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Python and R have seen immense growth in popularity in the "Machine Learning Age". They both are high-level languages that are easy to learn and write. The language you use will depend on your background and field of study and work. R is a language made by and for statisticians, whereas Python is a more general purpose programming language. Regardless of the background, there will be times when a particular algorithm is implemented in one language and not the other, a feature is better documented, or simply, the tutorial you found online uses Python instead of R. In either case, this would require the R user to work in Python to get his/her work done, or try to understand how something is implemented in Python for it to be translated into R. This course helps you cross the R-Python language barrier.
Learn about some of the most important data types (integers, floats, strings, and booleans) and data structures (lists, dictionaries, numpy arrays, and pandas DataFrames) in Python and how they compare to the ones in R.
Control flow, Loops, and Functions
This chapter covers control flow statements (if-else if-else), for loops and shows you how to write your own functions in Python!
In this chapter you will learn more about one of the most important Python libraries, Pandas. In addition to DataFrames, pandas provides several data manipulation functions and methods.
You will learn about the rich ecosystem of visualization libraries in Python. This chapter covers matplotlib, the core visualization library in Python along with the pandas and seaborn libraries.Plotting directly using pandas50 xpUnivariate plots in pandas100 xpBivariate plots in pandas100 xpSeaborn50 xpUnivariate plots in seaborn100 xpBivariate plots in seaborn100 xpFacet plots in seaborn100 xpMatplotlib50 xpUnivariate and bivariate plots in matplotlib100 xpSubfigures in matplotlib100 xpWorking with axes100 xpPolishing up a figure100 xp
PrerequisitesIntroduction to Writing Functions in R
Data Science Consultant at Lander Analytics
Daniel is a Software Carpentry instructor and a doctoral student in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, where he works in the Social and Decision Analytics Laboratory under the Biocomplexity Institute. He received his MPH at the Mailman School of Public Health in Epidemiology and is interested in integrating hospital data in order to perform predictive health analytics and build clinical support tools for clinicians. An advocate of open science, he aspires to bridge data science with epidemiology and health care.