HarvardX: Data Science: R Basics
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
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- Certification
- Certificate of completion
- Duration
- 8 weeks
- Price Value
- $ 219
- Difficulty Level
- Introductory
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Harvard's Professional Certificate Program in Data Science
Welcome to "Data Science: R Basics," the first course in Harvard's Professional Certificate Program in Data Science. This exciting and hands-on course is designed to introduce you to the fundamentals of R programming, a powerful tool in the world of data science. Instead of learning R in isolation, you'll dive into a real-world scenario, using crime data from the United States to develop practical skills that can be applied to various data analysis challenges.
While this course is designed for beginners, having an up-to-date web browser is recommended to enable programming directly in a browser-based interface. No prior programming experience is required, but a basic understanding of mathematics and statistics would be beneficial.
The skills acquired in this course are highly valuable in today's data-driven world. Learners will be able to:
By completing this course, you'll be well-prepared to tackle the subsequent courses in the Professional Certificate Program in Data Science, which cover more advanced topics like probability, inference, regression, and machine learning. Join us on this exciting journey into the world of data science and R programming!
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This course covers the basics of R: a free programming language and software environment used for statistical computing and graphics. R is widely used by data analysts, statisticians, and data scientists around the world. This course covers an introduction to R, from installation to basic statistical functions. You will learn to work with variable and external data sets, write functions, and hear from one of the co-creators of the R language, Robert Gentleman.
"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.