Data Tools We're Thankful For

Thanksgiving| Data Science Basics| Tools| Open Source | | Claire Carroll
It’s that time of year, when we take stock of all that we’re grateful for in our lives. Since we know that concretely expressing gratitude can make you happier, we’ve compiled a list of the top four data tools we’re thankful for. These tools make data science easier, more beautiful, and faster, thus improving our lives every day. Many of these tools are open source too, and often serve many more users than contribute, so consider contributing time to their success if you’re thankful.gratitude

4. Regular Expressions

We think regular expressions are a bit underrated, since they enable precise language parsing. These patterns can be hyper specific—replacing “apple” with “banana”— or can be a bit more variable, such that they would return any word that starts with ‘a’ and ends with ‘e.’ Without them, finding specific phrases or text constructions (like emails or dates) in large data sets would be very laborious. There are many supporting text editors that make testing and debugging your regular expressions a breeze.

3. Seaborn Plotting in Python

Seaborn is a Python visualization library that enables everything from single plots to multi-plot grids. Now in version 0.9.0, the Seaborn library recognizes that one size doesn’t fit all when it comes to graphically understanding your data. It’s based on matplotlib and closely integrated with pandas data structures. When you try to use matplotlib directly, you need to translate each variable into specific parameters of the visualization model, however Seaborn Plotting eliminates this hassle. The library seeks to make visualization a key part in understanding and exploring data, rather than a summary step at the end of the analysis process. 


seaborn plotting

2. R Studio

This is one of our favorite tools when we’re working in R. R Studio is an integrated development environment (IDE) that was made especially for R, but can incorporate HTML, PDFs, word documents, and slide shows too. There’s a smooth organizational system you can customize to handle multiple working directories. The editor offers syntax highlighting, code completion, and smart indentation. If that wasn’t enough, the workspace manager enables you to execute code directly from the editor. There is an R documentation resource and assistance integrated into the IDE so if you forget some specific syntax, R Studio can guide you through it. There’s also serious debugging support to help diagnose and address errors quickly. R Studio is customizable too, and comes in open source and paid commercial editions.

1. Dataiku DSS

We’re thankful for the platform that helps us do it all. This end-to-end platform can get you from data cleaning and governance basics to robust AI modeling and predictive analytics for your business needs. Quick analysis is available in minutes, with the ability to gather specific deeper insights with visual recipes. Fast and effective machine and deep learning is possible with prebuilt models, or user-specific macros and plugins, even if you lack robust coding skills. Dataiku enables you to collaborate on real-time datasets and smart models to improve the quality of data-driven information at your disposal. In addition, Dataiku integrates seamlessly with the other data tools on our list.

What Other Tools do you need?

These tools all make the data science landscape a better place to be. If you want to learn more, check out our guidebook on why teams need data science tools.



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