Data Analysis with R E-Learning Training Certified Teachers Exam Quizzes Assessments Test Exam Live Labs Tips Tricks Certificate.
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Product description
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:
Demo Data Analysis with R Training
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
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.
Specifications
Article number
128303322
SKU
128303322
Language
English
Qualifications of the Instructor
Certified
Course Format and Length
Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration
26 Hours
Assesments
The assessment tests your knowledge and application skills of the topics in the learning pathway. It is available 365 days after activation.
Online Virtuele labs
Receive 12 months of access to virtual labs corresponding to traditional course configuration. Active for 365 days after activation, availability varies by Training
Online mentor
You will have 24/7 access to an online mentor for all your specific technical questions on the study topic. The online mentor is available 365 days after activation, depending on the chosen Learning Kit.
Progress monitoring
Access to Material
365 days
Technical Requirements
Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance
Helpdesk and online knowledge base 24/7
Certification
Certificate of participation in PDF format
Price and costs
Course price at no extra cost
Cancellation policy and money-back guarantee
We assess this on a case-by-case basis
Award Winning E-learning
Tip!
Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.
Data Analysis with R E-Learning Training Certified Teachers Exam Quizzes Assessm...
€360,58€298,00
Specifications
Article number
128303322
SKU
128303322
Language
English
Qualifications of the Instructor
Certified
Course Format and Length
Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration
26 Hours
Assesments
The assessment tests your knowledge and application skills of the topics in the learning pathway. It is available 365 days after activation.
Online Virtuele labs
Receive 12 months of access to virtual labs corresponding to traditional course configuration. Active for 365 days after activation, availability varies by Training
Online mentor
You will have 24/7 access to an online mentor for all your specific technical questions on the study topic. The online mentor is available 365 days after activation, depending on the chosen Learning Kit.
Progress monitoring
Access to Material
365 days
Technical Requirements
Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance
Helpdesk and online knowledge base 24/7
Certification
Certificate of participation in PDF format
Price and costs
Course price at no extra cost
Cancellation policy and money-back guarantee
We assess this on a case-by-case basis
Award Winning E-learning
Tip!
Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.
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