Free online courseIntroduction to Programming

Course Description

The "Introduction to Programming with R for Data Science" is an expertly crafted course designed to serve as an entry point for those aspiring to delve into the expansive world of data science and business intelligence. With a total duration of 8 hours and 48 minutes, this course is ideal for individuals aiming to build a solid foundation in programming using R, one of the most powerful tools in data science.

As part of the Information Technology category, this course specifically addresses the skills required under the Data Science and Business Intelligence subcategory. It begins with an introductory module, Introduction, which familiarizes students with the basics of R programming and sets the stage for more complex topics.

Following the introduction, Lecture 1 - Representing Data delves into the various ways data can be represented in R. You'll learn about different data types and structures, and how to efficiently handle them within the programming environment. This essential knowledge serves as the backbone for understanding and manipulating data.

Next, in Lecture 2 - Transforming Data, the course explores techniques to transform and manipulate data sets, enabling you to prepare data for analysis. This module focuses on the key operations necessary to clean and modify data, making it ready for deeper analysis and insights.

Lecture 3 - Applying Functions shifts the focus towards the functional programming capabilities of R. This lecture emphasizes the significance of functions in R, demonstrating how they can be utilized to streamline and optimize data processing tasks.

In Lecture 4 - Tidying Data, students are introduced to the concept of tidy data, a standard way of organizing data sets that makes them easier to analyze. This lecture covers the principles of tidy data and the tools within R that facilitate tidying operations.

Visualization is a critical component of data science, and Lecture 5 - Visualizing Data arms students with the skills to create compelling and informative visual representations of data. By using R's powerful visualization libraries, you'll learn how to make your data come alive through effective visuals.

Lecture 6 - Testing Programs focuses on the importance of testing in programming. This lecture teaches you how to write tests to ensure the reliability and accuracy of your R programs, an indispensable skill for any data scientist.

Finally, Lecture 7 - Packaging Programs introduces the concept of packaging in R. This module guides you through the process of organizing your R code into packages, making it easier to share and reuse your code in future projects.

This comprehensive course is yet to receive reviews but stands as a promising gateway for individuals eager to explore the realm of data science through the lens of R programming. Whether you are a novice or looking to reinforce your data science capabilities, "Introduction to Programming with R for Data Science" is structured to equip you with the essential skills and knowledge needed to excel in the field.