Wondering whether you should use Python or R for data analysis? You’ve come to the right place.
learning data science+5
Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python! In this tutorial, you'll learn about the statistical concepts behind survival analysis and you'll implement a real-world application of these methods in R.
Learn about Logistic Regression, its basic properties, and build a machine learning model on a real-world application in Python. This Keras tutorial introduces you to deep learning in Python: learn to preprocess your data, model, evaluate and optimize neural networks. In this tutorial, you will learn how to train generative models to compose music in TensorFlow 2.0. This Python for Finance tutorial introduces you to algorithmic trading, and much more. This tutorial explains how to install, run, and use Jupyter Notebooks for data science, including tips, best practices, and examples. This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself. XGBoost is one of the most popular machine learning algorithm these days. Regardless of the type of prediction task at hand; regression or classification. Learn how to perform Exploratory Data Analysis for Natural Language Processing using WordCloud in Python. Learn how to implement For Loops in Python for iterating a sequence, or the rows and columns of a pandas dataframe. Curious about which Python environment is best suited for you? In this tutorial, you'll be given an overview of the top Python IDEs used for data science. In this tutorial, you will learn why it is important to pre-process data and how to do it in pandas. Learn how to use Python and R in conjunction with each other to utilize the best of both in a single data science project.