Templates

Boost your programming skills with free Python, SQL, R, and Excel templates and create top-notch projects for your portfolio.

Save hours of browsing through the Internet and accelerate your workflow with code-ready templates for your programming projects. Personally crafted by 365 Data Science instructors, these templates bring a surprising efficiency level to your work process.

Purple Blue Cover of Histogram with ggplot2 in R. This template resource is from 365 Data Science.
Templates r

Histogram with ggplot2 in R Template

The histogram is a popular graph for visually representing the data distribution of a feature. This is a free .r histogram template showing the distribution of house prices implemented in R using ggplot2. You will be taught how to build the first three layers of a ggplot- defining the data, aesthetics, and geometry, and set bin parameters You finish by adding a title, changing the background theme and visually tweaking the aesthetics of your histogram.

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Yellow Cover of Sending a GET Request in Python. This template resource is from 365 Data Science.
Templates python

Sending a GET Request in Python Template

This free .ipynb template demonstrates how to send an HTTP GET request in Python which is the backbone of the modern internet- the most popular one is the GET request. This type of request is the most basic form of request since it is sent to servers when opening web pages or accessing APIs. In this template, we have implemented a simple GET request using the Python library "requests".

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Yellow Cover of Stacked Area Chart Notebook in Matplotlib Python. This template resource is from 365 Data Science team.
Templates python

Stacked Area Chart Notebook in Matplotlib Python Template

In this free .ipynb template we have a stacked area chart, implemented in Python with the Pyplot module of Matplotlib. The chart shows the popularity of different engine types in automobiles across the span of several decades. In the stacked area chart each category is ‘stacked’ or ‘placed’ on top of the previous, presenting the totality of the data and avoiding overlap among categories.

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