Building Web Applications in R with Shiny Training Course

This instructor-led, live training in Greece (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 Greece (available online or onsite) is tailored for business analysts seeking to automate trading using algorithmic strategies, Python, and R.

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

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

Big Data encompasses solutions designed for the storage and processing of large-scale datasets. Initially developed by Google, these Big Data frameworks have evolved and inspired numerous analogous projects, many of which are available as open-source software. R has established itself as a widely used programming language within the financial sector.

This instructor-led, live training in Greece (online or onsite) targets beginner-level data analysts seeking to use R programming for data manipulation, basic analysis, and creating insightful visualizations.

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 graphics. An increasing number of managers and data analysts within corporations and academia rely on R. It has also gained popularity among statisticians, engineers, and scientists without prior computer programming experience due to its ease of use. The language's rise in prominence is driven by the growing demand for data mining to achieve various objectives, such as setting optimal prices, accelerating drug discovery, or refining financial models. R offers a wide array of packages tailored for data mining tasks.

This instructor-led, live training in Greece (online or onsite) targets data analysts interested in programming with R within 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 is an essential instrument for modern business. Throughout this course, we will concentrate on cultivating practical, hands-on data analysis skills. Our objective is to empower participants 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 Greece (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

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

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

R is a widely utilized, open-source environment designed for statistical computing, data analytics, and graphics. This course provides students with an introduction to the R programming language, covering fundamental syntax, essential libraries, and advanced concepts. Participants will engage in advanced data analytics and visualization techniques using real-world datasets.

Target Audience

Developers and Data Analysts

Duration

3 days

Format

Instructional lectures combined with practical, hands-on exercises

This instructor-led, live training in Greece (online or onsite) targets beginner-level professionals aiming to master the fundamentals of R and learn effective data handling techniques.

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

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

This instructor-led, live training in Greece (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 graphics. It is increasingly utilized by corporate managers and data analysts across both industry and academia. R offers a broad range of packages for data mining and analytical tasks.

This instructor-led live training in Greece (online or on-site) 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 represent over 90% of all existing data, with a significant portion appearing as text. Sources such as blog posts, tweets, social media platforms, and other digital publications continuously contribute to this expanding volume of information.

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

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

Format of the Course

• A blend of lectures and discussions, emphasizing extensive hands-on practice, with occasional assessments to gauge understanding.

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 for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

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

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

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