Building Web Applications in R with Shiny Training Course

This instructor-led, live training in France (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 France (online or onsite) is designed for business analysts aiming to automate trading via algorithmic methods, Python, and R.

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

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

Big Data refers to solutions designed for storing and processing large datasets. Originally developed by Google, 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 France (online or onsite) is designed for 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 France (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.

In today's business landscape, data analytics serves as a vital asset. This course prioritizes the development of practical, hands-on skills for data analysis. The primary objective is to equip participants with the ability to provide evidence-based responses 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 France (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 used, open-source environment for statistical computing, data analytics, and graphics. This course introduces participants to the R programming language, covering language fundamentals, libraries, and advanced concepts, along with practical advanced data analytics and graphing using real-world data.

Audience

Developers / Data Analysts

Duration

3 days

Format

Lectures and Hands-on Practice

This instructor-led live training in France (online or onsite) is designed for intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

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

• Grasp the fundamentals of forecasting techniques within R.
• Apply ARIMA models and exponential smoothing methods for time series analysis.
• Utilize the 'forecast' package to develop accurate forecasting models.
• Automate forecasting workflows for both business and research purposes.

This instructor-led live training in France (online or onsite) is designed for data scientists who wish to program in Python and R for KNIME.

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

• Plan, build, and deploy machine learning models within KNIME.
• Make data-driven decisions for operations.
• Implement end-to-end data science projects.

Unstructured data is estimated to comprise more than 90 percent of all data, with a significant portion existing in text format. This vast repository expands continuously through sources such as blog posts, tweets, social media platforms, and other digital publications.

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

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

Course Format

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

In this instructor-led live training, participants will master advanced Machine Learning techniques using R by building a real-world application step-by-step.

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

• Comprehend and implement unsupervised learning methods
• Utilize clustering and classification to generate predictions from real-world datasets
• Visualize data to rapidly derive insights, support decision-making, and refine analytical processes
• Enhance machine learning model performance through hyper-parameter tuning
• Deploy models into production environments for integration into broader applications
• Apply sophisticated machine learning techniques to analyze social network data, big data, and other complex queries

R for Data Analysis and Research equips analysts, scientists, and students with practical techniques for statistical computing and data visualization using R. Covers core principles of data manipulation, statistical functions, normal distribution analysis, and regression. Examines proven methods for t-tests, ANOVA, multivariable analysis, chi-square tests, and custom function scripting. Helps researchers generate publication-quality graphics and extract actionable insights from complex datasets.

R has established itself as a leading programming language within the financial sector. Its applications span from core trading platforms to sophisticated risk management systems.

Through this instructor-led live training, participants will acquire the skills necessary to leverage R for developing practical applications that address specific finance-related challenges.

Upon completing this training, participants will be capable of:

• Grasping the fundamental concepts of the R programming language
• Choosing and employing R packages and methodologies to organize, visualize, and analyze financial data sourced from diverse channels (such as CSV, Excel, databases, and web APIs)
• Constructing applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Effectively troubleshooting, integrating, deploying, and optimizing R applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Format of the course

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

Note

• While this training addresses key problems encountered by finance professionals, participants with specific topics, tools, or techniques they wish to explore in greater depth are encouraged to contact us for customization.

R is a free, open-source programming language designed for statistical computing, data analysis, and graphical visualization. It is increasingly adopted by managers and data analysts within both corporate environments and academic institutions. R has also gained popularity among statisticians, engineers, and scientists who may lack formal programming expertise but appreciate its user-friendly nature. The language's rise in prominence is driven by the growing demand for data mining techniques applied to diverse objectives, such as optimizing pricing strategies, accelerating drug discovery, and refining financial models. R offers an extensive collection of packages tailored for data mining tasks.

This course explores the manipulation of R objects, covering essential skills such as reading data, accessing R packages, creating R functions, and generating insightful graphs. Participants will learn to analyze data using standard statistical models. The curriculum includes hands-on experience with the R software (https://www.r-project.org), utilizing both the command-line interface and a graphical user interface (GUI).