentals of using Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets utilizing Python and Power Query.
• Conduct statistical analysis and forecasting with R.
• Develop professional dashboards and reports using Power BI.
• Effectively integrate and analyze data from diverse sources.

R is a widely adopted, open-source platform for statistical computing, data analytics, and graphics. This course provides students with an introduction to the R programming language, covering foundational language features, essential libraries, and advanced concepts. Participants will explore advanced data analytics and visualization techniques using real-world datasets.

Target Audience

Developers and data analytics professionals

Duration

3 days

Format

Lecture-based instruction combined with hands-on exercises

This instructor-led, live training in Slovakia (online or onsite) is designed for beginners who wish to master the fundamentals of R and learn how to work with data.

Upon completing this course, participants will be able to:

• Navigate the R programming environment and understand the RStudio interface.
• Import, manipulate, and explore datasets using R commands and packages.
• Conduct basic statistical analysis and summarize data.
• Create visualizations using both base R and ggplot2.
• Efficiently manage workspaces, scripts, and packages.

This instructor-led, live training in Slovakia (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.

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.

This instructor-led live training in Slovakia (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 form. Continuous contributions from blog posts, tweets, social media platforms, and other digital publications constantly expand this vast repository of information.

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

Upon completion of this training, participants will be equipped to prepare datasets (both large and small) from diverse sources, then apply appropriate algorithms to analyze and report on their significance.

Course Format

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

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