14 data science tools to consider (with definition)

By Indeed Editorial Team

Published 11 November 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many businesses generate significant amounts of data through daily activities. Analysing and using this data productively is a way to recognise patterns, forecast success or gain a clear picture of sales or marketing success. Tools for data science provide the technology necessary to use data, with data scientists using a wide range of unique software to visualise and analyse vast quantities of data in one place. In this article, we define what data science tools are and provide 14 of the most popular options available to analyse data.

What are data science tools?

Data science tools are software, applications or suites of programmes that businesses and data scientists use to collate, analyse and visualise data that goes through a company. The exact functionality of tools for data science differs depending on their purpose, with some tools providing niche services such as human language processing and others offering generalised data operation services. The output of tools for data science is typically filtered, organised or simplified data that businesses can use to inform business decisions and forecast future activities.

Related: Data science vs. data analytics: what's the difference?

14 popular data science tools

Tools for data science are available in various specialisations and price points, from simplistic open-source tools for data scientists to enterprise solutions for use across the business. Ensuring you're using the ideal tool for the job is crucial for effectively managing data and gaining insight and information that is valuable for many purposes. Here are some of the popular tools for data science you may consider:

1. KNIME

KNIME offers an open source, free platform that data scientists can utilise to blend different tools and types of data. This tool works with a wide range of data sources and platforms, providing functionality with end-to-end data science by combining everything under one dashboard and environment. Key features of KNIME include the accessing and merging of data, modelling and visualisation and leveraging insights from data. A commercial version, under the name KNIME Server, offers additional commercial functionality to 'productionise' data effectively.

2. BigML

BigML is a high-profile data science tool that provides a cloud-based, interactable GUI with specific features for processing machine learning algorithms. BigML provides a single location to complete machine learning activities with data from all business areas as a standardised tool. For example, default predictive modelling algorithms provide forecasting, clustering, time-series and classification in one package. BigML also supports automation methods, allowing you to create reusable scripts to automate specific parts of data workflows.

Related: 12 data science certifications to consider gaining

3. RapidMiner

RapidMiner provides a comprehensive tool that businesses can utilise to visualise the life-cycle of prediction modelling. Key functionalities of this platform include model building, data preparation, deployment and validation. An inclusive GUI makes connecting blocks for practical usage easy, while a cloud-based repository provides consistent access to all users. RapidMiner also has functionality for big-data analytics built into the system, allowing businesses to handle large volumes of data through a single, accessible tool. The RapidMiner Studio feature includes the majority of tools necessary for day-to-day operation.

4. Tableau

Tableau provides high-quality data virtualisation in a dedicated software package, focusing on the requirements of companies working within business intelligence. The critical functionality of interfacing with databases and spreadsheets is a valuable feature of Tableau, alongside the capability to accurately visualise geographic information into maps. An analytics tool is also available with the platform, allowing you to effectively analyse and visualise data in the same place.

Related: Data science graduate schemes (with roles and requirements)

5. Integrate.io

Integrate.io is a valuable integration tool with extract, load and transform (ELT) and extract, transform and load (ETL) functionalities. This platform allows you to bring all data sources together in one space, with a toolkit necessary for building data pipelines and total flexibility and scalability to suit business needs. Integrate.io is a helpful tool for preparing for cloud integration, with features including metric centralising, sales solutions, customer support functionalities and marketing solutions to support multiple aspects of a business. Legacy connections to existing systems and easy migration processes provide ease of use when converting to the Integrate platform.

6. Designer Cloud

Designer Cloud from Trifacta offers a series of different data wrangling and preparation products, from personal-level solutions to enterprise functionalities. This cloud platform provides convenient data profiling with visualisations for ease of access, with functionality for building, deploying and managing data pipelines for data engineers and analysts. Multi-cloud support provides businesses with an effective way to combine multiple streams of data in one place, with a high degree of connectivity to reporting tools, spreadsheets and data science applications.

Related: AI analytics in data science: definition, uses and tips

7. SAS

SAS offers an option for large organisations that require robust data analysis solutions. As an industry staple, SAS features multiple statistical libraries and tools that date scientists can utilise to organise data and create effective models with high reliability. Additional features outside of standard SAS may be necessary to fully use this software, depending on business goals and the specific data analysis requirements.

8. Data Robot

Data Robot offers a flexible tool for data scientists, software engineers and IT professionals to automate machine learning, allowing for a wide range of models and predictions to tailor to particular business requirements. Python SDK and APIs provide employees with a visual interface and ease of use, providing a shorter learning curve to handle deployment successfully. Data Robot specialises in collaboration between teams on a single platform, with machine learning, AI applications and decision intelligence all providing necessary information for various operational purposes.

Related: A guide on how to be a data science consultant in 7 steps

9. Excel

For general business use beyond advanced data science, Microsoft's Excel platform provides fundamental insight and information for spreadsheets of data. Organisation and summarisation of data through visualisations, such as charts and graphs, is a core functionality of this tool. Easily sorting, filtering and formatting data are all standard features of Excel. Using existing software may be valuable for businesses without significant data science requirements to gain simple insights and basic information to make reports and commit to business decisions.

10. Apache Hadoop

Apache Hadoop provides an open-source framework that's entirely scalable to suit different operational needs, with a modular design that allows companies to implement the functionalities they require specifically. For instance, Hadoop YARN provides a framework for job scheduling and cluster management of resources, while Hadoop MapReduce supplies a parallel processing solution that accounts for large data sets. The ability to handle data sets with customisable programming models enables companies to personalise their approach and access the specific services they require.

Related: How to write a data science CV that gets you hired

11. MATLAB

MATLAB is a scalable programming and computing solution that allows for data analysis and algorithms development within a dedicated platform. Interactive apps divide data algorithms for easier accessibility, with a selection of external language interfaces such as Python, C/C++, Fortran and Java, providing flexibility for data output. MATLAB's solution allows for parallel computing and web and desktop deployment, with cloud functionalities allowing for practical access and easy inclusion of data from multiple sources.

12. NLTK

NLTK is a unique programme with the ability to process human language through various filters and formats effectively. Using machine learning, the natural language toolkit reads data using techniques such as tagging, parsing and tokenisation to provide a functional output. NLTK has a particular purpose, supporting functionalities such as speech tagging, word segmentation and text-to-speech recognition that directly involve reading natural language to a high standard.

Related: What is data modelling? (Definition, types and skills)

13. Alteryx

Alteryx provides a functional platform for discovering, preparing and analysing large amounts of data. This tool has coverage to discover and collaborate data across every area of an organisation, combining incoming information into complete models. Easy access to central management of workflows, users and data is a helpful tool to centralise processes, while the option to embed models created in R, Python or Alteryx into processes is practical in effectively controlling and managing data within a business.

14. TensorFlow

TensorFlow is an industry-standard tool for machine learning, featuring various advanced machine learning algorithms, including deep learning. This open-source tool continues to evolve and change over time, with the capability to run on both CPU and GPUs to maximise efficiency and increase processing power. Some of the key functionalities available in TensorFlow include image classification, speech recognition and language generation, with new machine learning options appearing regularly.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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