Data Analysis with R E-Learning

Master data analysis and statistical modeling using the power of R.

R is a globally recognized programming language used for statistical computing, data analysis, and data mining. In this hands-on course, you'll learn the fundamentals of R, how to work with datasets, and apply statistical concepts to perform insightful analysis and modeling.

What you’ll learn:

• R language fundamentals: syntax, variables, functions, and structures
• Data exploration and cleaning using real-world datasets in R
• Apply statistical techniques like mean, variance, and correlation
• Build models using regression and clustering
• Use R for reporting and visualization

This course is part of an Agile Learning Kit, providing step-by-step learning with labs, mentoring, and 365-day access.

Why Choose This Training?

• Gain practical skills in statistical analysis with R
• Learn by doing with applied data problems
• Includes e-learning, practice labs, mentor support & assessments
• Agile structure ensures progressive and manageable learning
• Full access to all resources for 365 days

Who Should Enroll?

This course is perfect for:

• Data analysts and researchers starting with R
• Students in statistics, data science, or economics
• Professionals looking to analyze data with open-source tools
• Academics and scientists needing structured analysis workflows

This Learning Kit with more than 26 hours of learning is divided into three tracks:

Course content

Module 1: Getting Started with R Programming

In this module, the focus will be on R programming for beginners. Explore the basics of R.
Courses (6 hours +):

R Programming for Beginners: Getting Started

Course: 1 Hour, 31 Minutes

• Course Overview
• Installing R on macOS
• Installing R on Windows
• Using the ? Operator in R
• Using help() and Creating Variables in R
• Using Reserved Words and Assignment Operators in R
• Using Vectors in R
• Performing Arithmetic Operations in R
• Creating Variables in R
• Using the Built-in Functions of R
• Using the Numeric Built-in Functions of R
• Recognizing the Basic Data Types in R
• Course Summary

R Programming for Beginners: Exploring R Vectors

Course: 1 Hour, 28 Minutes

• Course Overview
• Creating Basic R Vectors
• Understanding the Finer Points of R Vectors
• Indexing into R Vectors
• Performing Vectorized Operations in R
• Implementing Relational Operations on R Vectors
• Creating R Vectors with Key-Value Pairs
• Recycling R Vectors in Vectorized Operations
• Filtering Data in R Vectors
• Using any(), all(), & which() Functions on R Vectors
• Course Summary

R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists

Course: 1 Hour, 36 Minutes

• Course Overview
• Creating Matrices in R
• Naming Dimensions in R Matrices
• Performing Math Operations on R Matrices
• Implementing Matrix Multiplication in R
• Combining Matrices in R
• Performing Indexing Operations on R Matrices
• Creating Arrays in R
• Indexing into R Arrays
• Using Lists in R
• Specifying Key-Value Pairs in R Lists
• Editing Keys and Values in R Lists
• Exploring R Lists with Different Data Types
• Course Summary

R Programming for Beginners: Understanding Data Frames, Factors, & Strings

Course: 1 Hour, 53 Minutes

• Course Overview
• Creating R Data Frames
• Naming R Data Frame Dimensions & Viewing Statistics
• Indexing into R Data Frames
• Filtering Data in R Data Frames
• Combining R Data Frames
• Joining R Data Frames
• Using Factors in R to Limit Variable Values
• Creating R Data Frames with Factors
• Using Factors with tapply() and split() in R
• Viewing Counts Using Tables in R
• Working with Strings in R
• Using formatC() & sprintf() in R
• Course Summary

Assessment:

Getting Started with R Programming

Module 2: Applying and Using R Programming Structures

In this module, the focus will be on R programming structures. Explore control flow, functions, and object systems.
Courses (4 hours +)

Using R Programming Structures: Leveraging R with Control Flow & Looping

Course: 1 Hour, 13 Minutes

• Course Overview
• Conditional Branching with If Statements in R
• Using ifelse() and the Switch Statement in R
• Iterating over Data with For Loops in R
• Iterating over R Lists and Matrices with For Loops
• Using Nested For Loops in R
• Using While Loops in R
• Using Repeat Loops in R
• Performing Advanced Looping in R
• Course Summary

Using R Programming Structures: Functions & Environments

Course: 1 Hour, 41 Minutes

• Course Overview
• Creating Custom Functions in R
• Returning Data from Functions in R
• Using Named Arguments in R
• Using Default Arguments in R
• Working with First-class Functions in R
• Storing Functions & Using Them in Switch Statements
• Working with R Environments
• Creating Inner Functions in R
• Recognizing R Functions and Environments
• Working with Closures in R
• Working with Replacement Functions in R
• Course Summary

Using R Programming Structures: Object Systems

Course: 59 Minutes

• Course Overview
• Recognizing the print() Function & S3 Object System
• Identifying R Function Invocations in S
• Creating Custom Classes Using R Functions
• Extending the print() Function for R Custom Classes
• Using Reference Classes in R
• Using Member Variables and Functions in R
• Using Inheritance in Reference Classes in R
• Course Summary

Assessment:

Applying and Using R Programming Structures

Module 3: Working with Datasets In R

In this module, the focus will be on R datasets. Explore how to load, save, and transform data as well as select, filter, join, and visualize data.
Courses (6 hours +)

Datasets in R: Loading & Saving Data

Course: 1 Hour, 44 Minutes

• Course Overview
• Installing R on macOS
• Installing RStudio on macOS
• Installing R on Windows
• Installing RStudio on Windows
• Running Commands Using the RStudio Console
• Working with Panes in RStudio
• Creating a New Project and Examining Datasets
• Demonstrating and Visualizing Built-in Datasets
• Browsing Package Vignettes
• Reading from CSV Files
• Reading from Text, XML, Excel, and JSON Files
• Writing Data Out to Different File Formats
• Course Summary

Datasets in R: Transforming Data

Course: 1 Hour, 59 Minutes

• Course Overview
• Working with an In-memory SQLite Table
• Connecting to and Retrieving Results from SQLite
• Updating Results with a Persistent Database
• Dropping and Renaming Columns
• Changing Column Data Types
• Transforming Data Using the Transform Function
• Transforming Data Using the Apply Function Family
• Transforming Data Using if_else() and mutate()
• Wide Form and Long Form: Using stack() and unstack()
• Wide Form and Long Form: Using melt() and dcast()
• melt() and dcast() on a Real Dataset
• Wide Form and Long Form: Using gather() and spread()
• Course Summary

Datasets in R: Selecting, Filtering, Ordering, & Grouping Data

Course: 1 Hour, 35 Minutes

• Course Overview
• Formatting Columns to Have the Right Data Type
• Selecting Specific Rows and Columns
• Filtering Operations on Data Frame Rows
• Selecting and Filtering Using Packages in tidyverse
• Using the dplyr filter() Function
• Retrieving Samples and Top N Results
• Specifying the Correct Data Types for Columns
• Sorting Using Order and Arrange
• Grouping and Aggregations on Data Frames
• Grouping and Aggregation Using dplyr
• Course Summary

Datasets in R: Joining & Visualizing Data

Course: 47 Minutes

• Course Overview
• Joining Data Frames Using merge()
• Joining Tibbles Using Joins and Filtering Joins
• Creating Histograms and Density Curves
• Using Plots and Charts to Visualize Data
• Course Summary

Assessment:

Working with Datasets in R

Module 4: Statistical Analysis and Modeling In R

In this module, the focus will be on statistical analysis and modeling in R. Explore probability distributions, statistical tests, regression analysis, clustering, and regularized models.
Courses (9 hours +)

Statistical Analysis and Modeling in R: Working with Probability Distributions

Course: 1 Hour, 38 Minutes

• Course Overview
• Statistical Tools for Understanding Data
• Population and Sample Metric Comparisons
• Characteristics of Probability Distribution Types
• Sampling and Analyzing Uniform Distribution Data
• Sampling and Analyzing Binomial Distribution Data
• Computing Probabilities in Binomial Distributions
• Sampling and Analyzing Poisson Distribution Data
• Examining Normal and Exponential Distributions
• Interpreting QQ Plots Using R
• Using QQ Plots in R to Compare Datasets
• Course Summary

Statistical Analysis and Modeling in R: Understanding & Interpreting Statistical Tests

Course: 1 Hour, 4 Minutes

• Course Overview
• Statistical Tools for Understanding Data
• Population and Sample Metric Comparisons
• Characteristics of Probability Distribution Types
• Sampling and Analyzing Uniform Distribution Data
• Sampling and Analyzing Binomial Distribution Data
• Computing Probabilities in Binomial Distributions
• Sampling and Analyzing Poisson Distribution Data
• Examining Normal and Exponential Distributions
• Interpreting QQ Plots Using R
• Using QQ Plots in R to Compare Datasets
• Course Summary

Statistical Analysis and Modeling in R: Statistical Analysis on Your Data

Course: 2 Hours, 7 Minutes

• Course Overview
• Identifying One-sample T-test Assumptions
• Performing the One-sample T-test in R
• Performing Variations of the One-sample T-test in R
• Performing the One-sample Z-test in R
• Identifying Assumptions of the Two-sample T-test
• Running Two-sample T-tests for Equal Variances in R
• Using Welch's two-sample T-test for Unequal Variance
• Using R to Perform the Paired Samples T-test
• Checking Paired Samples T-test Assumptions Using R
• Performing the Wilcoxon Signed-rank Test Using R
• Identifying Assumptions of the ANOVA Test Using R
• Running the One-way ANOVA and Tukey HSD Tests in R
• Running the Two-way ANOVA Test for Different Models
• Parametric vs. Non-parametric Tests
• Course Summary

Statistical Analysis and Modeling in R: Performing Regression Analysis

Course: 1 Hour

• Course Overview
• The Basic Characteristics of Machine Learning Models
• Building and Evaluating Regression Models Using R
• Visualizing Data Relationships Using R
• Performing Simple Linear Regression in R
• Performing Multiple Regression in R
• Deriving Predictions Using Regression Models in R
• Building Regression Models Using Cross-validation
• Course Summary

Statistical Analysis and Modeling in R: Performing Classification

Course: 1 Hour, 37 Minutes

• Course Overview
• Recognizing and Evaluating Classification Models
• Interpreting Logistic Regression Using R
• Training and Evaluating a Logistic Regression Model
• Building a Logistic Model in R Using all Predictors
• Using R to Train a Model with Imbalanced Data
• Building and Evaluating Models with R
• Using R to Evaluate Imbalanced Data Model Types
• Using Resampling Techniques on Imbalanced Data in R
• Recognizing Decision Tree Models
• Using R to Explore and Process Data
• Visualizing Decision Trees and Performing Prediction
• Course Summary

Statistical Analysis and Modeling in R: Performing Clustering

Course: 50 Minutes

• Course Overview
• Recognizing and Evaluating Clustering Models
• Investigating and Visualizing Clustering Data in R
• Performing K-means Clustering, Interpreting Results
• Using R to Find the Optimal Number of Clusters
• Using K-means Clustering on Multi-attribute Data
• Course Summary

Statistical Analysis and Modeling in R: Building Regularized Models & Ensemble Models

Course: 1 Hour, 32 Minutes

• Course Overview
• Overfitting and Underfitting Machine Learning Models
• The Bias-Variance Trade-off
• Exploring and Understanding Data for Regression
• Performing Ordinary Least Squares (OLS) Regression
• Preparing Data for Regularized Regression Models
• Performing Ridge Regression in R
• Performing Lasso Regression in R
• Performing ElasticNet Regression in R
• Recognizing Ensemble Learning
• Using R to Explore and Visualize Data
• Performing Regression Using Decision Trees in R
• Performing Regression Using Random Forest in R
• Course Summary

Assessment:

Statistical Analysis and Modeling in R

Practice Lab: Data Science Using R

The Data Science Using R Lab will provide you with the necessary platform to gain hands on skills where you can practice different tasks related to MongoDB. You will cover areas like manipulating a data set using multiple dplyr verbs, adding the browser function to some R code to debug it, using xtable to output a table in LaTeX format, and creating an R Markdown file (.rmd) and rendering the output as html.