Data Science is on a constant upswing — data-driven organisations and IT industries are leveraging Machine Learning and Big Data analytics tools to derive actionable business insights and strengthen their decision-making process.
The market size of data science solutions was valued at USD 95.3 billion in 2021, and it is predicted to reach USD 322.9 billion in 2026, rising at a CAGR of 27.7%.
While the use of data science platforms is rocketing exponentially, you can be hard-pressed to find the best open source data science tools that can offer the best bang for the buck.
Unpack our guide to know about open source Data Science systems.
What is Data Science?
Data Science is a multi-disciplinary strategy for discovering, extracting, and surfacing hidden trends and patterns in raw data through fusing analytical approaches, domain expertise, and top-notch technologies.
The domains it incorporates to help data scientists streamline analysis of large-scale data sets and transform them into optimal outcomes include:
- Machine learning
- Deep learning
- Artificial intelligence
- Descriptive statistical analysis
- Predictive analysis
- Natural language processing
- Optimization
- Forecasting
- Interfacing
- Informatics and more
Importance of Data Science
- Data Science allows business and IT executives to make decisions based on data rather than mere intuitions. It makes measuring, monitoring, and recording performance metrics effortless. With data science, consolidating unstructured, structured, and scattered data from multiple channels for analysis has ruled out the condition of taking high-stake risks — experts can build predictive models leveraging data to channelise their sales and marketing efforts accurately.
- By driving actions based on data trends, these platforms help define industry goals, boost performance, and maintain a lasting relationship with customers that, in turn, keep revenue rolling in the business.
- Top-end data science systems have tools for all teams of an organisation across an entire analytics lifecycle. These solutions allow staff members to collaborate within a single centralised environment for improved performance.
What are Open Source Data Science Tools?
An open-source data science platform is software developed in a collaborative public manner that is released under a licence where the copyright holder makes the source code readily available for download and lets anyone do end-to-end data analytics right out of the box.
They can be free or paid, are mostly cross-platform, and allow developers to learn from code bases, reuse or enhance them and eventually contribute to them without being tied down by expensive licences!
Best Open Source Data Science Tools
Weka — best for Data Mining and Transformation
Developed by the University of Waikato and released under the GNU General Public Licence, Weka is a Java-based data mining workbench with a bunch of tools for efficient data pre-processing, regression, classification, and clustering, association rules mining, workflow, and interactive visualisation.
For anyone trying to make a smooth transition into Machine Learning and Data Science, this open-source beginner-friendly tool, with an intuitive GUI, can be great for gaining hands-on experience with ML algorithms and starting out with predictive modelling and analytics — no technical flair or prior coding background is required.
Pros
- Uses Java Database Connectivity to provide access to SQL database
- Cross-platform compatibility (Mac, Windows, and Linux)
- WEKA components: Explorar (the interface to execute all data pre-processing and ML tasks), Experimenter (allows developing and executing your custom experiments and comparing the performance of algorithms), Knowledge Flow (Weka’s component-based data flow interface), Workbench (integrates all GUI into a single environment)
Con
- Cannot execute multi-relational data mining
KNIME Analytics Platform — Best for Data Analysis
KNIME is an enterprise-ready open-source data analytics solution with tools for a complete data science lifecycle — from end-to-end data pre-processing, integration, and model building and validation to interactive data visualisation and reporting.
So you can, with few clicks and no code, model each stage of the analytics cycle from a single centralised workflow, fully govern data flow, and ensure your analysis is always top-notch and current.
Pros
- Parallel execution on multi-core systems
- Supports drag-and-drop intuitive GUI to help build visual workflow with no coding.
- Seamlessly integrates data from an array of external data warehousing systems and databases like Microsoft SQL Server, Snowflow, Hive, Snowflake, etc.
- KNIME supports in-database processing; plus maximises processing performance and efficiency by allowing distributed processing on Apache Spark
Cons
- Execution in other coding languages is slow
- Cumbersome user interface
ML Flow — Best for Model Deployment
MLflow is a robust open-source system specialised in helping manage the end-to-end Machine Learning lifecycle. It acts as a centralised model registry and offers executives to effortlessly and linearly deploy ML models within their organisations by streamlining the model experimentation, reproduction, deployment, retraining, and validation process in application development.
Pros
- Supports well-documented and consistent RESP APIs, R, Python, and Java
- One-click project reproduction and model deployment functionality — no environment configuration or code rewriting needed
- Seamlessly integrable with a spectrum of external software -— Keras, Java, TensorFlow, Python, Databricks, and many more!
Cons
- No data and notebook versioning
- No resource monitoring
D3.js — Best for Data Visualisation
D3.js is a JavaScript-based open-source library framework that manipulates documents based on dynamic data and produces interactive and customisable visualisations in web browsers leveraging Scalable Vector Graphics (SVG), HTML, and Cascading Style Sheets (CSS).
It’s a modular framework that ties graphical components and arbitrary data to a Document Object Model (DOM) and allows modifications by letting you employ data-focused transformations.
Pros
- D3.js allows code reusability.
- Supports manipulating large-scale datasets
- Works on static data of various formats — Objects, Arrays, CSV, XML, and JSON
- Gives complete control over visualisation functionality with a wide range of options — geospatial mapping, graphs, pie charts, Gann charts, bar charts, and more
Cons
- data-source limitations
- Steeper learning curve
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