R for Data Science Solutions


R for Data Science Solutions. Over 100 hands-on tasks to help you effectively solve real-world data problems using the most popular R packages and tec

100,00  10,00 

The name of this course is R for Data Science Solutions. The knowledge you will get with this indescribable online course is astonishing. Over 100 hands-on tasks to help you effectively solve real-world data problems using the most popular R packages and tec.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Packt Publishing, one of the very best experts in this field.

Description of this course: R for Data Science Solutions

Course Description R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. 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 are cleared with R’s excellent data visualization feature. The first section in this course 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. An example of data manipulation is provided, illustrating how to use the ‘dplyr’ and ‘data.table’ packages to efficiently process larger data structures. We also focus on ‘ggplot2’ and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later sections offer 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. By the end of this course, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. About The Author Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing.

Requirements of this course: R for Data Science Solutions

What are the requirements? This collection of independent videos offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.

What will you learn in this course: R for Data Science Solutions?

What am I going to get from this course? Get to know the functional characteristics of R language Extract, transform, and load data from heterogeneous sources- Understand how easily R can confront probability and statistics problems Get simple R instructions to quickly organize and manipulate large datasets Create professional data visualizations and interactive reports Predict user purchase behavior by adopting a classification approach Implement data mining techniques to discover items that are frequently purchased together Group similar text documents by using various clustering methods

Target audience of this course: R for Data Science Solutions

Who is the target audience? This course is for budding data scientists, analysts, and those who are familiar with the basic operations of R.