Learn more about the famous pipe operator %>% and other pipes in R, why and how you should use them and what alternatives you can consider! Learn how to effectively use list comprehension in Python to create lists, to replace (nested) for loops and the map(), filter() and reduce() functions, ...! Learn more about LDA2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. A comprehensive introductory tutorial to Python loops. Learn and practice while and for loops, nested loops, the break and continue keywords, the range function and more! Learn to scrape novels from the web and plot word frequency distributions; You will gain experience with Python packages requests, BeautifulSoup and nltk. In this tutorial, you’ll learn about multi-indices for pandas DataFrames and how they arise naturally from groupby operations on real-world data sets. In this tutorial, you'll learn how to use the pandas groupby operation, which draws from the well-known split-apply-combine strategy, on Netflix movie data.
Tackle probability and statistics in Python: learn more about combinations and permutations, dependent and independent events, and expected value. This NetworkX tutorial will show you how to do graph optimization in Python by solving the Chinese Postman Problem in Python. This scikit-learn tutorial will walk you through building a fake news classifier with the help of Bayesian models.
This Seaborn tutorial introduces you to the basics of statistical data visualization Apache Spark tutorial introduces you to big data processing, analysis and ML with PySpark. A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hall of Fame The data.table cheat sheet helps you master the syntax of this R package, and helps you to do data manipulations. This tutorial covers 5 ways in which you can easily write pandorable or more idiomatic Pandas code.