Take the first step into image analysis in Python by using k-means clustering to analyze the dominant colors in an image in this free data science tutorial.
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.
At Dataquest, we strongly advocate portfolio projects as a means of getting a first data science job. In this blog post, we’ll walk you through an example portfolio project. The project is part of our Statistics Intermediate: Averages and Variability course, and it assumes familiarity with: Sampling (populations, samples, sample representativity) Frequency distributions Box plots […]
At Dataquest, we strongly advocate portfolio projects as a means of getting your first data science job. In this blog post, we’ll walk you through an example portfolio project. The project is part of our Statistics Fundamentals course, and it assumes some familiarity with: Sampling (simple random sampling, populations, samples, parameters, statistics) Variables Frequency distributions […]
Getting into Machine Learning and AI is not an easy task, but is a critical part of data science programs. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial that we […]
Employers usually give a lot of weight to a candidate’s portfolio when hiring for a junior data science role. Although you may be capable of technically impressive projects, your job hunt will suffer if you don’t pay enough attention to the stylistic aspects as well. A busy employer is not going to review poorly constructed […]
Ed Hawkins, a climate scientist, tweeted the following animated visualization in 2017 and captivated the world: This visualization shows the deviations from the average temperature between 1850 and 1900. It was reshared millions of times over Twitter and Facebook and a version of it was even shown at the opening ceremony for the Rio Olympics. […]
Editor’s note: This post was written as part of a collaboration with Enigma, a public data company. Author India Kerle is a data curator at Enigma. There are a canon of open datasets used widely in data science projects — you’ve likely come across something making use of the Iris Flower classic or New York’s […]
Editor’s note: This post was written as part of a collaboration with data.world, a site for sharing and hosting data. Authors Shannon Peifer and Gabriela Swider are on the data.world team. Finding the right data can be difficult. And even once you have it, how do you collaborate with others to make sense of it? […]
In previous blog posts, we have described the Postgres database and ways to interact with it using Python. Those posts provided the basics, but if you want to work with databases in production systems, then it is necessary to know how to make your queries faster and more efficient. To understand what efficiency means in […]
When working with data, a key part of your workflow is finding and importing data sets. Being able to quickly locate data, understand it and combine it with other sources can be difficult. One tool to help with this is data.world, where you can search for, copy, analyze, and download data sets. In addition, you […]
This is the fifth post in a series of posts on how to build a Data Science Portfolio. You can find links to the other individual posts in this series at the bottom of the post. If you’ve ever worked on a personal data science project, you’ve probably spent a lot of time browsing the […]
I moved from Boston to the Bay Area a few months ago. Priya (my girlfriend) and I heard all sorts of horror stories about the rental market. The fact that searching for “How to find an apartment in San Francisco” on Google yields dozens of pages of advice is a good indicator that apartment hunting […]