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Python for R Users

This course is for R users who want to get up to speed with Python!

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5 Hours15 Videos57 Exercises11,623 Learners4950 XP

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Course Description

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.

  1. 1

    The Basics


    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.

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    50 xp
    Assignment and data types
    100 xp
    Arithmetic with strings
    100 xp
    Containers - lists and dictionaries
    50 xp
    100 xp
    100 xp
    Functions, methods, and libraries
    50 xp
    100 xp
    NumPy arrays
    100 xp
    Pandas DataFrames
    100 xp
  2. 3


    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.

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Daniel Chen

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.
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