Advanced R Training Course

Big Data refers to solutions designed for storing and processing extensive data sets. Initially developed by Google, these Big Data solutions have since evolved and inspired numerous similar projects, many of which are now available as open-source. R is a widely used programming language in the financial sector.

R is an open-source programming language that is widely used for statistical computing, data analysis, and graphics. It has gained increasing popularity among managers and data analysts in both corporate and academic settings. Additionally, R has attracted a following among statisticians, engineers, and scientists who lack formal computer programming skills but find it user-friendly. The rise of R can be attributed to the growing importance of data mining for various objectives, such as setting ad prices, accelerating drug discovery, or refining financial models. R offers a vast array of packages that support data mining applications.

R is a highly popular, open-source environment for statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering its fundamental aspects, libraries, and advanced concepts. It also delves into advanced data analytics and graphing using real-world data.

Audience

Developers and Data Analysts

Duration

3 days

Format

Lectures and Practical Hands-on Sessions

This instructor-led, live training in Taiwan (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

This course is designed for business owners, including marketing managers, product managers, and customer base managers, along with their teams, as well as professionals specializing in customer insights.

Overview

The course explores the entire customer life cycle, starting from acquiring new customers, managing existing ones to maximize profitability, retaining valuable customers, and understanding why certain customers are leaving. We will use real (though anonymized) data from various industries such as telecommunications, insurance, media, and high technology.

Format

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

R is an open-source programming language that is widely used for statistical computing, data analysis, and graphical representation. It is increasingly favored by managers and data analysts in both corporate settings and academic institutions. R offers a vast array of packages designed for data mining, making it a versatile tool for various analytical tasks.

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 has gained increasing popularity among managers, data analysts, and researchers in both corporate and academic settings. R is also favored by statisticians, engineers, and scientists who lack extensive programming skills due to its user-friendly nature. The rise of data mining for various applications, such as setting advertising prices, accelerating drug discovery, or refining financial models, has further fueled its popularity. R offers a vast array of packages specifically tailored for data mining.

This course delves into the manipulation of objects in R, including data reading, accessing R packages, writing functions, and creating informative graphs. It also covers data analysis using common statistical models. The course provides comprehensive instruction on how to use the R software (https://www.r-project.org), both through command-line interfaces and graphical user interfaces (GUIs).

R is an open-source programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly favored by managers and data analysts in both corporate and academic settings. R has also gained traction among statisticians, engineers, and scientists who may not have extensive programming skills but find it user-friendly. The growing popularity of R can be attributed to the expanding use of data mining for diverse objectives, such as setting advertising prices, accelerating drug discovery, or refining financial models. R offers a vast array of packages specifically tailored for data mining.

R is an open-source programming language widely used for statistical computing, data analysis, and creating graphics. It is increasingly favored by managers and data analysts in both corporate and academic settings. R has also gained a following among statisticians, engineers, and scientists who lack formal programming skills but find it user-friendly. The growing popularity of R can be attributed to the increasing reliance on data mining for diverse objectives, such as setting advertising prices, accelerating drug discovery, or refining financial models. R offers a comprehensive array of packages specifically designed for data mining.

This course provides an introduction to implementing neural networks for practical applications using the R-project software.

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

The goal is to provide a practical, advanced R programming course that participants can directly apply in their professional work.

Sector-specific examples are utilized to ensure the training is relevant and applicable to the audience.

This course is an integral component of the Data Scientist skill set, specifically within the Data and Technology domain.

Audience

Course Format

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 designed for cleaning, processing, modeling, and visualizing data. Some of the key packages include ggplot2, dplyr, tidyr, readr, purrr, and tibble.

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

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

• Perform data analysis and create compelling visualizations
• Draw meaningful insights from various sample datasets
• Filter, sort, and summarize data to answer exploratory questions
• Transform processed data into informative line plots, bar plots, and histograms
• Import and filter data from a variety of sources, including Excel, CSV, and SPSS files

Description:

This course is designed to teach R users how to develop web applications without the need to learn cross-browser HTML, JavaScript, and CSS.

Objective:

The course covers the fundamentals of how Shiny apps function.

It also explores all the commonly used input, output, rendering, and paneling functions from the Shiny library.