R is an open source programming language and software environment for statistical computing and graphics. R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data like optimization and analyzation are cleared with R’s excellent data visualization feature. If you're interested to work effectively with data and solve real-world data problems using the most popular R packages and techniques, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

 The highlights of this Learning Path are: 

• Learn how to analyze, interpret, and optimize data in R
• Solve data problems efficiently
• Produce high-quality statistical graphics and increase your efficiency in data analysis code

Let’s take a quick look at your learning journey. This Learning Path will take you on a journey to become an efficient data science practitioner as you will thoroughly understand the key concepts of R. Starting from the absolute basics, you will quickly be introduced to programming in R. You will learn how to load data into R for analysis, and get a good understanding of how to write R scripts. You will also get to know how to perform basic analysis of the data.

Further, this Learning Path deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. You will be provided with an example of data manipulation illustrating how to use the ‘dplyr’ and ‘data.table’ packages to efficiently process larger data structures. This Learning Path also  offers an insight into time series analysis, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path will also throw light on how functional programming and meta-programming with R can simplify and speed up your data analysis code.

By the end of this Learning Path, you will understand how to resolve issues and comfortably offer solutions to problems encountered while performing data analysis and produce high-quality statistical graphics to increase your efficiency in data analysis code.

Meet Your Expert:

We have combined the best works of the following esteemed author to ensure that your learning journey is smooth:

• Mykola Kolisnyk has been working in test automation since 2004. He has been involved with various activities including creating test automation solutions from scratch, leading test automation teams, and working as a consultant with test automation processes. During his working career, he has had experience with different test automation tools such as Mercury WinRunner, Micro Focus SilkTest, SmartBear TestComplete, Selenium-RC, WebDriver, Appium, SoapUI, BDD frameworks, and many other different engines and solutions. He has had experience with multiple programming technologies based on Java, C#, Ruby, and so on, and with different domain areas such as healthcare, mobile, telecoms, social networking, business process modeling, performance and talent management, multimedia, e-commerce, and investment banking.
• Richard Skeggs is not new to big data as he has over 15 years of experience in creating big data repositories and solutions for large multinational organizations in Europe. Having become a single father, he has changed his focus and is now working within the academic and research community. Richard has special interest in big data and is currently undertaking research within the field. His research interests revolve around machine learning, data retrieval, and complex systems.
• Yu-Wei, Chiu (David Chiu) is the founder of Largit Data Company. He has previously worked for Trend Micro as a software engineer, with the responsibility of building up big data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques to data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, a book compiled for Packt Publishing.
• Dr. David Wilkins is a microbial ecologist currently based in Sydney, Australia. The author has worked with the R technology for around five years.