7 Powerful Open Source Tools For Your Data Projects
These powerful open source tools for data projects will make your work that much more seamless and functional. Here's what is recommended.
Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed.
Here are some open-source options to consider.
Ludwig is a tool that allows people to build data-based deep learning models to make predictions. You don’t even need coding knowledge to get started with it. Besides enabling you to train data sets for machine learning purposes, it has a visualization component that could bring your data to life and make it more interpretable by people who aren’t data professionals but need to make sense of the information.
Ludwig is a TensorFlow-based toolbox that aims to allow people to use machine learning during their data work without having extensive prior knowledge. Some examples of the projects you could undertake with help from Ludwig include text or image classification, machine-based language translation and sentiment analysis.
Differential privacy takes a cryptographic approach to data science by mixing user data with artificial “white noise.” Doing this protects the privacy of the people involved by ensuring that a malicious person could not trace a data source back to a single individual or otherwise reveal their identity. In September 2019, Google decided to make it’s Differential Privacy Library available as an open-source tool.
By making that decision, the company hoped it would help businesses keep data safe even if they didn’t have the privacy-boosting resources that a mega enterprise might have. When Google talked about releasing this tool in its blog, the brand pointed out that if you don’t protect user data, you risk losing people’s trust.
Kubernetes is an application management and deployment platform that allows working with applications in a container environment. It can assist with things like load balancing and keeping your applications up and running as expected during fluctuating conditions. One thing that makes Kubernetes so stable is the fact that it uses API Contracts. They’re pluggable components that make Kubernetes conform to standards.
As long as two modules both conform to the same set of standards, you can swap them out, and due to the shared characteristics of the modules, this aspect of Kubernetes can shorten your integration testing process.
It may not immediately seem like Kubernetes is a good fit for your data science projects, but you shouldn’t overlook it. Kubernetes streamlines many aspects of application management, and it can do the same for your data science projects.
One of the things it can assist with is repeatable batch jobs. For example, if you’re trying to work with data in reproducible ways, sticking with the same process is crucial. Also, you don’t have to become a Kubernetes expert to use it for data science. It’s a powerful framework that you can apply whether you’re creating machine learning algorithms to work with data or want to use analytics to solve business problems.
4. Apache Drill
If you’re ready to start querying data without dealing with so much overhead, Apache Drill is for you. It removes the need to load the data, maintain schemas or transform the data before performing queries. Users only need to include the respective path in the SQL query to get to work. In addition to supporting standard SQL, Apache Drill lets you keep depending on business intelligence tools you may already use, such as Qlik and Tableau.
Also, no matter your current skill level with big data analysis, Apache Drill tries to remove some of the obstacles that people often face. It allows secure and interactive SQL analytics at the petabyte scale.
Plus, if your company has only started working with data and cannot make a significant investment in data analytics yet, that’s no problem. Apache Drill provides the resources for one person or a small team to use. In short, it makes big data analysis more accessible.
ParaView got developed to analyze huge datasets, and it even works on supercomputers. But, that doesn’t mean you can’t use it on an ordinary workplace laptop. Paraview helps you analyze your data with qualitative or quantitative techniques, then get another perspective on it with visualizations. That’s particularly helpful if you need to prepare the data and then display it in a way that’s easy for people to digest.
And, if you need a little guidance to get started and feel comfortable using the tool, free online tutorials exist to help you get your bearings. The official ParaView site includes a community support section, as well.
Sometimes a data project is most effective if people can interact with the data. This graphing library is ideal if you’re at the point where you want to transform your data into an interactive graph.
It offers numerous styles to consider, ranging from bar charts to heatmaps. The website breaks down the types of charts into categories. For example, there are financial charts, which could work well when showing year-end reports.
Alternatively, Plotly offers geographical maps. You might find that one of those aligns with a data science project that shows in which neighborhoods your business obtained the most new customers over the past year or discover that the map works particularly well for showing the routes taken by members of your sales team who are on the road often.
The Jamovi website says this tool wants to bridge the gap between researchers and statisticians. It works like a fully functional spreadsheet, which means there is not a large learning curve to navigate when starting to use it.
Also, if you’re not strong in statistics yet, no problem — let Jamovi act as your introductory tool. There is also a suite of analyses to help you start to explore immediately after completing your download and installing the product.
Tools to Help Your Data Science Projects Excel
Having the necessary tools is crucial for helping your data science projects succeed instead of falter. These seven open-source options are enough to get you started, and they’ll likely highlight new and practical ways to utilize your company’s information.