Advanced R Training Course

Big Data is a term used to describe 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 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 popularity among managers and data analysts in both corporate and academic settings. R is also favored by statisticians, engineers, and scientists who may not have extensive computer programming skills, as they find it user-friendly. The increasing demand for data mining to achieve various objectives, such as setting ad prices, accelerating drug discovery, or refining financial models, has further boosted its popularity. R offers a broad range of packages specifically designed for data mining.

R is a widely popular, open-source environment for statistical computing, data analytics, and graphics. This course provides an introduction to the R programming language for students, covering language fundamentals, 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 Sessions

This instructor-led, live training in Slovakia (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, including marketing managers, product managers, and customer base managers, along with their teams; professionals specializing in customer insights.

Overview

The course explores the customer life cycle, from acquiring new customers to managing existing ones for profitability, retaining valuable customers, and understanding why certain customers leave. We will work with real (though anonymized) data from various industries such as telecommunications, insurance, media, and high tech.

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, freely available programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly being adopted by both corporate professionals and academic researchers. R offers a vast array of packages that facilitate data mining and other advanced analytical tasks.

Audience

• managers
• developers
• scientists
• students

Format of the Course

The course is available in two formats: online instruction and discussion, or face-to-face workshops.

R is an open-source programming language designed for statistical computing, data analysis, and creating graphics. It is increasingly being adopted by managers and data analysts in both corporate and academic settings. R has also gained popularity among statisticians, engineers, and scientists who lack extensive computer programming skills due to its user-friendly nature. The growing demand for data mining in various fields—such as setting ad prices, accelerating the discovery of new drugs, or refining financial models—has contributed significantly to R's widespread use. R offers a vast array of packages specifically tailored for data mining.

This course delves into the manipulation of objects within R, covering topics such as reading data, accessing R packages, writing R functions, and generating informative graphs. It also includes techniques for analyzing data using common statistical models. Participants will learn how to utilize 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 that is freely available and widely used for statistical computing, data analysis, and graphics. It is increasingly favored by managers and data analysts in both corporate and academic settings. R has also gained popularity among statisticians, engineers, and scientists who lack formal programming skills but find it user-friendly. The growing interest in R is largely driven by the expanding use of data mining for various purposes, such as setting advertising prices, accelerating drug discovery, or refining financial models. R offers a vast array of packages that support these data mining activities.

R is an open-source programming language designed for statistical computing, data analysis, and graphics. It is increasingly favored by managers and data analysts both in corporate settings and academia. R has also gained a following among statisticians, engineers, and scientists who lack formal computer programming skills due to its user-friendly nature. The growing popularity of R can be attributed to the increasing reliance on data mining for various objectives, such as setting advertisement prices, accelerating drug discovery, or refining financial models. R offers a vast array of packages that support these data mining activities.

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

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

The goal is to provide a practical, advanced R programming course for participants who are interested in applying these methods in their professional work.

Sector-specific examples are used to ensure the training is relevant to the audience.

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

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 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 datasets of sample data
• 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 help R users create 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 delves into all the commonly used input, output, rendering, and paneling functions from the Shiny library.