JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area. Check out this very cool project showing that Deep Convolutional Neural Nets aren't nearly as intimidating as they sound by implementing one in a simple spreadsheet. What do AI-savvy companies do differently?

This article changed the way I think about reporting. Most data scientists don't look forward to reporting but when done right, it can be used as a means to create strong stakeholder relationships in your organization. The need for data scientists is far outpacing the availability of people in the industry. In order to stay competitive, companies should focus on differentiating themselves in specific areas. AI and machine learning will become ubiquitous and woven into the fabric of society.

This post walks through a complete example illustrating an essential data science building block: the underfitting vs. overfitting problem. The author explores the problem through a beginner's implementation of cross-validation. The wide growth of deep learning has complicated things a bit in the hardware department. This post will walk through the different types of computer chips, where they're available, and which ones are the best to boost your performance. One of the most common problems in data science is that of dealing with missing values.

Stack Overflow uses A/B testing to compare a new version to a baseline for a design, a machine learning model, or practically any feature; these tests are part of the decision-making process. Which version of a button, predictive model, or ad is better? We don't have to guess blindly, but instead we can use tests as part of our decision-making toolkit. Hiring a data scientist can be a tricky process. The actual definition of "Data Scientist" is vague, the day-to-day job of someone with "Data Scientist" in their job title varies dramatically between organizations, and people come to the field from a wide variety of backgrounds.

A nearly exhaustive collection of all the different ways that we can visualize data, from bubble charts to histograms. You'll definitely want to bookmark this for future reference when deciding how to represent your insights. K-Means Clustering, one of the popular clustering algorithms is a type of unsupervised learning that is often used when you don't have labeled data. In this post, the author walks through implementing K-Means in Python from scratch. Food for thought when your preparing for your next presentation.