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Data science is a trending topic that every industry is aware of. As a data scientist, your primary job is to extract meaningful insights from data. But here’s the downside: with data exploding at an exponential rate, it’s more challenging than ever. You will often feel like you are finding the needle in a digital haystack. This is where data science tools emerge as our saviors. They help you extract, clean, organize and visualize data to extract meaningful insights from it. Now, let’s address the real problem. With the abundance of data science tools, how will you navigate finding the right ones? The answer to this question is found in this article. Through a careful combination of personal experience, invaluable community feedback, and the pulse of the data-driven world, I’ve curated a list that packs a punch. I have focused solely on open source data science tools due to their cost-effectiveness, agility, and transparency.
Without further delay, let’s explore the top 10 open source data science tools you should have in your arsenal this year:
KNIME is a free and open source tool that empowers both data science beginners and seasoned professionals by opening the door to effortless data analysis, visualization, and deployment. It’s a canvas that transforms your data into actionable insights with minimal programming. It is a beacon of simplicity and power. You should consider using Knime for the following reasons:
- GUI-based data preprocessing and pipeline allow users from various technical backgrounds to perform complex tasks without many problems
- It allows seamless integration into your current workflows and systems
- KNIME’s modular approach allows users customize your workflows according to your need
Weka is a classic open source tool that allows data scientists to preprocess data, build and test machine learning models, and visualize data using a GUI interface. Although it is quite old, it is still relevant in 2023 due to its adaptability to address model challenges. It provides support for multiple languages including R, Python, Spark, scikit-learn, etc. It is extremely useful and reliable. Here are some of the Weka outshining features:
- Not only is it suitable for data science professionals, but it is also a great platform for teaching machine learning concepts. providing educational value.
- It allows achieve sustainability effortlessly reducing data pipeline downtime, resulting in reduced carbon emissions.
- Delivery mind-blowing performance by providing support for high I/O, low latency, small files, and mixed workloads without tuning.
apache spark is a well-known data science tool that offers real-time data analysis. It is the most widely used engine for scalable computing. I have mentioned it because of its ultra-fast data processing capabilities. You can easily connect to different data sources without worrying about where your data is located. While impressive, it’s not all sunshine and rainbows. Due to its speed, it needs a good amount of memory. Here’s why you should choose Spark:
- Is Easy to use and offers a simple programming model that allows you to create applications using the languages you are already familiar with.
- You can get a unified processing engine for your workloads.
- It’s a one stop shop for batch processing, real-time updates, and machine learning.
fast miner It stands out for its comprehensive character. It is your true companion throughout the entire data science lifecycle. From data modeling and analysis to data deployment and monitoring, this tool covers it all. It offers a visual workflow layout, eliminating the need for complex coding. This tool can also be used to create custom data science algorithms and workflows from scratch. RapidMiner’s extensive data preparation capabilities allow you to deliver the most refined version of data for modeling. Here are some of the key features:
- Simplifies the data science process by providing a visual and intuitive interface.
- RapidMiner connectors make effortless data integrationregardless of size or format.
Neo4j Graph Data Science is a solution that analyzes complex relationships between data to uncover hidden connections. It goes beyond rows and columns to identify how data points interact with each other. It consists of pre-configured graph algorithms and automated procedures designed specifically for data scientists to quickly demonstrate the value of graph analysis. It is particularly useful for social network analysis, recommender systems, and other scenarios where connections are important. Here are some of the additional benefits it provides:
- Improved predictions with a rich catalog of more than 65 graph algorithms.
- It allows seamless data ecosystem integration using more than 30 connectors and extensions.
- Its powerful tools allow quick implementation allowing you to quickly release workflows into the production environment.
glot2 is an amazing data visualization package in R. It turns your data into a visual masterpiece. It is based on the grammar of graphics and offers a playing field for customization. Even the default colors and aesthetics are much nicer. ggplot2 uses the layered approach to add details to your images. While you can turn your data into a beautiful story waiting to be told, it’s important to recognize that dealing with complex figures can lead to cumbersome syntax. Here’s why you should consider using it:
- The ability to save plots as objects allows you to create different versions of the plot without repeating a lot of code.
- Instead of juggling between multiple platforms, ggplot2 provides a unified solution.
- Many useful resources and extensive documentation to help you get started.
D3 is short for data-driven documents. It is a powerful open source JavaScript library that allows you to create stunning images employing DOM manipulation techniques. Create interactive visualizations that respond to changes in data. However, it has a steep learning curve specifically for those who are new to JavaScript. Although its complexity can be a challenge, the rewards it offers are invaluable. Some of them are listed below:
- Offers personalization providing a large number of modules and APIs.
- Is light and does not affect the performance of your web application.
- Works well with current web standards and can easily integrate with other libraries.
Metabase is a drag-and-drop data exploration tool that can be accessed by both technical and non-technical users. Simplifies the data analysis and visualization process. Its intuitive interface allows you to create interactive dashboards, reports and visualizations. It is becoming extremely popular among businesses. It provides several other benefits listed below:
- Replaces the need for complex SQL queries with queries in simple language.
- Collaboration support by allowing users to share their ideas and findings with others.
- Supports more than 20 data sources, allowing users to connect to databases, spreadsheets and APIs.
High expectations is a data quality tool that allows you to perform checks on your data and detect any breaches effectively. As the name suggests, you define some expectations or rules for your data and then monitor your data against those expectations. It allows data scientists to have more confidence in their data. It also provides data profiling tools to speed up data discovery. The key strengths of Great Expectations are:
- Generate detailed documentation for your data that is beneficial for both technical and non-technical users.
- Seamless integration with different data channels and workflows.
- It allows automated tests to detect any problems or deviations earlier in the process
PostHog is open source primarily in the product analytics landscape that allows companies to track user behavior to improve product experience. It allows data scientists and engineers to get to data much faster, eliminating the need to write SQL queries. It is a complete product analytics suite with features like dashboards, trend analysis, funnels, session recording, and much more. These are the key aspects of PostHog:
- Provides an experimentation platform to data scientists through its A/B testing capabilities.
- It allows seamless integration with data warehouses for both importing and exporting data.
- Provides a Deep understanding of user interaction with the product. capturing session replays, console logs and network monitoring
One thing I would like to mention is that as we advance further in the field of data science, these tools are no longer mere options, but have become the catalyst that guides you towards informed decisions. So, don’t hesitate to dive into these tools and experiment as much as you can. As I wrap up, I’m curious: are there any tools you’ve found or used that you’d like to add to this list? Feel free to share your thoughts and recommendations in the comments below.
Kanwal Mehreen is an aspiring software developer with a strong interest in data science and ai applications in medicine. Kanwal was selected as a Google Generation Scholar 2022 for the APAC region. Kanwal loves sharing technical knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.
