R Programming Essentials for Data Science & Analytics (TTDS6683)

Course Objectives

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, you'll explore 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:

• Data Science essentials
• R programming Essentials
• Variables and Types, Loops, R Scalars, Vectors, and Matrices
• String and Text Manipulation, List & Functions
• DataFrames and File I/O
• Reading data from files and data prep
• Visualization
• Exploration With Dplyr
• Statistical Modeling With R
• Data Exploration
• Regressions
• R and Big Data

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.

Course Prerequisites

Course Agenda

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. Each chapter below has hands-on labs.

1. Session: Data Science Essentials

• Data Science
• Process of Doing Data Science

2. Session: Introducing R

• R Essentials
• Variables and Types
• Control Structures (Loops / Conditionals)
• R Scalars, Vectors, and Matrices
  • Defining R Vectors
  • Matrices
• String and Text Manipulation
  • Character data type
  • File IO
• Lists
• Functions
  • Introducing Functions
  • Closures
  • lapply/sapply functions
• DataFrames

3. Session: Intermediate R

• DataFrames and File I/O
• Reading data from files
• Data Preparation
• Built-in Datasets
• Visualization
  • Graphics Package
  • plot() / barplot() / hist() / boxplot() / scatter plot
  • Heat Map
  • ggplot2 package ( qplot(), ggplot())
• Exploration With Dplyr

4. Session: Analytics With R

• Statistical Modeling With R
  • Statistical Functions
  • Dealing With NA
  • Distributions (Binomial, Poisson, Normal)
• Data Exploration
• Regressions
  • Linear Regressions
  • Logistic Regressions
• Text Processing (tm package / Wordclouds)
• R and Big Data

Course Materials

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.