Data Analysis with R

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 course is for you. It contains carefully selected data analysis concepts such as: cluster analysis, time-series analysis, handling missing data, sentiment analysis, as well as advanced data visualization with R and ggplot2. Throughout the course, you will use the various tools you've learned about to analyze real-world datasets from various industry sectors. By the end of the course, the readers will have a thorough understanding of advanced data analysis concepts and how to implement them in R.

Delegates will learn

 ·      Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively

·       Delve into data visualization and regression-based methods with R/RStudio

·       Explore multinomial logistic regression with categorical response variables at three levels

·       Build an experimental design to gather your own data and conduct analysis

·       Build systems for varied domains including market research, network analysis, social media analysis, and more

·       Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling

·       Master prediction and model assessment


İf you are looking for a course that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the course for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book

Data Visualization with R/RStudio

Data Partitioning, Multiple Linear Regression and Multicollinearity

Building recommendation systems to carry out smart analytics over complex datasets

Taming time series data – Time Series analysis using Recurrent Neural Networks

Streaming data Clustering analysis in R

Analyze and understand networks using R

Program Details
Duration 2 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom

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