Master the basics of data analysis in Python. Expand your data science skill set by learning scientific computing with numpy.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames.
Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Learn to train and assess models performing common machine learning tasks such as classification, regression and clustering.
Learn to explore your data so you can properly clean and prepare it for analysis.
Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.
Master fundamental techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.
Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.
Learn the core techniques necessary to extract meaningful insights from time series data.
In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.
Learn to apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
This course was designed to get you up to speed with the most important and powerful methodologies in statistics.
Use your data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Use a rich baseball dataset from the MLB's Statcast system to practice your data exploration skills.
Learn how to parse data in any format. Whether it's flat files, statistics software, databases, or web data, you'll handle it all.
A friendly introduction to fundamental concepts in statistics in R.