R for Data Science: Classification & Clustering

R for Data Science: Classification & Clustering

• Course Overview
• perform the preparatory steps needed to create a classification and decision tree
• use the rpart library to build a decision tree
• use the ctree library to build a decision tree
• perform the preparatory steps needed to carry out clustering
• use the k-means clustering method
• use hierarchical clustering with the hclust and cutree methods
• apply a decision tree method to a classification problem

Explore the advantages of the programming language R in this 8-video Skillsoft Aspire course. An essential skill for statistical computing and graphics, R is the tool of choice for data science professionals in every industry and field. It both creates reproducible high-quality analyses, and offers unparalleled graphic and charting capabilities. Learners will examine how to apply classification and clustering methods to data science problems by using R. Key concepts covered in this course include performing the preparatory steps needed to create a classification and decision tree; using the rpart library and ctree library to build a decision tree; and how to perform the preparatory steps needed to carry out clustering. Next, explore use of the k-means clustering method; using hierarchical clustering with the hclust and cutree methods; and applying a decision tree method to a classification problem. Finally, learn to train a decision tree classifier by using the data and a relationship inside of those data.

R for Data Science: Data Exploration

R for Data Science: Data Exploration

• Course Overview
• use the dplyr library to load data frames
• select subsets of data using dplyr
• filter tabular data using dplyr
• perform multiple operations using the pipe operator
• create new columns using the mutate method
• summarize data using summary functions
• use the dplyr join functions to combine data
• use the group_by method from the dplyr library
• query data using various dplyr library functions

The tool of choice for data science professionals in every modern industry and field, the programming language R has become an essential skill for statistical computing and graphics. It both creates reproducible high-quality analyses and takes advantage of superior graphic and charting capabilities. In this 10-video Skillsoft Aspire course, you will explore data in R by using the dplyr library, including working with tabular data, piping data, mutating data, summarizing data, combining datasets, and grouping data. Key concepts covered in this course include using the dplyr library to load data frames; selecting subsets of data by using dplyr; and how to filter tabular data using dplyr. You will also learn to perform multiple operations by using the pipe operator; how to create new columns with the mutate method; and how to summarize data using summary functions. Next, use the dplyr join functions to combine data. Then learn how to use the group by method from the dplyr library, and how to query data with various dplyr library functions.

R for Data Science: Data Structures

R for Data Science: Data Structures

• Course Overview
• create vectors in R
• manipulate R vectors
• sort R vectors
• use lists in R
• create matrices in R
• perform matrix operations in R
• create factors in R
• create data frames in R
• perform data frame operations in R
• create and use a data frame

R is a programming language that is an essential skill for statistical computing and graphics. It is the tool of choice for data science professionals in every industry and field—not only to create reproducible high-quality analyses, but to take advantage of R's great graphic and charting capabilities. In this 11-video Skillsoft Aspire course, you will explore the fundamental data structures used in R, including working with vectors, lists, matrices, factors, and data frames. The key concepts in this course include: creating vectors in R and manipulating and performing operations on vectors in R; how to sort vectors in R; and how to use lists in R and explore example code line by line executing each line using the run current line command along the way. You will also examine creating matrices and performing matrix operations in R; creating factors and data frames in R; performing data frame operations in R; and how to create and use a data frame.

R for Data Science: Data Visualization

R for Data Science: Data Visualization

• Course Overview
• create a scatter plot
• create a line graph
• create a bar chart
• create a box and whisker plot
• create a histogram
• create a bubble plot
• use an appropriate plot to visualize data

Continue exploring the advantageous aspects of the programming language R in this 8-video Skillsoft Aspire course. An essential skill for statistical computing and graphics, R has become the tool of choice for data science professionals in every industry and field. Learn how to create reproducible high-quality analyses, while taking advantage of R's great graphic and charting capabilities. Learners will explore how to use R to create plots and charts of data. Key concepts covered in this course include creating a scatter plot by using the built-in R method; creating a line graph on a time series data set; and creating a bar chart with the built-in R function bar plot. You will learn how to create a box and whisker plot by using the built in mtcars data set; to create a histogram with the built-in R function hist, and the equivalent by using the ggplot2 library functions; and how to create a bubble plot with the ggplot2 library. Finally, learn how to use an appropriate plot to visualize data.

R for Data Science: Importing and Exporting Data

R for Data Science: Importing and Exporting Data

• Course Overview
• read data from a CSV formatted text file
• read data from an Excel spreadsheet
• read tabular data from a HTML file
• export tabular data from R to a CSV file
• export tabular data from R to an Excel spreadsheet
• export tabular data from R to an HTML table
• read data from an HTML table and export to CSV

An essential skill for statistical computing and graphics. The programming language R the tool of choice for data science professionals in every industry and field—both to take advantage of R's great graphic and charting capabilities and to create reproducible high-quality analyses. In this 8-video Skillsoft Aspire course, you will discover how to use R to import and export tabular data in CSV (comma-separated values), Excel, and HTML format. The key concepts covered in this course include how to read data from a CSV formatted text file and from an Excel spreadsheet; how to read tabular data from an HTML file; and how to export tabular data from R to a CSV file and to an Excel spreadsheet. In addition, learners will explore exporting tabular data from R to an HTML table; how to read data from an HTML table and export to CSV; and how to confirm that the contents of the CSV file were written correctly.

R for Data Science: Regression Methods

R for Data Science: Regression Methods

• Course Overview
• perform the preparatory steps needed to create a linear model
• create a linear regression model using the lm method in R
• extract the results of a linear regression
• test the predict method on a linear model
• perform the preparatory steps needed to create a logistic model
• apply the glm method on a logistic regression problem
• create a linear regression model and use the predict method

The programming language has become an essential skill for statistical computing and graphics, the tool of choice for data science professionals in every industry and field. R creates reproducible high-quality analyses, and allows users to take advantage of its great graphic and charting capabilities. In this 8-video Skillsoft Aspire course, you will discover how to apply regression methods to data science problems by using R. Key concepts covered in this course include preparing a data set before creating a linear regression model how to create a linear regression model with the lm method in R; and extracting statistical results of a linear regression problem. You will also learn how to test the predict method on perform the preparatory steps needed to create a logistic model; and how to apply the generalized linear model (glm) method on a logistic regression problem. Finally, learners see how to create a linear regression model and use the predict method on a linear model.