R is a functional programming environment for business analysts and data scientists. It's a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.
Introduction to R Programming for Data Science & Analytics is a comprehensive hands-on course that presents common scenarios encountered in analysis and present practical solutions. In this course, special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like Madlib are included.
This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:
- R Language and Mathematics
- How to work with R Vectors
- How to read and write data from files, and how to categorize data in factors
- How to work with Dates and perform Date math
- How to work with multiple dimensions and DataFrame essentials
- Essential Data Science and how to use R with it
- Visualization in R
- How R can be used in Spark (Optional / Overview)
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional R Programming, Python, data science, AI / machine learning / deep learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This course, geared for Data Analysts and Data Scientists who need to learn the essentials of how to program in R. Incoming students should have prior experience working with Excel or SAS, and should know the basics of SQL. Students should have intermediate-level experience in their field, and prior experience working with programming languages.
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
- From Excel or SAS to R (Optional)
- Common challenges with Excel / SAS
- The R Environment
- Hello, R
- Working with R Studio
- R Basics
- Simple Math with R
- Working with Vectors
- Comments and Code Structure
- Using Packages
- Vector Properties
- Creating, Combining, and Iteratorating
- Passing and Returning Vectors in Functions
- Logical Vectors
- Reading and Writing
- Text Manipulation
- Working with Dates
- Date Formats and formatting
- Time Manipulation and Operations
- Multiple Dimensions
- Adding a second dimension
- Indices and named rows and columns in a Matrix
- Matrix calculation
- n-Dimensional Arrays
- Data Frames
- R in Data Science
- AI Grouping Theory
- Linear Regression
- Logistic Regression
- Elastic Net
- R with MadLib
- Importing and Exporting static Data (CSV, Excel)
- Using Libraries with CRAN
- K-means with Madlib
- Regression with Madlib
- Other libraries
- Data Visualization
- Powerful Data through Visualization: Communicating the Message
- Techniques in Data Visualization
- Data Visualization Tools
- Databases, Data lakes & Additional Topics
- Building connections to Databases and Data lakes, for both Python and R (using Hive server)
- Methods to “query” data from database and data lakes, for both Python and R
- Creating and passing macro variables. Specifically, R sprint, paste, paste0, and paste3 (not sure of the equivalent in Python).
Optional - Time Permitting Topics
- R with Hadoop
- Overview of Hadoop
- Overview of Distributed Databases
- Overview of Pig
- Overview of Mahout
- Exploiting Hadoop clusters with R
- Hadoop, Mahout, and R
- Business Rule Systems
- Rule Systems in the Enterprise
- Enterprise Service Busses
- Using R with Drools
- R with AWS (Optional, Upon Request)
- Best practices for working with AWS (completely outside of R and Python)
Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.
Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.