Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames.
Master the basics of data analysis in Python. Expand your data science skill set by learning scientific computing with numpy.
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 about datasets in R, study types, sampling strategies, and experimental design.
Learn to train and assess models performing common machine learning tasks such as classification, regression and clustering.
Master fundamental techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.
Learn to explore your data so you can properly clean and prepare it data for analysis.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn how to parse data in any format. Whether it's flat files, statistics software, databases, or web data, you'll handle it all.