Advanced R Training Course

Big Data encompasses solutions designed for managing and analyzing extensive datasets. Initially developed by Google, these Big Data solutions have advanced significantly and have spurred numerous comparable initiatives, many of which are now offered as open-source options. In the financial sector, R is a widely-used programming language.

R is a widely-used, open-source environment for statistical computing, data analysis, and graphics. This course provides an introduction to the R programming language for students. It delves into the basics of the language, key libraries, and advanced topics. The curriculum also includes practical applications in advanced data analytics and graphing using real-world datasets.

Audience

Developers / Data Analysts

Duration

3 days

Format

Lectures and Hands-on Sessions

R is an open-source programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly utilized by managers and data analysts in both corporate environments and academic settings. R offers a broad range of packages specifically tailored for data mining.

R is an open-source, freely available programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly adopted by managers and data analysts in both corporate environments and academic institutions. R offers a broad range of packages specifically tailored for data mining.

This instructor-led, live training in the UAE (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.

Audience

Business owners such as marketing managers, product managers, and customer base managers, along with their teams; professionals specializing in customer insights.

Overview

This course covers the entire customer lifecycle, from attracting new customers to managing existing ones for profitability, retaining loyal customers, and understanding why some customers leave. We will use real (though anonymous) data from various sectors including telecommunications, insurance, media, and high tech.

Format

The training is instructor-led over five half-day sessions, featuring both in-class exercises and homework assignments. It can be conducted either in a classroom setting or online.

R is an open-source, freely available programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly adopted by numerous managers and data analysts within corporate settings and academic institutions. R offers a broad range of packages specifically tailored for data mining.

Audience

• Managers
• Developers
• Scientists
• Students

Course Format

Online instruction and discussion OR in-person workshops

R is an open-source programming language designed for statistical computing, data analysis, and graphics. It is increasingly utilized by managers and data analysts within corporations and academic institutions. R has also gained popularity among statisticians, engineers, and scientists who may not have extensive computer programming experience but find it user-friendly. Its widespread adoption can be attributed to the growing importance of data mining for various purposes such as setting ad prices, accelerating drug discovery, or refining financial models. R offers a broad range of packages specifically tailored for data mining.

This course focuses on handling objects in R, including importing data, utilizing R packages, creating R functions, and generating insightful graphs. It also covers the analysis of data using common statistical models. The course provides instruction on how to use the R software (https://www.r-project.org) both through command-line operations and within a graphical user interface (GUI).

R is an open-source programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts in both corporate settings and academic institutions. R appeals to statisticians, engineers, and scientists who may not have extensive computer programming experience due to its user-friendly nature. Its widespread use can be attributed to the growing importance of data mining for diverse objectives such as setting advertisement prices, accelerating drug discovery, or refining financial models. R offers a broad range of packages specifically tailored for data mining.

This course provides an overview of utilizing neural networks to solve practical problems through the use of R-project software.

This course is designed for data scientists and statisticians who already possess foundational R and C++ coding skills and wish to enhance their advanced R programming abilities.

The aim is to provide a practical, advanced R programming course for participants looking to apply these techniques in their professional roles.

Sector-specific examples are incorporated to ensure the training is pertinent to the audience.

This course is a component of the Data Scientist skill set (Domain: Data and Technology).

The Tidyverse is a suite of flexible R packages designed for data cleaning, processing, modeling, and visualization. Key components include ggplot2, dplyr, tidyr, readr, purrr, and tibble.

During this instructor-led live training session, participants will learn how to manipulate and visualize data using the tools provided by the Tidyverse.

By the end of the course, participants will be able to:

• Analyze data and generate compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Create informative line plots, bar charts, and histograms from processed data
• Import and filter data from a variety of sources such as Excel, CSV, and SPSS files

Audience

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

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Description: 

This course is tailored for R users aiming to develop web applications without delving into cross-browser HTML, JavaScript, and CSS.

Objective:

It provides an introduction to the fundamental workings of Shiny apps.

It encompasses all frequently utilized input/output/rendering/paneling functions within the Shiny library.