Data Science: R Fundamentals
Master R programming fundamentals to manipulate data, perform statistical analysis, and create compelling visualizations for data-driven insights.
Master R programming fundamentals to manipulate data, perform statistical analysis, and create compelling visualizations for data-driven insights.
This course provides a comprehensive introduction to R programming for data science. Designed for beginners, it covers fundamental R concepts and their practical application to real-world data analysis. Using a dataset on crime in the United States, students will learn how to manipulate, analyze, and visualize data effectively. The curriculum progresses from basic R syntax and data types to more advanced topics like data wrangling and visualization. By focusing on building a solid foundation rather than covering all R techniques, this course prepares students for tackling real-world data analysis challenges. Participants will gain hands-on experience in using R to address key questions about crime disparities across different states, developing crucial skills for the rapidly growing field of data science.
4.8
(5 ratings)
English
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What you'll learn
Master essential R syntax for data analysis
Understand fundamental programming concepts in R including data types, vectors, and indexing
Apply conditional statements and loops in R programming
Perform data wrangling operations using dplyr
Create basic data visualizations in R
Analyze real-world datasets using R
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course offers a comprehensive introduction to R programming for data science, focusing on building a strong foundation in essential concepts and techniques. The curriculum is structured to progressively introduce key R programming elements, starting with basic syntax and data types, and advancing to more complex operations and data manipulation techniques. Students will learn about functions, data types, vectors, and advanced functions in R. The course also covers general programming concepts such as conditional statements and loops, specifically in the context of R. A significant portion of the course is dedicated to practical application, where students will use real-world crime data from the United States to learn data wrangling, analysis, and visualization techniques. By the end of the course, participants will have developed the skills to manipulate data, create visualizations, and perform basic statistical analyses using R.
Fee Structure
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4.8 course rating
5 ratings
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