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Top 15 Open-Source Data Science Tools to Learn (and Use) in 2023

4 minute read | October 11, 2021
Sakshi Gupta

Written by:
Sakshi Gupta

Data science is on a continued upswing—both in terms of career opportunities as well as in the ways that organizations, across industries, are making use of it. Everyone from HDFC bank and Flipkart to the government of India is leveraging data science platforms, methods, and techniques. Clearly, there’s no time like the present to build a data science career. And a great way to start is by developing skills in a few data science tools.

Our Springboard experts recommend the top 15 data science tools to learn this year. Don’t rush to learn them all. Work on becoming conversant in as many as you can, but get hands-on experience in one or two by experimenting with them on your data science projects.

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Top Open-Source Data Science Tools

Data Mining and Transformation

Strictly speaking, data mining is about identifying patterns in large datasets. But in practice, it has come to include extraction, collection, storage and analysis of information. There are tools that can do one or more of these tasks. Our top three are:

  1. Weka is a popular tool used for data mining, pre-processing and classifying data. Weka’s GUI simplifies classification, association, regression, and clustering, providing statistically robust results. 
  2. Scrapy is best for writing web spiders that will crawl websites and extract data (you know, scraping). Written in Python, Scrapy is fast and powerful. CareerBuilder uses Scrapy to collect data on job offers across multiple sites.
  3. Pandas is a popular data wrangling software, also written in Python. It is perfect for working with numerical tables and time-series data. It provides flexible data structures that make data manipulation easy. It is the backbone of recommendation engines from Netflix and Spotify. 

Data Analysis and Big Data Tools

Once the data has been collected and processed, it’s time for analysis. Here, you need a tool to get the data ready for model training and refining predictions. Some of the best ones are:

  1. KNIME, or Konstanz Information Miner, in full, provides end-to-end data analysis, and integration and reporting. Its GUI allows users to perform pre-processing, analysis, model building and visualization with minimal programming.
  2. Hadoop is a software framework primarily used for storage and processing of big data on a distributed model. This allows the data to be processed faster, and any hardware failures to be handled better.
  3. Spark from Apache is an analytics engine for big data. With Spark, you can run large-scale workloads of peta-bytes of data and build applications faster, and deploy them comfortably across virtual machines, containers, on-prem or on the cloud.
  4. Neo4J is a graph database management platform, and the most popular one at that. Unlike a relational database, graph databases store connections along with the data, and Neo4J helps users detect hard-to-find patterns on such data.

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Model Deployment

One of the key purposes of data science is in developing machine learning models on the data. These models can be logical, geometric or probabilistic models. Here are some tools you can use for model-building.

  1. TensorFlow.js is the JavaScript edition of the popular machine learning framework, TensorFlow. You can develop models in JavaScript or Node.js and use TensorFlow.js to deploy them over the web on the client browser.
  2. MLFlow is a machine learning lifecycle management platform — from building and packaging to deploying models. If you’re experimenting with multiple tools or building several models, MLFlow helps manage all of it from one place. You can integrate library, language or algorithm with the product.

Data Visualization

Data visualization needs to be more than just a visual representation of data. Today, it needs to be scientific, visual and more importantly insightful. In that, it should go beyond reporting; it must present analytical reasoning through interactive visual interfaces. Here are some tools that can help visualize your data science projects.

  1. Orange is an easy to use data visualization tool with a large toolkit. In spite of being a GUI-based beginner-friendly tool, you mustn’t mistake it for a light-weight one. It can do statistical distributions and box plots as well as decision trees, hierarchical clustering and linear projections.
  2. With D3.js or Data-Driven Documents (D3), you can visualize data on web browsers using HTML, SVG and CSS. It is popular with data scientists for its capabilities in animation and interactive visuals.
  3. Ggplot2 helps data scientists create aesthetically pleasing and elegant visualizations, using R. So next time you want to really wow your audience, you know which library to choose for creating your visuals!

Development Environments

Like most programming, writing and deploying data science code can also be done more efficiently with an integrated development environment. IDEs offer code insights, test your code, help you identify errors easily, and even allow you to run your code directly with plugins. Here are some IDEs especially for data science-related code.

  1. Jupyter Notebooks is a web application that can host code, data, notes, equations, etc. — in other words, an interactive online document. If you’re working on a project with other data scientists, Jupyter Notebooks is the perfect collaboration tool!
  2. Zeppelin Notebooks is a web-based environment where you can perform data analysis using many languages like Python, SQL, Scala etc. You can explore, share, analyze, and visualize data with Zeppelin Notebooks.
  3. R Studio’s biggest attraction is that it integrates R-based tools into a single environment. You can write clean code, execute it, manage workflows and even debug it with R Studio.

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.