Datalore

Explore the essential Python tools and libraries for portfolio optimization, get a walk through the process of calculating fundamental portfolio metrics such as lognormal returns and Sharpe ratios, and learn how to implement an established portfolio optimization strategy – mean-variance optimization.

Learn about the difference between the typical use cases of R and Python for data science. Discover the tasks most suited to the languages, as well as how to use them in Datalore.

Discover the similarities and differences between R and Python. Programming styles, data visualization and libraries for each programming language are covered.

Greetings from the Datalore team! Jupyter notebooks can get messy. Perhaps you have tried different things in one notebook, or maybe you have chunks of outdated code and variable declarations all over the place. This isn’t necessarily a sign that something is wrong. Rather, the tool was designed …

Jupyter notebooks are arguably the most widely used tool in data science. However, when it comes to collaboration, resource management, and security...

Last year presented an unexpected challenge for the world, one we all had to rise to. In 2020, we managed to launch Datalore Professional and deliver lots of great new features for our users. Here we’ve assembled a description of some of the most notable ones. Datalore Professional In November we l…

Though this is our final blog post of 2020, we have a bunch of new Datalore updates we’d like to share, including the results of two major studies we recently conducted.

Here’s how we used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science.