R for Data Science: RStudio 1.1.4 intermediate
- 7 Courses | 4h 55m 3s
- 11 Books | 58h 19m
- 1 Audiobook | 8h 9m 58s
Data science is a multi-disciplinary field used to analyze large amounts of data to detect relationships and uncover meaning. Discover how the R programming language can be used with data.
COURSES INCLUDED
R Data Structures
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.
11 videos |
51m
Assessment
Badge
Importing & Exporting Data using R
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.
8 videos |
33m
Assessment
Badge
Data Exploration using R
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.
10 videos |
40m
Assessment
Badge
R Regression Methods
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.
8 videos |
36m
Assessment
Badge
R Classification & Clustering
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.
8 videos |
38m
Assessment
Badge
R for Data Science: Data Visualization
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.
8 videos |
32m
Assessment
Badge
Cleaning Data in R
R is a programming language that is essential for data science, used for statistical computing and graphics. In this 13-video course, learners explore essential methods for wrangling and cleaning data with R. Begin by recognizing types of unclean data and criteria for ensuring data quality. First, learners see how to fetch a JSON (JavaScript Object Notation) document over HTTP and load data into a dplyr table. Learn how to load multiple sheets from an Excel document and how to handle common errors encountered when reading CSV (comma-separated values) data. Read data from a relational database with a SQL (structured query language) query. Explore joining tabular data by combining two related data sets by using a join operation, and spreading data-reshaping tabular data by spreading values from rows to columns. Look at summarizing data, applying a summary function using dplyr; imputing data, using mean imputation to replace missing values; and extracting matches, using a regular expression and data wrangling tools from the tidyverse package. The closing exercise practices data wrangling functions using R.
13 videos |
1h 2m
Assessment
Badge
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Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
The Big R-Book: From Data Science to Learning Machines and Big DataThis book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use.
13h 56m
By Philippe J. S. De Brouwer
Book
R in Action: Data Analysis and Graphics with R, Second EditionFocusing on practical solutions, this book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods.
9h 19m
By Robert Kabacoff
Book
Learn RStudio IDE: Quick, Effective, and Productive Data ScienceFor programmers who want to start doing data science, but don't know what tools to focus on, this book is a quick, no-nonsense tutorial of RStudio that will give you a head start to develop the insights you need in your data science projects.
1h 18m
By Matthew Campbell
Book
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and VisualizationIncluding case studies throughout, this book shows you how to conduct data analysis using the popular R language.
7h 32m
By Joshua F. Wiley, Matt Wiley
Book
Data Science Using Python and RWritten for the general reader with no previous analytics or programming experience, this step-by-step book will show you how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques.
3h 50m
By Chantal D. Larose, Daniel T. Larose
Book
Learn R for Applied Statistics: With Data Visualizations, Regressions, and StatisticsFor those who want to learn R programming for statistics, this resource is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations.
1h 48m
By Eric Goh Ming Hui
Book
Functional Programming in R: Advanced Statistical Programming for Data Science, Analysis and FinanceOffering an exhaustive description of how functions are defined and used, this unique and concise resource provides an introduction to functions in R and how to write functional programs in R.
1h 51m
By Thomas Mailund
Book
Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and FinanceAn introduction to metaprogramming in the R language, this book shows you how to leverage R's natural flexibility in how function calls and expressions are evaluated, to create small domain-specific languages to extend R within the R language itself.
1h 25m
By Thomas Mailund
Book
Advanced Object-Oriented Programming in R: Statistical Programming for Data Science, Analysis and FinanceIncluding a case study project as a take away for readers, this practical book provides an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner.
1h 43m
By Thomas Mailund
Book
Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data ScientistPresenting best practices for data analysis and software development in R, this comprehensive book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
6h 29m
By Thomas Mailund
Book
Practical Data Science with R, Second EditionProviding numerous tools, modeling techniques, and real-world use cases, this invaluable resource takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science.
9h 8m
By John Mount, Nina Zumel
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AUDIOBOOKS INCLUDED
Audiobook
Practical Data Science with RThis audio edition of "Practical Data Science with R" lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business.
8h 9m 58s
By Nina Zumel
SKILL BENCHMARKS INCLUDED
Acquiring and Cleaning Data in R Competency (Intermediate Level)
The Acquiring and Cleaning Data in R Competency benchmark measures whether a learner has had exposure and experience in gathering data, identifying dirty data, and cleaning the data in R. A learner who scores high on this benchmark demonstrates knowledge and experience in getting the data in various formats, understanding the data, and cleaning the data using R libraries for data analysis.
17m
| 17 questions
Exploratory Data Analysis in R Competency (Intermediate Level)
The Exploratory Data Analysis in R Competency benchmark measures whether a learner has had exposure and experience in performing exploratory data analysis in R. A learner who scores high on this benchmark demonstrates knowledge and experience in getting the data in various formats, understanding the data, and exploring and visualizing the data in R for data analysis.
23m
| 23 questions
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