Data Analysis with R

The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis. This course teaches you everything you need to know to get started with R, from installing R to running R from the command line. You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and

Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R. This course shows you how to create and generate vectors using the c() and vector() functions, respectively. You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors. Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling. On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.

Vectors are a great basic data structure in R, but they have important limitations on the dimensions and types of data they contain. Matrices, arrays, and lists are powerful R structures that mitigate these limitations. This course will help you distinguish each of these three elements' purpose and show you how to use them. You'll start by using matrices to store two-dimensional data. You'll then differentiate between row-major and column-major matrices. You'll learn how to use arrays and how you can easily create three-dimensional arrays as you can two-dimensional arrays. You'll then move on to the use of lists and how they differ from vectors. After taking this course, you'll be able to identify when and how to use a matrix, a list, and an array.

Data frames are an R abstraction for tabular data similar to that contained in spreadsheet files or database tables. Data frames can work directly with files in the CSV, JSON, and Excel format, all common formats used to store data. This course outlines the characteristics of data frames in the R programming language and demonstrates how to use them. You'll learn to create basic R data frames from multiple vectors. You'll use factors - similar to enums or enumerated types in other programming languages and great for categorical variables. You'll also learn how to perform various string manipulation operations, such as splitting and joining strings and changing case. You'll then practice the important topic of printing precisely formatted strings with placeholders for variable values. When you're done, you'll be able to use data frames, factors, and strings professionally in your R programming projects.

Final Exam: Getting Started with R Programming will test your knowledge and application of the topics presented throughout the Getting Started with R Programming track of the Skillsoft Aspire Data Analysis with R Journey.

Becoming adept at using R will form a valuable part of your statistical data analysis programming language toolkit. Achieving this involves learning how to utilize the functional programming structures of R. This course shows how to use conditional constructs, statements, looping, and functions effectively in R. You'll practice using if, else, and the ifelse functions and the switch construct. You'll work with for and while loops. Recognize the next and break statements in R. And examine the repeat loop, which does not have a condition at all and must be used with a break statement. You'll then move on to advanced looping using the vapply(), lapply(), and sapply() functions in R. By the end of this course, you'll be able to use f R for control flow and looping.

R supports several powerful features, such as first-class functions, functions on the left-hand side of an assignment, and explicit environment objects that bind variables to values. Taken together, these make R a powerful language for functional programming. This course will show you how to work effectively with functions in R. Specifically, you'll learn how to create and invoke functions in R and leverage R support for functional programming and first-class functions. You'll recognize how an R environment is a virtual binding between variable names and values. You'll create nested environments and leverage the fact that individual functions have their own local environments. You'll also create and invoke closures as well as replacement functions. By the end of the course, you'll have the confidence to work with functions in your R programming projects.

R supports not one but multiple alternative object-oriented programming paradigms. These are known as object systems and constitute a relatively underutilized but incredibly powerful feature of the R language. This course will show you how to work effectively with object systems in R. You'll begin by identifying different object systems. You'll then examine how the S3 object system allows some features of object-oriented programming, albeit in a very different form from other OOP languages. You'll move to leverage the R5 object system, also known as the system of reference classes, to create classes and instantiate objects, specify member variables and methods, and initialize values of member functions. You'll also implement inheritance using the system of reference classes. When you're done with this course, you'll be able to utilize different object systems in your R programming projects.

Final Exam: Applying and Using R Programming Structures will test your knowledge and application of the topics presented throughout the Applying and Using R Programming Structures track of the Skillsoft Aspire Data Analysis with R Journey.

Transforming and manipulating massive amounts of data is crucial for all organizations. The R programming language offers a plethora of packages to load, explore, manipulate, and transform data. R is ideal for data analysis, mutation, and cleaning, making it a choice language for statisticians and data scientists. In this course, you'll learn how to write R script files using the RStudio environment. You'll use different panes to debug and evaluate your R program, import data in various file formats, and access files embedded in an R package and stored on your machine. Additionally, you'll learn how to export data to different file formats. Once you've completed this course, you'll know how to work R using RStudio, import and export data in R, and perform simple data transformation and exploration operations.

Organizations store data in various ways. The R programming language offers a straightforward interface to work with data in relational databases and transform it to the format you need for analysis. In this course, you'll learn how to connect to relational databases using the APIs provided in the Database Interface package (DBI) in R. You'll connect to SQLite data and perform create, read, update, and delete (CRUD) operations on your database tables. You'll also use R functions to mutate and transform data. You'll practice renaming columns, changing variable types, and creating new columns from derived data. You'll examine the tidyverse universe of data science packages and work with data in the wide and long formats. Once you've completed this course, you'll have a strong foundation in basic data manipulation and transformation using the R programming language.

Data analysis often requires performing a series of complex transformations. R makes this hassle-free via the forward pipe operator for chaining operations, data selection and filtering based on conditional operations, and grouping and aggregating options to compute summaries. Learn how to carry out all these operations in this course. Task you'll carry out include using logical and relational operators to perform conditional filtering, sampling records at random, and computing the top N records based on values in a variable. You'll also learn to use the forward pipe operator in the magrittr package and tibbles, the next-generation data frame, to store and transform your data. You'll round this course off by performing ordering, grouping, and aggregations on your data. When you're finished, you'll have a solid grasp of complex operations on data frames and be able to apply these concepts using the R programming language.

Data for the same entity is often stored in multiple locations. Your analysis may require bringing this data together in a single location. Doing this forms a core part of data preprocessing. Another core task is recognizing the relationships in your data. In this course, you'll practice methods to merge data to prepare for statistical and predictive modeling and identify relationships in your data using charts and graphs. You'll combine data in different data frames (or tibbles) based on the values in common columns. You'll use the merge() function to perform join operations and implement joins using functions from the tidyverse. You'll also examine the plotting systems available in R and use the plot() functionality and the ggplot2 package to visualize and explore your data. Upon completion of this course, you'll be able to combine your data in a meaningful way and uncover data relationships.

Final Exam: Working with Datasets in R will test your knowledge and application of the topics presented throughout the Working with Datasets in R track of the Skillsoft Aspire Data Analysis with R Journey.

Interpreting data is a core pre-processing step in data analysis and modeling. Use this course to practice using various dynamic statistical tools to explore and understand your data. During this course, you'll explore population distributions to model random variables, work with discrete and continuous probability distributions, and use discrete probability distribution types, such as the uniform, binomial, and Poisson distributions. You'll also examine continuous distributions, such as the normal and the exponential distributions. You'll round the course off by learning how to read and interpret QQ plots, which can be used to compare the distributions of two samples of data. When you're finished, you'll be able to use probability distributions to model events and understand your data.

Statistical analysis involves making educated guesses known as hypotheses and testing them to see if they hold up. Use this course to learn how to apply hypothesis testing to your data. Examine the use of descriptive statistics to summarize data and inferential statistics to draw conclusions. Learn how population parameters differ from summary statistics and how confidence intervals are used. Discover how to perform hypothesis testing on sample data, construct null and alternative hypotheses, and interpret the results of your statistical tests. Investigate the significance of the p-value of a statistical test and how it can be interpreted using the significance threshold or alpha level. Additionally, examine the most commonly used statistical tests, the T-test and the analysis of variance (ANOVA). When you're done, you'll have the confidence to set up the null and alternative hypotheses for your tests and interpret the results.

Hypothesis testing determines whether the educated guesses you've made about your data should be accepted or rejected. T-tests and ANOVA tests are some of the most commonly used methods in hypothesis testing. Knowing how to perform and interpret these tests are core data scientist skills. In this course, get hands-on running statistical tests on your sample data. Test assumptions made by statistical tests, run T-tests, perform ANOVA analysis, and interpret the results. Perform the one-sample t-test and the one-sample Z-test. Run the two-sample t-test to compare data from different groups or categories and the paired samples t-test to compare data across time. When you're finished with this course, you'll have the know-how to run and interpret statistical tests using the R programming language.

Regression models are used to predict continuous values and are some of the most commonly used machine learning models. Use this course to grasp what exactly machine learning (ML) algorithms are and how you can use ML models to predict outcomes based on input data. Learn how regression models work, train them, and evaluate regression results using metrics such as R2 and RMSE. Perform regression analysis in R using the ordinary least squares regression. Build models using simple and multiple regression and train a regression model using cross-validation. Upon completing this course, you'll be able to perform regression to predict continuous values and evaluate these models using metrics such as the R2 and adjusted R2.

Classification models are used to classify or categorize data points into two or more categories. Learn how these models work and how you can evaluate your classification models using the confusion matrix and metrics such as accuracy, precision, and recall. During this course, you'll perform classification using both logistic regression and an imbalanced dataset. You'll also examine why precision or recall scores may be better metrics than accuracy to evaluate such models. Furthermore, build a classification model using decision trees, visualize the tree structure, and explore the variable importance assigned by this tree structure to understand and interpret the model. When you've finished this course, you'll be able to confidently use logistic regression and decision trees to build classification models and evaluate your models using accuracy, precision, and recall.

Clustering is an unsupervised learning algorithm that self-discovers patterns in data and helps identify logical groupings. Use this course to distinguish between supervised and unsupervised learning and recognize how regression and classification algorithms differ from clustering. Examine the basic principles of clustering models and how k-means clustering finds logical groupings in your data. Learn the evaluation techniques used in clustering and find the optimal number of clusters in your data using both the elbow method and the Silhouette score. Perform clustering on a dataset with multiple attributes and visualize clusters in your data using principal components. When you've completed this course, you'll be able to find groupings in your data using k-means clustering and compute the optimal number of clusters for your data.

Understanding the bias-variance trade-off allows data scientists to build generalizable models that perform well on test data. Machine learning models are considered a good fit if they can extract general patterns or dominant trends in the training data and use these to make predictions on unseen instances. Use this course to discover what it means for your model to be a good fit for the training data. Identify underfit and overfit models and what the bias-variance trade-off represents in machine learning. Mitigate overfitting on training data using regularized regression models, train and evaluate models built using ridge regression, lasso regression, and ElasticNet regression, and implement ensemble learning using the random forest model. When you're done with this course, you'll have the skills and knowledge to train models that learn general patterns using regularized models and ensemble learning.

Final Exam: Statistical Analysis and Modeling in R will test your knowledge and application of the topics presented throughout the Statistical Analysis and Modeling in R track of the Skillsoft Aspire Data Analysis with R Journey.