18 Data Science Tools to Consider Using in 2024

neub9
By neub9
3 Min Read

The growing size and complexity of enterprise data, along with its vital role in decision-making and strategic planning, are propelling organizations to invest in the people, processes, and technologies necessary to derive valuable business insights from their data assets. This includes a range of tools commonly utilized in data science applications. According to an annual survey conducted by consulting firm Wavestone’s NewVantage Partners unit, 87.8% of chief data officers and other IT and business executives from 116 large organizations reported an increase in their investments in data and analytics initiatives, such as data science programs, in 2022. Looking towards the future, 83.9% expect further increases in 2023 despite current economic conditions. The survey also revealed that 91.9% of the organizations achieved measurable business value from their data and analytics investments in 2022, with 98.2% expecting their planned 2023 spending to yield returns. Despite this progress, many strategic analytics goals remain aspirational, with only 40.8% of respondents stating they’re competing on data and analytics, and just 23.9% having created a data-driven organization. As data science teams work to achieve these analytics goals, they can choose from a broad selection of tools and platforms. Here’s an overview of 18 top data science tools that may aid in the analytics process, listed in alphabetical order with details on their features and capabilities — and some potential limitations.

1. **Apache Spark:** An open source data processing and analytics engine capable of handling large amounts of data, Apache Spark is popular amongst organizations for its speed and ability to process data rapidly.

2. **D3.js:** A JavaScript library for creating custom data visualizations in a web browser, D3.js uses web standards such as HTML, Scalable Vector Graphics and CSS to generate visual representations of data.

3. **IBM SPSS:** A family of software for managing and analyzing complex statistical data, IBM SPSS includes two primary products: SPSS Statistics and SPSS Modeler, offering a broad range of statistical analysis and data science capabilities.

4. **Julia:** An open source programming language designed for numerical computing and machine learning, Julia provides convenience of a high-level dynamic language and performance comparable to statically typed languages such as C and Java.

5. **Jupyter Notebook:** A computational notebook tool that enables interactive collaboration among data scientists, data engineers, mathematicians, and researchers, facilitating the creation, editing, and sharing of code and explanatory text in a single document.

6. **Keras:** A high-level interface that makes it easier to access and use the TensorFlow machine learning platform, Keras is designed to accelerate the implementation of machine learning models through a development process with high iteration velocity.

By leveraging these tools, organizations can gain valuable insights and make informed decisions based on their data assets.

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