Data Science Tools

This ability to extract insights from enormous sets of structured and unstructured data has revolutionized a wide range of fields, from agriculture to astronomy to marketing and medicine. Today, businesses, government, academic researchers and many others rely on it to tackle complex tasks that push beyond the limits of human capabilities. Data science is increasingly paired with Machine Learning (ML) and other Artificial Intelligence (AI) tools to ratchet up insights and drive efficiency gains. For example, it can aid in predictive analytics, making Internet of Things (IoT) data actionable, developing and modeling new products, spotting problems or anomalies during manufacturing and understanding a supply chain in deeper and broader ways.

The marketplace of data science tools approach tasks in remarkably different ways and use different methods to aggregate and process data and generate actionable reports, graphics or simulations.

Here’s a look at 15 of the most popular tools and what sets them apart.

Data Science Tools Comparison Chart

15 Data Science Tools for 2023

Data Cleaning and Preprocessing Tools

Trifacta

Trifacta is a cloud-based, self-service data platform for data scientists looking to clean, transform and enrich raw data and turn it into structured, analysis-ready datasets.

Pros:

• Intuitive and user-friendly
• Machine Learning-based
• Integrates with data storage and analysis platforms

Cons:

• Costly for smaller projects
• Limited support for programming languages

Pricing
There isn’t a free option of Trifacta. However, there’s a Starter option at $80 per user, per month for basic functionality. The Professional option costs $4,950 per user, per year for added functionality, but requires a minimum of three licenses. There’s also the option for a desktop-based or a cloud-based free trial.

OpenRefine

OpenRefine is a desktop-based, open-source data cleaning tool that helps make data more structured and easier to work with. It offers a broad range of functions, data transformation, normalizations and deduplication.

Pros:

• Open-source and free to use
• Supports multiple data formats: CVS, XML and TSV
• Supports complex data transformation

Cons:

• No built-in ML or automation features
• Limited integration with data storage and visualization tools
• Steep learning curve

Pricing
100 percent free to use.

DataWrangler

DataWrangler is a web-based data cleaning and transforming tool developed by the Stanford Visualization Group, now available on Amazon SageMaker. It allows users to explore data sets, apply transformations and prepare data for downstream analysis.

Pros:

• Web-based with no need for installation
• Built-in data manipulation operations
• Automatic suggestions for  appropriate data-cleaning actions

Cons:

• Limited integration with data storage and visualization tools
• Limited support of large datasets
• Limited updates and customer support

Pricing
The use of DataWrangler on the Amazon SageMaker cloud is charged by the hour, starting at $0.922 per hour at 64 GiB of memory for standard instances, and at $1.21 at 124 GiB of memory for optimized memory.

AI/ML-Based Frameworks

Scikit-learn

Scikit-learn is a Python-based and open-source library that encompasses a wide range of tools for data classification and clustering using AI/ML.

Pros:

• Comprehensive documentation
• Reliable and consistent API
• Wide range of algorithms

Cons:

• Limited support for neural networks and deep learning frameworks
• Not optimized for GPU-usage

Pricing
100 percent free to use.

TensorFlow

Developed by Google, TensorFlow is an open-source machine learning and deep learning library. It enables users to deploy various models across several platforms, supporting both CPU and GPU computation.

Pros:

• Scalable and suitable for large-scale projects
• Allows for on-device machine learning
• Includes an ecosystem of visualizations and management tools
• Open-source and free to use

Cons:

• Steep learning curve
• Dynamic data modeling can be challenging

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.071 per hour.

PyTorch

PyTorch is an open-source ML library developed by Meta’s AI research team and based on the Torch library. It’s known for its dynamic computation graphs, computer vision and natural language processing.

Pros:

• Simplifies the implementation of neural networks
• Easy integration with Python
• Open-source and free to use
• Strong community support and documentation

Cons:

• Few built-in tools and components
• Limited support for mobile and embedded devices

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.253 per hour.

Deep Learning Libraries

Keras

Keras is a high-level neural network library and Application Programming Interface (API) written in Python. It’s capable of running on top of numerous frameworks, such as TensorFlow, Theano and PlaidML. It allows users to simplify the process of building, training and deploying data-based deep learning models.

Pros:

• User-friendly and easy to use
• Extensive documentations
• Pre-made layers and components

Cons:

• Limited compatibility with low-level frameworks
• Complex models may suffer from performance issues

Pricing
100 percent free to use.

Fast.ai

Fast.ai is an open-source deep-learning library built on top of Meta’s PyTorch and designed to simplify the training of neural networks using minimal code.

Pros:

• User-friendly interface
• Built-in optimization for deep learning tasks
• Extensive documentation and educational resources

Cons:

• Limited customization options
• Smaller active community

Pricing
100 percent free to use.

Hugging Face Transformers

Hugging Face Transformers is an open-source, deep-learning library that focuses on natural languages processing models, such as GPT, BERT and RoBERTa. It offers pre-trained models along with the tools needed to fine-tune them.

Pros:

• Large repository of ready-use models
• Supports Python and TensorFlow
• Active online community

Cons:

• Limited open natural language processing tasks
• Steep learning curve

Pricing
The library is 100 percent free to use, but when combined with AWS Cloud and AWS Inferentia2, pricing starts at $0.76 per hour.

Big Data Processing Tools

Apache Spark

Apache Spark is a distributed and open-source computing system designed to simplify and speed up data processing. It supports a wide range of tasks including data transformers, ML and graph processing.

Pros:

• In-memory data processing for higher performance
• Built-in ML and graph processing libraries
• Integrates seamlessly with Hadoop ecosystems and various data sources

Cons:

• Processing is resource-intensive
• Requires pre-existing programming knowledge

Pricing
The system is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.117 per hour.

Apache Hadoop

Apache Hadoop is an open-source, distributed computing framework that processes large volumes of data across clusters of servers and databases. It consists of Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Pros:

• Highly-scalable and fault-tolerant
• Supports a wide variety of tools such as Apache Hive and HBase for data processing
• Cost-effective

Cons:

• Disk-based storage leads to slower processing
• Limited support for real-time data processing
• MapReduce as a steep learning curve

Pricing
The framework is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.076 per hour.

Dask

Dask is a flexible, parallel computing library for Python that enables users to scale numerous well-known workflows using APIs such as Scikit-learn and NumPy. It’s designed specifically for multi-core processing and distributed computing.

Pros:

• Interface similar to Python
• Support for dynamic, real-time computation
• Lightweight and compatible with Python workflows

Cons:

• Limited support for languages other than Python
• Not ideal for processing large datasets

Pricing
100 percent free to use.

Cloud-based Data Science Platforms

Google Colab

Google Colab is a cloud-based Jupyter Notebook environment in which users are able to write and execute Python code directly in their web browsers. It’s a collaborative platform for both data science and machine learning tasks with accelerated computations.

Pros:

• No setup or installation required
• Online access to GPUs and TPUs
• Supports real-time collaboration and data sharing

Cons:

• Limited computing resources available
• Lack of built-in support for third-party integration

Pricing
With a free version available, Google Colab pricing plans start at $9.99 per month for the Colab Pro plan and $49.99 per month for the Colab Pro+ plan; a pay-as-you-go option starts at $9.99 per 100 compute units, or $49.99 per 500 compute units.

Databricks

Databricks is a unified data analytics platform that combines ML with big data processing and collaborative workspaces, all in a managed cloud environment. It’s a comprehensive solution for data engineers, scientists and ML experts.

Pros:

• Seamless integration with Apache Spark
• Supports high-performance data processing and analysis
• Built-in tools for version control, data visualization and model deployment

Cons:

• Cost ineffective for smaller projects
• Steep learning curve
• Vendor lock-in

Pricing
With a 14-day free trial available, Databricks can be deployed on the user’s choice of Azure, AWS or Google Cloud. There’s a price calculator, enabling customization of subscriptions.

Amazon SageMaker

Amazon SageMaker is a fully managed, ML platform that runs on Amazon Web Services. It allows data scientists and developers to build, train and deploy machine learning models in the cloud, providing end-to-end solutions for data processing, model training, tuning and deployment.

Pros:

• Integrates seamlessly with the AWS ecosystem and tools
• Built-in algorithms for popular machine learning frameworks, such as MX Net, PyTorch and TensorFlow
• Wide range of tools for model optimization, monitoring, and versioning

Cons:

• Steep learning curve
• High-end pricing
• Vendor lock-in

Pricing
With a free tier available, Amazon SageMaker is available in an on-demand pricing model that allows customization of services and cloud capacity.

Factors to Consider When Choosing Data Science Tools

As the importance of data continues to grow and transform industries, selecting the right tools for your organization is more critical than ever. However, with the vast array of available options, both free and proprietary, it can be challenging to identify the ideal fit for specific needs.

There are a number of factors to consider when choosing data science tools, whether it’s data processing frameworks or ML libraries.

Scalability

Scalability is a crucial factor to consider early on in the decision-making process. That’s because data science projects often involve large volumes of data and computationally-intensive algorithms. Tools like Apache Spark, TensorFlow and Hadoop are designed with big data in mind, enabling users to scale operations across multiple machines.

It’s essential to ensure that a tool can efficiently manage the data size and processing demands of the project it is chosen for, both currently and in the future as needs evolve.

Integration With Existing Infrastructure

Seamless integration with an organization’s existing infrastructure and legacy software is vital for efficient data processing and analysis. This is where caution can prevent being locked into a specific vendor.

Many online tools and platforms, such as Amazon SageMaker and Databricks, are compatible with a number of legacy systems and data storage solutions. This enables them to complement an organization’s existing technology stack and greatly simplify the implementation process, allowing users to focus on deriving insights from data.

Community Support and Documentation

A strong online community and comprehensive documentation are particularly important when choosing data science tools to be used by smaller teams. After all, active user communities are able to provide troubleshooting assistance, share best practices, and even contribute to the ongoing development of the tools.

Tools like Keras and Scikit-learn boast extensive documentation in addition to a widespread and active online community. This makes them accessible to beginners and experts alike. When it comes to documentation, it’s crucial that the available documents include up-to-date information and are regularly updated with the latest advancements.

Customizability

The ability to flexibly customize tools is essential to accommodate unique project requirements, but to also optimize performance based on available resources. Tools like PyTorch and Dask offer some of the most useful customizability options compared to their counterparts. They allow users to tailor their data processing workflows and algorithms to their specific needs.

Determining the level of customization offered by a tool and how it aligns with a project is important to guarantee the desired level of control.

Learning Curve

While all tools have a learning curve, it’s important to find data science tools with complexity levels that match the expertise of the data science and analytics teams that will be using them.

Tools such as Google Colab and Fast.ai are known for their user-friendly and intuitive interface, but other programming-based tools, like Apache Spark and TensorFlow, may be harder to master without prior experience.

The Future of Data Science Tools

The rapid development and innovation in the fields of AI and ML are also driving the development of new algorithms, frameworks and platforms used for data science and analytics. In some instances, those advancements occur too fast, and staying informed about the latest trends ensures the ability to remain competitive in an economy reliant on deriving insights from raw data.

Automation is increasingly playing a prominent role in how data is gathered, prepared and processed. Using AI and ML, tools like AutoML and H2O.ai can be used to streamline data parsing by automating some of the numerous steps that go into the process. In fact, the growing role of automation in data science is likely to shape the industry’s landscape going forward, determining which tools and skill-set are more viable and in demand.

The same is likely to apply to quantum computing, as it holds great potential to revolutionize countless data processing and optimization problems, thanks to its ability to tackle complex and large-scale tasks. Its impact could potentially lead to new algorithms, frameworks and tools specifically designed for data processing in quantum environments.

Bottom Line: Data Science Tools

Choosing the right data science tools for an organization requires a careful evaluation of factors such as scalability, integration with existing infrastructure, community support, customizability and ease of use. As the data science landscape continues to evolve, staying informed about the latest trends and developments, including ongoing innovations in AI and ML, the role of automation and the impact of quantum computing will be essential for success in the data-driven economy.