Python is a popular and powerful interpreted language. Unlike R, Python is a complete language and platform that you can use for both research and development.

Why Python?

  1. Simple and consistent: Python offers concise and readable code. While complex algorithms and versatile workflows stand behind machine learning and deep learning, Python’s simplicity allows developers to write reliable systems. Python code is understandable by humans, which makes it easier to build models for machine learning and deep learning.
  2. Extensive selection of libraries and frameworks: Implementing machine learning and deep learning algorithms can be tricky and requires a lot of time. Python provides a well-structured and well-tested environment that enables developers to come up with the best coding solutions.
  3. Platform independence: Python is a platform independent language. Python code can be used to create standalone executable programs for most common operating systems. Python is supported by many platforms including Linux, Windows, and macOS.
  4. Great community and popularity: Python is among the top 10 most popular programming languages.

Some popular Python libraries are Pandas, Numpy, SciPy, Seaborn, Scikit-learn,Tensorflow and Keras . A comprehensive list of python standard libraries can be found here.

It is common for today’s scientific and business industries to collect large amounts of data, and the ability to analyze the data and learn from it is critical to making informed decisions. Machine learning is a branch in computer science that studies the design of algorithms that can learn. R is one of the major languages for data science. It provides excellent visualization features, which is essential to explore the data before submitting it to any automated learning, as well as assessing the results of the learning algorithm.

Why R?

  1. R is free: Unlike other proprietary software packages that require expensive licenses, R is open source and you can always download it for free. No matter where you travel, you can have access to R on your computer.
  2. R gives you access to cutting-edge technology: Top researchers develop statistical learning methods in R, and new algorithms are constantly added to the list of packages you can download.
  3. R is a useful skill: Employers that value analytics recognize R as useful and important. If for no other reason, learning R is worthwhile to help boost your resume.

Some popular R packages/libraries are caret, ggplot2, mlbench, class, caTools, randomForest, impute, ranger, kernlab, class, glmnet, naivebayes, rpart, rpart.plot.