R Language Training in Bern

R Markdown is an authoring framework that combines executable R code with narrative text to create dynamic and reproducible documents.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level R users who wish to generate automated, dynamic reports using R Markdown for internal documentation, research publications, or web publishing.

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

• Create R Markdown documents with embedded R code chunks and formatted text.
• Generate outputs in multiple formats including HTML, PDF, and Word.
• Use parameterized reports and conditional logic to generate dynamic content.
• Customize document appearance with themes, templates, and LaTeX for professional presentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Bern (online or onsite) is aimed at data analysts and anyone who is interested to learn how to use and integrate Tableau, Python, R, and SQL for data visualization and analysis.

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

• Perform data analysis using Python, R, and SQL.
• Create insights through data visualization with Tableau.
• Make data-driven decisions for business operations.

This instructor-led, live training in Bern (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.

Shiny is an R package that enables users to build interactive and dynamic web applications directly from R, without requiring knowledge of JavaScript or complex web frameworks.

This instructor-led, live training (online or onsite) is aimed at intermediate-level R users who wish to build and deploy custom web applications using Shiny for data visualization, user interaction, and reporting.

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

• Create interactive web applications using Shiny and R.
• Design user interfaces with reactive elements and dynamic inputs.
• Incorporate data visualizations and reporting features into Shiny apps.
• Deploy and share Shiny apps locally or online via Shiny Server or Posit Cloud.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Bern (online or onsite) is aimed at intermediate-level to advanced-level computer science professionals who wish to leverage Stata for statistical analysis and integrate it with R.

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

• Effectively use Stata for data analysis and statistical modeling.
• Compare Stata’s functionalities with SPSS and R.
• Integrate Stata with R for seamless statistical computing.
• Develop and automate workflows using Stata and R.

R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data.

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to 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 Bern (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 Bern (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

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

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.

This instructor-led, live training in Bern (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, 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.

This instructor-led, live training in Bern (online or onsite) is aimed at data analysts who wish 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.

This instructor-led, live training in Bern (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.

This instructor-led, live training in (online or onsite) is aimed at data analysts who wish to use Stata and R to analyze big data for statistical analysis.

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

• Create statistic models for predicting key interest variables and events.
• Generate descriptive visualizations, summary tables, frequencies, and more.
• Manage and structure large databases to preapare for data analysis.

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

This instructor-led, live training in Bern (online or onsite) is aimed at data scientists who wish to program in R for Tableau.

• Implement Tableau analytics with R.
• Return values to Tableau with learning algorithms in R.
• Structure and visualize R functions in Tableau.
• Make data driven decisions for business operations.

Description:

This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS.

Objective:

Covers the basics of how Shiny apps work.

Covers all commonly used input/output/rendering/paneling functions from the Shiny library.

This instructor-led, live training in Bern (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.