R is the key that opens the door between the problems you want to solve with data and the answers you need. This course walks you through the process of answering questions through data.
Unlock the power of data analysis with our comprehensive R programming course! This exciting journey begins with a real-world problem and guides you through the entire process of solving it using data. You'll master essential techniques for data preparation, explore your data to uncover hidden insights, and develop robust predictive models. By the end of this course, you'll be equipped with the skills to confidently analyze data and deliver meaningful results in various professional settings.
This course is ideal for aspiring data analysts, data scientists, or professionals looking to enhance their data analysis skills using R. It's perfect for those with basic R programming knowledge who want to take their abilities to the next level and apply them to real-world scenarios.
The skills learned in this course are highly valuable across various industries. Graduates can apply their knowledge to:
This course offers a perfect blend of theoretical knowledge and practical application, ensuring that you'll be well-prepared to tackle real-world data analysis challenges using R. Don't miss this opportunity to elevate your data analysis skills and advance your career!
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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.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
"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.
In this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. This course covers the concepts of correlation and linear relationships, ordinary least squares (OLS) linear regression, diagnostic tests for OLS linear regression, and dummy variables.
