Shiny, R and HTML: Merging Data Science and Web Development Training Course

This instructor-led, live training in Italy (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 session Italy (online or onsite) is tailored for business analysts who wish to automate trading processes using algorithmic trading, Python, and R.

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

• Utilize algorithms to rapidly buy and sell securities at specialized intervals.
• Lower transaction costs associated with trading through the use of algorithmic strategies.
• Automatically track stock prices and execute trades accordingly.

Big Data denotes solutions designed for the storage and processing of extensive data sets. Initially pioneered by Google, these Big Data solutions have evolved and spurred the creation of other similar projects, many of which are accessible as open-source software. R has established itself as a widely adopted programming language within the financial sector.

Provided as an instructor-led live training in Italy (online or on-site), this course targets beginner-level data analysts looking to utilize R programming for data manipulation, basic analytical tasks, and the creation of insightful visualizations.

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

• Comprehend the fundamentals of R Programming.
• Apply core data science methodologies.
• Generate visual data representations.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. An increasing number of corporate managers and data analysts, both within organizations and in academia, utilize R. It is also widely adopted by statisticians, engineers, and scientists who lack traditional computer programming skills, thanks to its user-friendly nature. Its growing popularity stems from the expanding role of data mining in diverse objectives, such as optimizing pricing strategies, accelerating drug discovery, or refining financial models. R offers a comprehensive suite of packages for data mining.

This instructor-led, live training in Italy (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 places a strong emphasis on cultivating practical, hands-on skills in data analysis. The primary objective is to empower participants with the ability to provide evidence-based answers to key business inquiries: 

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 Italy (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 stands as a highly popular, open-source environment dedicated to statistical computing, data analytics, and graphics. This course provides students with an introduction to the R programming language, covering foundational language elements, essential libraries, and advanced concepts. Participants will engage in advanced data analytics and visualization techniques using real-world datasets.

Target Audience

Developers and data analytics professionals

Duration

3 days

Format

Instructional lectures combined with hands-on practice

This instructor-led, live training in Italy (online or onsite) is aimed at beginner-level professionals who wish to gain a mastery of the fundamentals of R and how to work with data.

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

• Understand the R programming environment and RStudio interface.
• Import, manipulate, and explore datasets using R commands and packages.
• Perform basic statistical analysis and data summarization.
• Generate visualizations using both base R and ggplot2.
• Manage workspaces, scripts, and packages effectively.

This instructor-led, live training in Italy (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 programming language dedicated to statistical computing, data analysis, and graphical visualization. It is increasingly adopted by managers and data analysts within both corporate environments and academic institutions. R offers a vast array of packages tailored for data mining and advanced analytics.

This instructor-led, live training in Italy (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 comprise over 90 percent of all data, with a significant portion existing in text format. This vast amount of information is continuously expanded by blog posts, tweets, social media content, and other digital publications.

This instructor-led live course focuses on extracting insights and meaning from this 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 interpret the meaning behind textual data. Data samples are provided in various languages to meet specific customer needs.

By the conclusion 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.

Course Format

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

Target Audience

Course Format

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

This instructor-led, live training enables participants to master data manipulation and visualization techniques using the tools provided by Tidyverse.

Tidyverse comprises a suite of versatile R packages designed for cleaning, processing, modeling, and visualizing data. Key packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R programming language
• Individuals new to data analysis and data visualization

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

• Conduct data analysis and generate compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Convert processed data into informative charts, such as line plots, bar plots, and histograms
• Import and filter data from diverse sources, including Excel, CSV, and SPSS files