TTDS6683: R Programming Essentials for Data Science & Analytics
About this Course
R is a functional programming environment for business analysts and data scientists, serving as the perfect tool for solving statistical, numerical, or probability-based problems based on real data, when they’ve pushed Excel past its limits. Introduction to R Programming for Data Science and Analytics is a three-day, hands-on course designed to provide you with the practical skills and experience needed use R to handle large datasets, perform intricate analyses and data management, and create vivid and insightful data visualizations – all essential competencies for today’s data professional.
Working in a hands-on learning environment guided by our expert instructor, you’ll gain an comprehensive understanding of R programming. Starting with the basics, you’ll learn about R and RStudio, the installation of packages, and diverse variable types and data structures. Data visualization forms a critical part of the learning journey as you learn to create base plots, use ggplot2, and explore interactive data visualization tools like Shiny and Plotly. The course emphasizes data management skills, teaching you data summarization, how to create factor variables, merge and join data, and use the table() function effectively.
By the end of the course, you’ll be proficient in data import and export, including handling Excel spreadsheets, creating and plotting linear model objects, creating data summarization tables, and combining matrices of objects into data frames. You’ll have the skills to manage data efficiently and produce engaging, insightful visualizations, ready to apply this knowledge in real-world projects. With these competencies, you’ll be well-positioned to make a tangible impact on your team’s data analytics processes, making you an invaluable asset in your professional ecosystem.
Audience Profile
This introductory-level course is ideal for technical team members who are new to R programming. Attendee roles might include (but are not limited to) data analysts, software developers, IT professionals, and data-driven project managers seeking to enhance their data manipulation and visualization skills. The course will also benefit data enthusiasts who want to gain hands-on experience in R programming for improved business analytics and decision making.
At Course Completion
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment you’ll explore:
· Understand the basics and become comfortable with using these core tools for data science and analytics.
· Learn how to create compelling and insightful data visualizations using tools such as ggplot2, and interactive graphics with Shiny and Plotly.
· Become skilled at managing, summarizing, and merging datasets, enhancing your ability to handle real-world, complex data.
· Learn how to install and effectively use a variety of R packages, widening your scope of data manipulation and analysis.
· Gain practical experience in working with different data sources, including excel spreadsheets, and exporting your results for further use or presentation.
· Apply the techniques learned throughout the course to solve practical, data-centric tasks, turning raw data into actionable insights.
Outline
1. Introduction to R
· Introduction to R
· Using R and RStudio
· Installing Packages
· Variable Types and Data Structures
· Numeric and Integers
· Vectors
· Basic Flow Control
· Data Import and Export
· Excel Spreadsheets
· Package Documentation and Vignettes
2. Data Visualization and Graphics
· Creating Base Plots
· Factor Variables
· Creating and Plotting a Linear Model Object
· Titles and Axis Labels
· ggplot2 Basics
· Histogram
· Bar Chart
· Scatterplot
· Boxplot
· Facet Wrapping and Gridding
· Exploring Shiny and Plotly
3. Data Management
· Creating Factor Variables in a Dataset
· Creating an Ordered Factor Variable
· Summarizing Data
· Data Summarization Tables
· Tables in R
· Creating Different Tables Using the table() Function
· Summarizing Data with the Apply Family
· Combining Matrices of Objects into Dataframes
· Merging and Joining Data
· Demonstrating Merges and Joins in R