Math is relevant to software engineering but it is often overshadowed by all of the exciting tools and technologies. In the field of data science, however, being familiar with linear algebra and statistics is very important to statistical analysis and prediction. In this tutorial, we’ll use SciPy and NumPy to learn some of the fundamentals of linear algebra and statistics. Getting Started We’ll be using Python to show how… Read More
One of my all-time favorite Facebook groups is “DogSpotting.” For those of you unfamiliar with this revolutionary group, it’s a Facebook group dedicated to posting pictures of random dogs you see as you go along your regular day. There are tons of “spotting” rules, but any way you slice it, this group is awesome. Using this model for inspiration, I built a Slack bot for a… Read More
Data Visualization is an art form. Whether it be a simple line graph or complex objects like wordclouds or sunbursts, there are countless tools across different programming languages and platforms. The field of geospatial analysis is no exception. In this tutorial we’ll build a map visualization of the United States Electoral College using Python’s plotly module and a Jupyter Notebook. Python Visualization Environment Setup This guide was… Read More
Sentiment analysis uses computational tools to determine the emotional tone behind words. Python has a bunch of handy libraries for statistics and machine learning so in this post we’ll use Scikit-learn to learn how to add sentiment analysis to our applications. Sentiment Analysis isn’t a new concept. There are thousands of labeled datasets out there, labels varying from simple positive and negative to more complex systems… Read More
While not all data science relies on statistics, a lot of the exciting topics like machine learning or analysis relies on statistical concepts. In this tutorial, we’ll learn how to calculate introductory statistics in Python. What is Statistics? Statistics is a discipline that uses data to support claims about populations. These “populations” are what we refer to as “distributions.” Most statistical analysis is based on probability,… Read More
If you plant me anywhere in Manhattan, I can confidently tell you where the nearest bubble tea place is located. This may be because I have a lot of them memorized, but for the times my memory betrays me, luckily I have the boba map on my data blog. In this tutorial, we’ll use a combination of Python, the Google Maps API, and geojsonio to create what… Read More
Sentiment analysis uses computational tools to determine the emotional tone behind words. This approach can be important because it allows you to gain an understanding of the attitudes, opinions, and emotions of the people in your data. At a higher level, sentiment analysis involves natural language processing and artificial intelligence by taking the text element, transforming it into a format that a machine can read, and… Read More
I’m going to give you a little bit of a spoiler alert: I’ve read the equivalent of about 14 books this past year. Now I’m not a cover-to-cover novel reading person — I consume most of my content in the form of articles and tutorials. So while I’m feverishly reading all the time I never have a sense of how much I’m actually reading. After all… Read More
As a native New Yorker, I would be a mess without Google Maps every single time I go anywhere outside the city. We take products like Google Maps for granted, but they’re an important convenience. Products like Google or Apple Maps are built on foundations of geospatial technology. At the center of these technologies are locations, their interactions and roles in a greater ecosystem of location services…. Read More