Shiny App Web Development with R Training Course

This instructor-led, live training in Brazil (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 Brazil (online or onsite) targets business analysts who wish to automate trading 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 increments.
• Decrease costs associated with trading by leveraging algorithmic trading techniques.
• Automatically monitor stock prices and execute trades.

Big Data refers to solutions designed for storing and processing extensive data sets. Originally developed by Google, these Big Data solutions have evolved and inspired various other similar projects, many of which are available as open-source software. R has become a popular programming language within the financial industry.

This instructor-led, live training in Brazil (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.

R is an open-source, free programming language designed for statistical computing, data analysis, and data visualization. It is increasingly utilized by managers and data analysts within both corporate and academic environments. R has also gained popularity among statisticians, engineers, and scientists who may lack formal programming training but appreciate its user-friendly nature. Its widespread adoption 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 supports this versatility through a comprehensive ecosystem of packages dedicated to data mining and analysis.

This instructor-led live training in Brazil (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.

Data analytics is a vital asset in the modern business landscape. Throughout this course, we will concentrate on building practical, hands-on skills in data analysis. The goal 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 Brazil (online or onsite) is designed for beginner-level professionals who want 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:

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

R is a highly popular, open-source environment designed for statistical computing, data analytics, and graphical visualization. This course provides students with an introduction to the R programming language, covering its core fundamentals, essential libraries, and advanced concepts. Participants will engage in advanced data analytics and graphing exercises 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 Brazil (online or onsite) is tailored for beginner-level professionals seeking to master the fundamentals of R and learn how to work effectively with data.

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

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

This instructor-led, live training in Brazil (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. A growing number of managers and data analysts within both corporate environments and academia rely on R. It offers a wide variety of packages tailored for data mining.

This instructor-led, live training in Brazil (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 represent over 90% of all data, with a significant portion existing in text format. Sources such as blog posts, tweets, social media interactions, and other digital publications constantly contribute to this expanding dataset.

This instructor-led, live course focuses on extracting insights and meaning from this information. By leveraging the R language alongside 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 samples are available in various languages, tailored to meet specific customer requirements.

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.

Format of the Course

• Combination of lectures, discussions, extensive hands-on practice, and occasional assessments to evaluate 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