This is the code repository for Mastering Data Analysis with R [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
With its popularity as a statistical programming language rapidly increasing with each passing day, R is increasingly becoming the preferred tool of choice for data analysts and data scientists who want to make sense of large amounts of data as quickly as possible. R has a rich set of libraries that can be used for basic as well as advanced data analysis tasks. If you have a basic understanding of data analysis concepts and want to take your skills to the next level, this video is for you. Spanning over four hours, it contains carefully selected advanced data analysis concepts such as: cluster analysis; time-series analysis; Association mining; PCA (Principal Component Analysis); handling missing data; sentiment analysis; spatial data analysis with R and QGIS; advanced data visualization with R and ggplot2.
Throughout the video, readers will use the various topics they've learned about to analyze real-world datasets from various industry sectors. By the end of the tutorial, readers will have a thorough understanding of advanced data analysis concepts and how to implement them in R.
The code bundle for this video course is available at - https://github.com/PacktPublishing/-Mastering-Data-Analysis-with-R-v-
- Start forecasting - Create algorithms to build predictive models
- Structure your data and most importantly - Wrangle, visualise and explore data
- Learn from your data - Gain insights and information from data
- Play with your data - Manipulate and clean it
- Let your data do the talking - Visualize data using ggplot
- Deploy the most popular machine algorithms like Random forest, and Decision trees to build powerful models
To fully benefit from the coverage included in this course, you will need:
If you are a data scientist or a data analyst and want to perform advanced data analysis tasks using the popular and open source R language, this tutorial will be perfect for you. A basic understanding of core data analysis concepts will be useful.
This course has the following software requirements:
Software Requirements :
•An Intel-compatible platform running Windows 2000, XP/2003/Vista/7/8/2012 Server/8.1/10. •At least 32 MB of RAM, a mouse, and enough disk space for recovered files, image files, etc. •The administrative privileges are required to install and run R-Studio utilities under Windows 2000/XP/2003/Vista/7/8/2012 Server/8.1/10. •A network connection for data recovering over network.
Hardware Requirements :
RAM: Minimum- 6GB-Win, 8GB-Mac; Recommended- 8GB Storage: Minimum- 7200RPM STATA with 20GB of available space, Recommended-SSD with 40GB of available space Processor: Minimum-Intel Core i3 2.5G hz, Recommended-Intel Core i5