The size of enterprise data is rocketing exponentially, making data science the most talked-about and in-demand technology in the IT and corporate environment.
Thanks to various high-end data science tools and frameworks that have become the holy grail of advanced data-based businesses, helping them chart out and automate data analysis to turn raw data into tangible business value.
Here’s a rundown of the best tools and frameworks in data science that facilitate the data gleaning process and drive your business forward.
What are Data Science Tools and Frameworks?
Data Science tools are a suite of packages, instructions, and techniques that run on user computers and help data scientists automate data extraction, mining, cleansing, and preparation to optimise the data analysis process and extract actionable insights from voluminous data sets.
A data science framework is an already-developed software containing built-in libraries with code reusability and can automate data mining functionality. The aim is to facilitate data processing, machine learning, or deep learning jobs. So, without reinventing the wheel of creating ML algorithms, data experts can better focus on driving business revenue, extracting meaningful insights from massive troves of raw data with ease and in less time.
Benefits of Data Science Frameworks
- Frameworks Make Optimised Code: Frameworks enable programmers to skip code duplication or error. The result is that they can focus more on analytics than entangling with writing, testing, or debugging to optimise a program.
- Frameworks are Pre-Optimised and Pre-Tested: With pre-tested, pre-optimised programming frameworks, data experts don’t have to work on building a convoluted Big Data infrastructure from the ground up. It significantly saves time as they don’t have to carry on unit testing or codebase maintenance tasks.
- Speedier Implementation: Applying well-documented, optimised, and pre-verified code significantly reduces the implementation runway — experts can better focus on exploring, tweaking, and optimising ML models rather than struggling with designing and validating them.
Best Data Science Frameworks and Tools
Let’s dive deeper into our well-researched list of the best Data Science tools and frameworks:
TensorFlow
Developed at Google, TensorFlow is an open-source end-to-end python-based ML tool with an extensive ecosystem of libraries and packages to help design deep learning neural networks. It’s an interactive multiplatform programming interface that is scalable and enables data experts to design and deploy ML-powered data-based applications and models using intuitive high-level APIs.
Pros
- Supports top-notch data visualisation via interactive graphs
- Employes multiple backend tools (GPUs, ASIC), etc., and is highly parallel — utilises CPU and GPU systems for functioning.
- Compatible with other languages like JavaScript, C++, and Python
- TensorFlow architecture is built on TPU for effortless model deployment on the cloud and maximum computation speed and performance.
- Supports effortless node debugging with TensorBoard
- Keras compatible
Cons
- Not compatible with Windows OS
- No support for OpenCL.
Apache MXNET
MXNET is an open-source deep-learning library best for research prototyping and production. From promptly defining, training, or deploying complex multi-layered neural networks on cloud infrastructure or mobile devices to pushing your production workload, MXNet can cater to all your requirements.
Pros
- Supports libraries and power-packed toolkits for natural language processing (NPL), computer vision, time series, etc.
- The imperative symbolic Gluon Python API makes model prototyping, training, and deployment effortless while not compromising performance and training speed.
- Seamlessly integrable with Python, Scala, Java, JavaScript, and Clojure.
Cons
- Smaller open-source community
Apache Hadoop
Owned by Apache Software Foundation and released under Apache licence 2.0, Hadoop is an open-source Java-based library framework best for data storage and executing distributed processing on massive troves of data sets across clusters of computers employing simple programming models.
The Hadoop ecosystem consists of three main elements:
- Hadoop Distributed File System (HDFS): File storage system in Hadoop
- Map-Reduce: Processing layer that executes high-level distributed data processing
- YARN: Allocates tasks and handles resource distribution
Pros
- Supports horizontal scaling/scaling out (adding any number of nodes) and vertical scaling/scaling up (increasing the processing power of the nodes)
- Compatible with a spectrum of data sets —structured (MySql Data), Semi-structured (XML, JSON), un-structured (images and videos)
- Parallel processing across all nodes in a cluster maximises speed and performance.
- Hadoop architecture is fault-tolerant.
Cons
- Supports only batch processing
- Real-time data processing capability and security should be improved.
- using Hadoop for smaller data sets limits its performance
D3.js
D3.j3 is a JavaScript-based open-source framework and library that manipulates documents based on data and allows data science experts to create interactive and dynamic data visualisations in web browsers with a skinny amount of code.
Fusing the data and graphical components to the DOM (Document Object Model), this modular framework allows users to apply DOM manipulation techniques to make data-driven modifications to the documents.
To enable custom data visualisation, D3.js exposes the maximum capabilities of web standards – HTML5, Scalable Vector Graphics (SVG), and Cascading Style Sheets (CSS), rather than applying its own graphic vocabulary.
Pros
- Supports high-level quantitative analysis, animation, annotation, interaction
- Offers 1000+ data visualisation techniques and 30+ modules right out of the box to let interactively visualise a large dataset
- supports data preprocessing even if you use another library
Cons
- steeper learning curve
- Statistical analysis not supported
DeepLearning4j
Developed by Eclipse, DeepLearning4j is the first open-source Java-based enterprise-grade deep-learning framework for JVM that facilitates designing, training, and deploying neural networks. Being distributed in nature, DL4j can significantly speed up training by leveraging multi-CPUs.
Pros
- Compatible with Clojure, Kotlin, and Scala
- Seamlessly integrates with Apache Spark and Apache Hadoop
- Distributed GPU and CPU support
- CNNs, DBNs, RBMs, and RNNs supported
Cons
- Not suitable for large-scale projects
- the neural network output function is not synchronised
RapidMiner
RapidMiner is the enterprise-ready end-to-end data science tool that enables data-driven businesses to drive transformation and gain a breakthrough competitive edge by fully augmenting and automating data preparation, model development, testing, and deployment processes.
Using a code-free interface for developing big data integrations and workflows, RapidMiner supports all teams across the entire analytics lifecycle.
Pros
- Allows building ML models through visual, code-based, or automated systems
- Supports on-demand or managed cloud deployment.
- Maximises performance and analytics speed by executing ETL and data preparation in-database
- Top-notch GUI capabilities
Cons
- The commercial version is expensive
- Limited features available with the free edition
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