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Data Science Tutorials

Advance your data career with our data science tutorials. We walk you through challenging data science functions and models step-by-step.
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Python

Scikit-Learn Tutorial: Baseball Analytics Pt 1

A scikit-learn tutorial to predicting MLB wins per season by modeling data to KMeans clustering model and linear regression models.

Daniel Poston

May 4, 2017

Python

Python Exploratory Data Analysis Tutorial

Learn the basics of Exploratory Data Analysis (EDA) in Python with Pandas, Matplotlib and NumPy, such as sampling, feature engineering, correlation, etc.
Karlijn Willems's photo

Karlijn Willems

March 15, 2017

Python

Python Dictionary Tutorial

In this Python tutorial, you'll learn how to create a dictionary, load data in it, filter, get and sort the values, and perform other dictionary operations.
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DataCamp Team

February 16, 2017

R Programming

Algorithmic Trading in R Tutorial

In this R tutorial, you'll do web scraping, hit a finance API and use an htmlwidget to make an interactive time series chart to perform a simple algorithmic trading strategy
Ted Kwartler's photo

Ted Kwartler

February 9, 2017

Python

Scipy Tutorial: Vectors and Arrays (Linear Algebra)

A SciPy tutorial in which you'll learn the basics of linear algebra that you need for machine learning in Python, with a focus how to with NumPy.
Karlijn Willems's photo

Karlijn Willems

February 8, 2017

R Programming

Web Scraping and Parsing Data in R | Exploring H-1b Data Pt. 1

Learn how to scrape data from the web, preprocess it and perform a basic exploratory data analysis with R
Ted Kwartler's photo

Ted Kwartler

January 12, 2017

R Programming

15 Easy Solutions To Your Data Frame Problems In R

Discover how to create a data frame in R, change column and row names, access values, attach data frames, apply functions and much more.
Karlijn Willems's photo

Karlijn Willems

January 10, 2017

Python

Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN

This article will explain the importance of preprocessing in the machine learning pipeline by examining how centering and scaling can improve model performance.
Hugo Bowne-Anderson's photo

Hugo Bowne-Anderson

April 26, 2016