Tag: Tutorials

Tutorial: Text Classification in Python Using spaCy

Learn text classification using linear regression in Python using the spaCy package in this free machine learning tutorial.

Read More

Tutorial: Introduction to Git and Github

Learn the basics of version control with Git and Github in this introductory tutorial designed to get you up and running fast.

Read More

Tutorial: Why Functions Modify Lists and Dictionaries in Python

In this beginner Python tutorial, we’ll take a look at mutable and immutable data types, and learn how to keep dictionaries and lists from being modified by our functions.

Read More

Tutorial: Poisson Regression in R

Poisson Regression can be a really useful tool if you know how and when to use it. In this tutorial we’re going to take a long look at Poisson Regression, what it is, and how R programmers can use it in the real world. Specifically, we’re going to cover: What Poisson Regression actually is and […]

Read More

Data Science Project: Profitable App Profiles for App Store and Google Play

Learn to do a complete data analysis project using only basic Python to find out what genre of apps an app developer should focus on.

Read More

Tutorial: Advanced Jupyter Notebooks

you’re doing data science in Python, notebooks are a powerful tool. This free Jupyter Notebooks tutorial has will help you get the best out of Jupyter.

Read More

Historic Wildfire Data: Exploratory Visualization in R

In recent weeks, news of the devastating wildfires sweeping parts of the US state of California have featured prominently in the news. While most wildfires are started accidentally by humans, weather conditions like wind and drought can exacerbate fires’ spread and intensity. Improved understanding of historical wildfire trends and causes can inform fire management and […]

Read More

How to Gather Your Own Data by Conducting a Great Survey

In this post, we’ll learn to create an online survey and how to prevent some common mistakes made in surveys. We’ll cover all steps of the survey process, including: Selecting a population Sampling methods Making a data analysis plan Writing good questions Distribution options Data Scientists know that even the slickest code, the best data […]

Read More

11 Design Tips for Data Visualization

Here’s an important fact that’s easy to forget: our data is only as helpful as it is understandable. Most of the time, making data understandable means creating some kind of data visualization. And while a simple bar graph might cut it for internal work, making your data both visually understandable and visually attractive can help […]

Read More

Python Dictionary Tutorial: Analyze Craft Beer with Dictionaries

Learn to use Python dictionaries to store, sort, and access data in this in-depth tutorial analyzing craft beer data to master dictionary techniques.

Read More

Tutorial: Understanding Regression Error Metrics in Python

Error metrics are short and useful summaries of the quality of our data. We dive into four common regression metrics and discuss their use cases.

Read More

Tutorial: Basic Statistics in Python — Probability

Explore statistics for data science by learning probability is, normal distributions, and the z-score — all within the context of analyzing wine data.

Read More

Tutorial: Basic Statistics in Python — Descriptive Statistics

Learn how to do descriptive statistics in Python with this in-depth tutorial that covers the basics (mean, median, and mode) and more advanced topics.

Read More

Python Generators

Python generators are a powerful, but misunderstood tool. They’re often treated as too difficult a concept for beginning programmers to learn — creating the illusion that beginners should hold off on learning generators until they are ready. I think this assessment is unfair, and that you can use generators sooner than you think. In this […]

Read More

Using Linear Regression for Predictive Modeling in R

In R programming, predictive models are extremely useful for forecasting future outcomes and estimating metrics that are impractical to measure. For example, data scientists could use predictive models to forecast crop yields based on rainfall and temperature, or to determine whether patients with certain traits are more likely to react badly to a new medication. […]

Read More