Course Summary
Welcome to Let's Learn R for Data Science! We extend a warm welcome,
especially if this is your first encounter with R, data science, or
programming in general.
In this course, we will embark on a journey through R, assuming
no prior programming experience. Each topic is designed to be
beginner-friendly, allowing learners from all backgrounds to
comfortably grasp the concepts.
While data science heavily relies on statistics, we will
prioritize simplicity by keeping the statistics and math
to a minimum throughout this course.
R stands out as one of the most popular programming languages
for data science due to its user-friendly nature and its
powerful abilities in tackling complex challenges in big data analysis.
The adoption of R is further amplified by the comprehensive data science
package Tidyverse, which offers extensive functionalities.
In addition, R is increasingly being integrated into academic curricula
and embraced across various industries.
Digi Cafe courses are built around the following three pillars:
- User-friendly text: Courses are designed as user-friendly as possible,
and are text-based so it is easier for a student to find and review material.
- Interactive code: Code editors that can be run in the browser are spread
throughout lessons to get hands-on experience learning the programming language.
- Community: If you have any questions or just want to engage in discussion
on the course material, you may join the
Digi Cafe Discord community where we have
a chat room for each programming language.
Learning Goals
Upon completing this course, you will have acquired
the following knowledge and skills:
- What is data: We will start with exploring the world
of data science and learn about R's pioneering concept of the
dataframe, a data structure that organizes data in an intuitive
and easy to work with way.
- What is programming: We will take a brief detour into
the history of programming to gain an understanding of how a
how a computer interprets code as well as how it's managed.
- Assignment and classes: We will finish the introduction
section by learning some fundamental programming concepts
such as assignment and classes.
- Tidyverse: We will learn the Tidyverse package,
which is R's most popular collection of packages for data manipulation
and visualization. Two of the key packages in the Tidyverse are
dplyr and ggplot2 which includes the
data manipulation functions and visualization functions respectively.
- What is the Tidyverse: In the Tidyverse we will learn how to
chain operations, summarise data, create new data from existing data,
work with grouped data, and transform its shape.
- ggplot2: In this visualization package we will learn
more on how to make visualizations such as scatter plots, line plots,
bar plots, and add colour to them.
- Loops and conditional statements: These two essential
programming techniques are crucial for controlling program
flow and automating tasks effectively, where loops enable code
repetition and conditional statements allow for selective code execution
based on conditions.
- A final project: We will conclude with a final project which
utilizes everything we have learned in the course.
In this course, our primary focus will be on learning R in the context
of data science. Instead of simply memorizing individual commands and
functions, we will adopt a comprehensive approach. Each lesson will build
upon the previous ones, systematically breaking down R concepts
to ensure a thorough understanding. So that you can gain first hand
experience with R, many of the lessons in this course
include interactive code blocks with R code that can be run directly
on the webpage.
By the end of the course, you will possess a strong foundation
in both R programming and data science. This knowledge will
serve as a solid base for further exploration and continued
learning in these fields.