27 R (programming language) courses

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. LESSON 1: SETTING UP R AND EXECUTING SIMPLE CODE * Topic A: Set Up the R Development Environment * Topic B: Write R Statements LESSON 2: PROCESSING ATOMIC DATA TYPES * Topic A: Process Characters * Topic B: Process Numbers * Topic C: Process Logicals LESSON 3: PROCESSING DATA STRUCTURES * Topic A: Process Vectors * Topic B: Process Factors * Topic C: Process Data Frames * Topic D: Subset Data Structures LESSON 4: WRITING CONDITIONAL STATEMENTS AND LOOPS * Topic A: Write Conditional Statements * Topic B: Write Loops LESSON 5: STRUCTURING CODE FOR REUSE * Topic A: Define and Call Functions * Topic B: Apply Loop Functions * Topic C: Manage R Packages LESSON 6: MANAGING DATA IN R * Topic A: Load Data * Topic B: Save Data * Topic C: Manipulate Data Frames Using Base R * Topic D: Manipulate Data Frames Using dplyr * Topic E: Handle Dates and Times LESSON 7: ANALYZING DATA IN R * Topic A: Examine Data * Topic B: Explore the Underlying Distribution of Data * Topic C: Identify Missing Values LESSON 8: VISUALIZING DATA IN R * Topic A: Plot Data Using Base R Functions * Topic B: Plot Data Using ggplot2 * Topic C: Format Plots in ggplot2 * Topic D: Create Combination Plots LESSON 9: MODELING DATA IN R * Topic A: Create Statistical Models in R * Topic B: Create Machine Learning Models in R

In this course, we'll learn how to visualize data with real weather data downloaded from the US National Weather Service using the R programming language and RStudio. We recommend that you have some background in HTML, CSS, and JavaScript. You don't need to be an expert by any means, but you should have experience building web pages with HTML and CSS, and you should have basic programming skills with JavaScript.

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. WHAT IS R * ? What is R? * ? Positioning of R in the Data Science Space * ? The Legal Aspects * ? Microsoft R Open * ? R Integrated Development Environments * ? Running R * ? Running RStudio * ? Getting Help * ? General Notes on R Commands and Statements * ? Assignment Operators * ? R Core Data Structures * ? Assignment Example * ? R Objects and Workspace * ? Printing Objects * ? Arithmetic Operators * ? Logical Operators * ? System Date and Time * ? Operations * ? User-defined Functions * ? Control Statements * ? Conditional Execution * ? Repetitive Execution * ? Repetitive execution * ? Built-in Functions * ? Summary INTRODUCTION TO FUNCTIONAL PROGRAMMING WITH R * ? What is Functional Programming (FP)? * ? Terminology: Higher-Order Functions * ? A Short List of Languages that Support FP * ? Functional Programming in R * ? Vector and Matrix Arithmetic * ? Vector Arithmetic Example * ? More Examples of FP in R * ? Summary MANAGING YOUR ENVIRONMENT * ? Getting and Setting the Working Directory * ? Getting the List of Files in a Directory * ? The R Home Directory * ? Executing External R commands * ? Loading External Scripts in RStudio * ? Listing Objects in Workspace * ? Removing Objects in Workspace * ? Saving Your Workspace in R * ? Saving Your Workspace in RStudio * ? Saving Your Workspace in R GUI * ? Loading Your Workspace * ? Diverting Output to a File * ? Batch (Unattended) Processing * ? Controlling Global Options * ? Summary R TYPE SYSTEM AND STRUCTURES * ? The R Data Types * ? System Date and Time * ? Formatting Date and Time * ? Using the mode() Function * ? R Data Structures * ? What is the Type of My Data Structure? * ? Creating Vectors * ? Logical Vectors * ? Character Vectors * ? Factorization * ? Multi-Mode Vectors * ? The Length of the Vector * ? Getting Vector Elements * ? Lists * ? A List with Element Names * ? Extracting List Elements * ? Adding to a List * ? Matrix Data Structure * ? Creating Matrices * ? Creating Matrices with cbind() and rbind() * ? Working with Data Frames * ? Matrices vs Data Frames * ? A Data Frame Sample * ? Creating a Data Frame * ? Accessing Data Cells * ? Getting Info About a Data Frame * ? Selecting Columns in Data Frames * ? Selecting Rows in Data Frames * ? Getting a Subset of a Data Frame * ? Sorting (ordering) Data in Data Frames by Attribute(s) * ? Editing Data Frames * ? The str() Function * ? Type Conversion (Coercion) * ? The summary() Function * ? Checking an Object's Type * ? Summary EXTENDING R * ? The Base R Packages * ? Loading Packages * ? What is the Difference between Package and Library? * ? Extending R * ? The CRAN Web Site * ? Extending R in R GUI * ? Extending R in RStudio * ? Installing and Removing Packages from Command-Line * ? Summary READ-WRITE AND IMPORT-EXPORT OPERATIONS IN R * ? Reading Data from a File into a Vector * ? Example of Reading Data from a File into A Vector * ? Writing Data to a File * ? Example of Writing Data to a File * ? Reading Data into A Data Frame * ? Writing CSV Files * ? Importing Data into R * ? Exporting Data from R * ? Summary STATISTICAL COMPUTING FEATURES IN R * ? Statistical Computing Features * ? Descriptive Statistics * ? Basic Statistical Functions * ? Examples of Using Basic Statistical Functions * ? Non-uniformity of a Probability Distribution * ? Writing Your Own skew and kurtosis Functions * ? Generating Normally Distributed Random Numbers * ? Generating Uniformly Distributed Random Numbers * ? Using the summary() Function * ? Math Functions Used in Data Analysis * ? Examples of Using Math Functions * ? Correlations * ? Correlation Example * ? Testing Correlation Coefficient for Significance * ? The cor.test() Function * ? The cor.test() Example * ? Regression Analysis * ? Types of Regression * ? Simple Linear Regression Model * ? Least-Squares Method (LSM) * ? LSM Assumptions * ? Fitting Linear Regression Models in R * ? Example of Using lm() * ? Confidence Intervals for Model Parameters * ? Example of Using lm() with a Data Frame * ? Regression Models in Excel * ? Multiple Regression Analysis * ? Summary DATA MANIPULATION AND TRANSFORMATION IN R * ? Applying Functions to Matrices and Data Frames * ? The apply() Function * ? Using apply() * ? Using apply() with a User-Defined Function * ? apply() Variants * ? Using tapply() * ? Adding a Column to a Data Frame * ? Dropping A Column in a Data Frame * ? The attach() and detach() Functions * ? Sampling * ? Using sample() for Generating Labels * ? Set Operations * ? Example of Using Set Operations * ? The dplyr Package * ? Object Masking (Shadowing) Considerations * ? Getting More Information on dplyr in RStudio * ? The search() or searchpaths() Functions * ? Handling Large Data Sets in R with the data.table Package * ? The fread() and fwrite() functions from the data.table Package * ? Using the Data Table Structure * ? Summary DATA VISUALIZATION IN R * ? Data Visualization * ? Data Visualization in R * ? The ggplot2 Data Visualization Package * ? Creating Bar Plots in R * ? Creating Horizontal Bar Plots * ? Using barplot() with Matrices * ? Using barplot() with Matrices Example * ? Customizing Plots * ? Histograms in R * ? Building Histograms with hist() * ? Example of using hist() * ? Pie Charts in R * ? Examples of using pie() * ? Generic X-Y Plotting * ? Examples of the plot() function * ? Dot Plots in R * ? Saving Your Work * ? Supported Export Options * ? Plots in RStudio * ? Saving a Plot as an Image * ? Summary USING R EFFICIENTLY * ? Object Memory Allocation Considerations * ? Garbage Collection * ? Finding Out About Loaded Packages * ? Using the conflicts() Function * ? Getting Information About the Object Source Package with the pryr Package * ? Using the where() Function from the pryr Package * ? Timing Your Code * ? Timing Your Code with system.time() * ? Timing Your Code with System.time() * ? Sleeping a Program * ? Handling Large Data Sets in R with the data.table Package * ? Passing System-Level Parameters to R * ? Summary LAB EXERCISES * Lab 1 - Getting Started with R * Lab 2 - Learning the R Type System and Structures * Lab 3 - Read and Write Operations in R * Lab 4 - Data Import and Export in R * Lab 5 - k-Nearest Neighbors Algorithm * Lab 6 - Creating Your Own Statistical Functions * Lab 7 - Simple Linear Regression * Lab 8 - Monte-Carlo Simulation (Method) * Lab 9 - Data Processing with R * Lab 10 - Using R Graphics Package * Lab 11 - Using R Efficiently

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

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This course provides basic introductory guidance to FinTech. You will be using an easy programming language R to learn some basic statistics in money management. You will also understand how to time the stock market and build tradeable factor-based algorithms from scratch. This course provides some of the most basic rules of thumb and intuition that every successful trader should know.