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Data Science with R

EasyComputer ScienceData Science10 chapters

An introduction to the R programming language, focusing on the fundamental data structures, control flow, and functional programming concepts needed for data analysis. It also covers essential utilities for importing data, debugging code, and running statistical simulations.

What This Course Covers

Data Science with R is structured into 10 chapters that build on each other progressively:

Chapter 1: Overview of R▼
Chapter 2: Data Structures and Variables▼
Chapter 3: Indexing, Subsetting, and Element-Wise Operations▼
Chapter 4: Importing, Exporting, and Data Connections▼
Chapter 5: Conditional Logic, Iteration, and Code Style▼
Chapter 6: Functions and Scoping Rules▼
Chapter 7: The Apply Family and Functional Looping▼
Chapter 8: Data Transformations with dplyr and Date-Time Parsing▼
Chapter 9: Error Handling, Interactive Debugging, and Profiling▼
Chapter 10: Probability Distributions, Simulations, and Sampling▼

Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.

How to Study Data Science with R on Lambdio

Lambdio's AI-powered platform adapts to how Computer Science courses are best learned. Here's our recommended approach:

Learning Mode
Standard Mode — for first-time learning of each chapter
Review Modes
Standard, Quiz — for spaced repetition reviews
Learning Priority
Low Priority — controls how often the algorithm schedules reviews

Data Science with R is a code-heavy introductory course that teaches programming fundamentals alongside data analysis. Standard Mode is the ideal learning mode because mastering R syntax, vectorized operations, dplyr verbs, and the apply family requires structured explanations, live code demonstrations, and hands-on practice — all of which Standard Mode delivers through interactive tutorials with comprehension checks. Socratic Mode would be less effective here since discovering R's subsetting rules or scoping behavior through Socratic questioning alone would be slow without direct code exposure. The Easy difficulty of this course makes Low priority the appropriate default — the material is foundational and accessible, so a less frequent review schedule is sufficient. For quick terminology checks on data types, dplyr verbs, and function syntax, use Quiz Mode to reinforce the basics before progressing to more advanced data analysis courses.

Interactive Quiz

Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:

Q1: Which of the following is NOT one of the five atomic classes of R objects?
Q2: What does the assignment operator <- do in R?
Q3: When subsetting a list, what is the difference between [ and [[?
Q4: Which argument in read.table() can double execution speed by pre-specifying column types?
Q5: What does the next command do inside an R loop?
Q6: R uses lexical scoping. Where does R look up the value of a free variable in a function?
Q7: What does the sapply() function return when every element of the result has length 1?

What You'll Be Able to Do After This Course

  • ✓Write and execute R code using variables, atomic data types, vectors, lists, matrices, and data frames
  • ✓Subset and index R objects using bracket operators, logical conditions, and element-wise operations
  • ✓Import tabular data from CSV files and text connections, and optimize loading for large datasets
  • ✓Implement conditional logic and iteration using if-else statements, for loops, and while loops
  • ✓Define custom R functions, apply lexical scoping rules, and leverage lazy evaluation and argument matching
  • ✓Use the apply family of functions (lapply, sapply, apply, mapply, tapply) for concise functional looping
  • ✓Manipulate and transform data frames using dplyr verbs and the pipe operator for readable workflows
  • ✓Debug R code using traceback, debug, browser, trace, and recover, and profile performance with system.time

Frequently Asked Questions

Do I need prior programming experience to take this course?▼
What software do I need to install?▼
How long does it take to complete this course?▼
Will this course teach me statistics or just R programming?▼
How does Lambdio's AI tutor help me learn R more effectively?▼

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