**18 data science tools to consider using in 2024** Here are 18 data science tools to consider using in 2024: * **Google Cloud Platform** * **Amazon Web Services** * **Microsoft Azure** * **IBM Watson** * **SAS** * **Oracle** * **SAP** * **Databricks** * **Alteryx** * **Tableau** * **Power BI** * **Looker** * **Qlik** * **Dundas BI** * **Sisense** * **TIBCO Spotfire** * **SAS Viya** These tools offer a variety of features and capabilities, so you can choose the ones that best meet your needs.

**Data science tools summary**

This article provides a comprehensive overview of 18 popular data science tools, including their features, capabilities, and potential limitations. The tools are listed in alphabetical order, and they include both open source and commercial options.

* **Apache Spark** is a distributed processing engine that can handle large amounts of data. It is well suited for continuous intelligence applications and near-real-time processing of streaming data.
* **D3.js** is a JavaScript library for creating custom data visualizations in a web browser. It uses web standards, such as HTML, Scalable Vector Graphics, and CSS, instead of its own graphical vocabulary.
* **IBM SPSS** is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a data analysis and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities.
* **Julia** is an open source programming language used for numerical computing, as well as machine learning and other applications. It combines the convenience of a high-level dynamic language with performance that is comparable to statically typed languages, such as C and Java.
* **Jupyter Notebook** is an open source web application that enables interactive collaboration among data scientists, analysts, and other users. It is a computational notebook tool that can be used to create, edit, and share code, as well as explanatory text, images, and other information.
* **Keras** is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It is an open source API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform.
* **Matlab** is a high-level programming language and analytics environment for numerical computing, mathematical modeling, and data visualization. It is primarily used by conventional engineers and scientists to analyze data, design algorithms, and develop embedded systems for wireless communications, industrial control, signal processing, and other applications.
* **Matplotlib** is an open source Python plotting library that is used to read, import, and plot data. Data scientists and other users can create static, animated, and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers, and various GUI toolkits.
* **NumPy** is an open source Python library that is used widely in scientific computing, engineering, and data science applications. The library consists of multidimensional array objects and routines for processing those arrays to enable various mathematical and logic functions. It also supports linear algebra, random number generation, and other operations.
* **Pandas** is another popular open source Python library that is typically used for data analysis and manipulation. Built on top of NumPy, it features two primary data structures: the Series one-dimensional array and the DataFrame, a two-dimensional structure for data manipulation with integrated indexing. Both can accept data from NumPy ndarrays and other inputs; a DataFrame can also incorporate multiple Series objects.
* **Python** is the most widely used programming language for data science and machine learning and one of the most popular languages overall. The Python open source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” as well as built-in data structures and dynamic typing and binding capabilities. The site also touts Python’s simple syntax, saying it’s easy to learn and its emphasis on readability reduces the cost of program maintenance.
* **PyTorch** is an open source framework used to build and train deep learning models based on neural networks. It is touted by its proponents for supporting fast and flexible experimentation and a seamless transition to production deployment. The Python library was designed to be easier to use than Torch, a precursor machine learning framework that’s based on the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators.
* **R** is the R programming language is an open source environment designed for statistical computing and graphics applications, as well as data manipulation, analysis, and visualization. Many data scientists, academic researchers, and statisticians use R to retrieve, cleanse, analyze, and present data, making it one of the most popular languages for data science and advanced analytics.
* **SAS** is an integrated software suite for statistical analysis, advanced analytics, BI, and data management. Developed and sold by software vendor SAS Institute Inc., the platform enables users to integrate, cleanse, prepare, and manipulate data; then they can analyze it using different . SAS can be used for various tasks, from basic BI and data visualization to risk management, operational analytics, , predictive analytics, and machine learning.
* **Scikit-learn** is an open source machine learning library for Python that’s built on the SciPy and NumPy scientific computing libraries, plus Mat

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