Data Science Showdown: Which Tools Will Gain Ground in 2025

In a recent article, we wrapped up 2024 with an overview of 10 data science trends that defined the year that just ended. Now, looking forward, this article ventures into forecasting data science tools that are expected to gain importance in the year ahead.

While many of the published 2025 prospects on relevant data science tools predominantly focus on well-known and established tools and technologies like Python, Tensorflow, Tableau, and so on, we intent to provide a more serendipitous and differential perspective that concentrates on relatively less extended tools whose prominence is growing.

Below, we identify and briefly analyze key data science tools expected to gain visibility this year. The discussion of tools is categorized by their primary applications and uses.

Tools for Big Data Processing & Efficient Computing

 
Are you a data scientist grappling with ever-larger datasets? Chances are you are one of them.

Among the indispensable tools that enable efficient processing of vast datasets, PySpark is drawing increasing attention with its distributed computing capabilities, having become a cornerstone for big data analytics leveraging the best of Spark and Python. Numba, a Python library that accelerates numerical computations, is also getting attention as one of the newest go-to approaches for performance-focused data science projects. Meanwhile, the Julia programming language, known for its high-speed computing features, is increasingly becoming an appealing option for data scientists who need to deal with for complex mathematical and scientific workflows, intersecting speed, and precision.

Data Visualization, Reporting & Communication

 
Data storytelling is here to stay, being a cornerstone approach for impactful data science, particularly in organizational contexts.

Among the latest data visualization and reporting tools, D3.js is gaining ground due to the unparalleled flexibility it offers in building custom, interactive visualizations that look simply stunning, while the versatile Plotly provides a friendly interface for crafting stunning, publication-ready charts and dashboards. The increasing popularity of these tools underscores the current demand for intuitive ways to communicate insights effectively to diverse audiences, based on more and more complex and diverse data sources.

Example visualization with D3.js | Source: Observable HQ

Application Development & Model Deployment

 
Fast turnaround from insights to action remains in high demand by organizations. For this reason, tools that facilitate rapid development and deployment of data and machine learning systems are gaining momentum.

Streamlit simplifies the creation of interactive dashboards and applications like predictive models, which allows data scientists to share preliminary results with minimal effort. For machine learning application lifecycle management, MLflow and Kubeflow stand out as leading platforms for tracking experiments, scalable model deployment, and reproducibility. Meanwhile, tools like H2O.ai offer robust AutoML capabilities (automatic training of machine learning models with little or no code) thereby democratizing AI by making machine learning model development to a wider variety of users. In the growing landscape of edge computing, NVIDIA’s DeepStream SDK deserves special mention: this tool yields real-time data processing and inference on edge devices—a vital capability for autonomous systems and IoT applications.

Data Cleaning & Preparation

 
A not less important part of a data scientist’s job is dealing with raw data, and preparing it to look its best for subsequent analyses. Among the tools that simplify this process, OpenRefine is recently on the radar: this tool excels in transforming raw, messy data into structured formats suitable for analysis, providing functionalities that save time and effort while enhancing reliability.

Cloud Platforms

 
A vital part of many data scientists’ work relies on using the broad range of services by cloud providers in their ever-expanding platforms. Among the three cloud giants’ platforms — Google with GCP, Amazon with AWS, and Microsoft with Azure — which one is gaining ground at present, and may continue doing so in the year ahead?

Google Cloud Platform (GCP), largely deemed as the least used of the three, is interestingly the one with a faster pace of growth at present, with its suite of machine learning and big data services. GCP’s offerings, such as BigQuery and Vertex AI, help many thousands of data scientists across the globe harness the power of the cloud for processing large datasets, training complex AI models, and integrating them into business workflows.

Wrapping Up

 
As we navigate 2025, the data science landscape keeps quickly evolving, with some tools gaining prominence because of their innovative characteristics or their ability to tackle current business challenges or data needs. This article delved into some tools expected to gain importance throughout 2025, providing insights into their recent or anticipated impact.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.