Handledningar i data science

In this tutorial, you'll learn with the help of an example how "Contingency Analysis" or "Chi-square test of independence" works and also how efficiently we can perform it using R.

This tutorial takes course material from DataCamp's Cleaning Data in Python course and allows you to clean strings using regular expressions.

Learn about the basic statistics required for Data Science and Machine Learning in Python.

This tutorial takes course material from DataCamp's free Intro to R course and allows you to practice Factors.

Practice Lists in R by using course material from DataCamp's Intro to R course.

In this tutorial, you're going to learn how to implement customer segmentation using RFM(Recency, Frequency, Monetary) analysis from scratch in Python.

When you know how many times you want to repeat an action, a for loop is a good option. Master for loops with this tutorial.

In this tutorial, you will learn how to analyze and display data using Tableau and make better, more data-driven decisions.

Learn how to join and merge DataFrames in pandas using concat(), merge(), and join() with practical examples.

In this tutorial, you shall work with lists and learn an efficient way to divide arbitrarily sized lists into chunks of a given size.

There is an optimal number of hidden layers and neurons for an artificial neural network (ANN). This tutorial discusses a simple approach for determining the optimal numbers for layers and neurons for ANN's.

Learn to use data visualization tools provided by the VIM package to gain quick insights into the missing data patterns.