Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconTop 10 Data Science Platforms in 2024

Top 10 Data Science Platforms in 2024

Last updated:
19th Feb, 2023
Read Time
10 Mins
share image icon
In this article
Chevron in toc
View All
Top 10 Data Science Platforms in 2024

What is Data Science Technology?

Data science technology is one of the rapidly growing technologies of this era. Data Science is the field of technology that includes domain expertise and programming skills with a knowledge of mathematics and statistics. They all combine to extract meaningful values from data.

This technology applies Machine Learning Algorithms to the information collected in the form of numbers or text or images or something like video or audio and many more. They are used to produce Artificial Intelligence systems that further perform jobs similar to human intelligence. As an outcome, these systems create valuable insights that analysts evaluate to transform into business value.

Check out our free courses to get an edge over the competition.

Why is Data Science becoming more important for an enterprise?

With innovations in technologies, enterprises realize the requirement of Data Science, Machine Learning and Artificial Intelligence. Whatever may be the size of the organization, Data Science always plays an important role to develop and implement meaningful insights for many business operations and strategies.

Explore our Popular Data Science Courses

What are Data Science Platforms?

Data science platforms are used for mining large volumes of data, whether it is structured or unstructured, and turning them into a valuable resource for identifying patterns to manage operations. With the growing demand for data science and machine learning, there are emerging software and tools that are developed with new technology. Here are some of the best data science platforms that serve as the top data science platforms in 2021 to fit the requirements of the business.

1. Dataiku DSS by Dataiku

The Dataiku DSS solution helps the data science team to run projects with Advanced Analytics. This data science platform encourages to provide more insights into the business, and eventually, it makes a significant impact. 

Dataiku is the centralized platform of data. It helps to move businesses along their data collaboration from analytics at scale to enterprise AI.

Dataiku provides a common place for both data experts and explorers, thus combining them with a repository of best practices that involves machine learning and AI deployment/management.

The best thing about Dataiku is it is a provider of a centralized and controlled environment, thus becoming the catalyst for data-powered companies. 

It expands its usefulness in Customers from a diverse range of retail, finance, e-commerce, the public sector, manufacturing, transportation, health care, pharmaceuticals, and more. Dataiku is on its way to accelerating self-service analytics by ensuring the operationalization of machine learning models in production. It emphasizes removing roadblocks, thus providing more opportunities for making a business-impacting model. Its creative solutions allow data science teams to work with a more innovative approach. 

Check out our data science courses to upskill yourself.

2. Alteryx Designer by Alteryx

Alteryx Designer is one of the top data science platforms in 2021.

It is designed in such brilliance that it empowers the data scientists and analysts to witness a data analytic experience. It derives answers from nearly any data source available with a many code-free tools that are also code-friendly to use.

It simplifies the data prep with data blending and reporting, using predictive and advanced analytics. It is designed for the data scientist team’s ease of use. Alteryx Designer provides blending of data in simple drag and drop form that can be applied to creating spreadsheets, databases, data lakes, cloud sources, enterprise applications, RPA bots and many more.

The main thing about Alteryx is it automates every step of analytics that includes data prep, blending, reporting, predictive analytics, and data science. It eventually accelerates visual insights and enriches further operations. As it automates analytics and applies repetitive processes, this helps drive faster actions as it is used to publish outcomes to interactive dashboards or send results straight to enterprise applications.

Alteryx Designer helps to access any data source or file, or application, or data type. With 260+ drag-and-drop building blocks, Alteryx powers a self-service platform that allows its users to experience simplicity and helps to start creating an interactive module.

When a data scientist prefers to use a “code-first” or “low-code” option, they can choose the Alteryx Designer and leverage the integrated tools like R and Python tools. Alteryx Designer offers integrated data prep and data quality in the model creation that helps further to create ML models in a faster time frame with a guided and assisted modelling experience.

Top Data Science Skills to Learn

3. RapidMiner Studio by RapidMiner

RapidMiner is an intuitive platform with visual workflow design and full automation. It is a comprehensive platform that requires minimal coding. It is capable of leveraging the entire Python library. RapidMiner meets all the needs of the beginner in data science to the skilled data scientist. It uses a drag and drop visual interface that helps to speed up and automate the creation of predictive models. RapidMiner has a rich library of more than 1,500 algorithms, ensuring the best model for a comprehensive model.

RapidMiner Studio has a collection of templates that are pre-built inside the software. They offer some common purposes like customer churn, fraud detection, predictive maintenance and some other important jobs.

RapidMiner studio has a unique feature called “Wisdom of Crowds” that provides proactive recommendations for helping beginner level users. One of the essential features of RapidMiner is it creates instant connections to databases, enterprise data warehouses, cloud storages, data lakes, business applications and many more. They even provide reuse connections whenever the user needs, and it is easily shareable with anyone who requires access. The best thing is RapidMiner allows the user to query and retrieve data without the need for writing complex SQL, and it empowers to facilitate highly scalable database clusters. 

RapidMiner Studio supports MySQL, Google BigQuery and PostgreSQL.

4. IBM SPSS Statistics by IBM

IBM SPSS is used to sort, arrange and analyse significant volumes of data like the survey dataset for predictive modelling and other analytic tasks. The main advantage of this platform is it is swift in arranging dataset and giving analysis.

The IBM® SPSS® software platform offers a vast range of efficiency and reliability for advanced statistical analysis. It consists of a large library of machine learning algorithms. IBM SPSS also offers open-source extensibility, text analysis, and integration with big data. It provides a seamless deployment into applications. 

IBM SPSS has become one of the top data science platforms in 2021 and the most popular platforms among the data science teams for its ease of use. It also offers flexibility and scalability that make SPSS accessible among users from all levels of skills, from beginners to experts. Additionally, it is appropriate for projects of all sizes and levels of complexity. SPSS helps the teams and the organization find new opportunities, improving efficiency and minimizing risks.

Read our popular Data Science Articles

5. H2O Driverless AI by

H2O is one of the best tools for machine learning when it comes to dealing with large volumes of data. H2O helps to improve the execution time with its faster model iterations and development.

The main important feature of H2O is it provides Driverless AI that empowers data scientists to work on projects in a more intelligent and faster way. It works efficiently by using automation technology to accomplish key machine learning jobs in a quick time frame. 

H2O delivers automatic feature engineering, model tuning, model selection and deployment, model validation, machine learning interpretability and automatic pipeline generation for model scoring.

H2O Driverless AI provides data science organizations with an extensible and customizable data science platform. It helps in addressing the requirements of a varied range of applications that every enterprise needs in every field. H2O Driverless AI has an extensive library of algorithms. It provides transformations to automate the high-value features for a specific data set. Data science teams can always extend the H2O Driverless AI platform if they wish to upload their own models, transformers and scorers. It further helps in the automatic machine learning workflow. 

6. Google AI Platform by Google

Google Cloud AI is an end-to-end platform that is fully managed. It offers brilliant governance with interpretable models in a faster way. 

This platform is efficient for every skill level user. The key features of this platform include AutoML or advanced model optimization along with a built-in Data Labelling Service. It also provides model validation and AI Explanations. There is a unique feature called What-If Tool that helps one understand model outputs and verify model behaviour. There is a black-box optimization service called Vizier that allows tuning the hyper-parameters. It also helps to optimize model performance. This platform manages models, experiments and end-to-end workflows with pipelines that apply MLOps. 

Check out our Data Science Professional Certificate in BDM from IIM Kozhikode

7. RStudio

Rstudio is an integrated development environment (IDE) for R that is a programming language. This is specially used for statistical computing and graphics. It is a platform that is dedicated to sustainable investment in free and open-source software for data science.

Rstudio is available in two formats: RStudio Desktop, which is a regular desktop application, while another one is RStudio Server that runs on a remote server. Rstudio Server allows accessing RStudio via a web browser.

RStudio includes a syntax-highlighting editor which supports direct code execution. It also offers tools for plotting, history, debugging, and workspace management. There is RStudio Server Pro that is an integrated development environment for R and Python. It uses a console, syntax-highlighting editor to support the execution of direct code. RStudio Server Pro uses tools for plotting, history, and debugging with workspace management.

8. KNIME Analytics Platform by KNIME

KNIME standard for the Konstanz Information Miner. It is a free, open-source platform for data analytics on a GUI based workflow.

It is also a reporting and integration platform. KNIME integrates different components for machine learning and data processing through its modular data pipelining that supports the “Lego of Analytics” concept.

It uses GUI (Graphical User Interface) and JDBC that allows assembling the nodes, blending allows assembly of nodes blending different data sources and also include pre-processing that is ETL: Extraction, Transformation, Loading for the purpose of modelling, data analysis and visualization. It might happen with the help of minimal programming.

One can perform various functions starting from basic I/O to data manipulations, transformations and data processing. It consolidates all the parts of the whole process into one workflow.

9. Matlab by MathWorks

MATLAB is a numerical computing platform that is used for processing mathematical information. It is a closed-source software. MATLAB offers matrix functions and algorithmic implementation. It also provides statistical modelling of data. MATLAB is the most widely used software in a wide range of scientific applications.

MATLAB is used for simulating neural networks and fuzzy logic.

One can create powerful visualizations using the MATLAB graphics library. MATLAB is additionally utilized in image and signal processing that creates a crucial and versatile tool for Data Scientists. It helps them handle all the tasks like data cleaning, data analysis and advanced Deep Learning algorithms.

MATLAB® makes data science more efficient with easily accessible tools and helps to pre-process data. It also provides a solution to building machine learning and predictive models. MATLAB helps in the deployment of models to enterprise IT systems.

10. Kraken by Big Squid 

Kraken is an AutoML platform that is built to enable data analysis with Advanced Analytics Solutions. 

Kraken includes a powerful data analysis tool that is built into the platform. With just one click, one can do whatever he wants: plot, colour, sort, and many more. This way, it helps to understand the data in a better way as the data scientist builds and iterates the predictive models.

The key features of Kraken include KRAKEN PIPELINE and KRAKEN AUTOML.

The Kraken no-code automated machine learning (AutoML) platform helps to simplify and automate data science jobs like data prep and cleaning, algorithm selection, model training as well as tuning. It also helps to

model deployment that further helps to focus on the task with higher priority.

The future of Data Science

Data Science is emerging with the purpose of providing solutions to organizations to transform a certain set of data into a valuable resource that will eventually help in creating impact in business value. With the rapid increase in business enterprises and organizations, Data Science is becoming more prevalent in every aspect. Machine Learning and Artificial Intelligence surfacing the new age of Information Technology, the emerging data science software and tools are serving as a pivotal role in every business model.

If you’d want to dive deeper into working with Python, especially for data science, upGrad brings you the Executive PGP in Data Science. This program is designed for mid-level IT professionals, software engineers looking to explore Data Science, non-tech analysts, early career professionals, etc. Our structured curriculum and extensive support ensure our students reach their full potential without difficulties.  


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Explore Free Courses

Suggested Blogs

42 Exciting Python Project Ideas & Topics for Beginners in 2024 With Source Code [Latest]
Summary: In this article, you will learn the 42 Exciting Python Project Ideas & Topics in 2024. Take a glimpse below. Mad Libs Generator Number
Read More

by Rohit Sharma

06 May 2024

Top 13 Highest Paying Data Science Jobs in India [A Complete Report]
In this article, you will learn about Top 13 Highest Paying Data Science Jobs in India. Take a glimpse below. Data Analyst Data Scientist Machine
Read More

by Rohit Sharma

12 Apr 2024

Most Common PySpark Interview Questions & Answers [For Freshers & Experienced]
Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s
Read More

by Rohit Sharma

05 Mar 2024

Data Science for Beginners: A Comprehensive Guide
Data science is an important part of many industries today. Having worked as a data scientist for several years, I have witnessed the massive amounts
Read More

by Harish K

28 Feb 2024

6 Best Data Science Institutes in 2024 (Detailed Guide)
Data science training is one of the most hyped skills in today’s world. Based on my experience as a data scientist, it’s evident that we are in
Read More

by Harish K

28 Feb 2024

Data Science Course Fees: The Roadmap to Your Analytics Career
A data science course syllabus covers several basic and advanced concepts of statistics, data analytics, machine learning, and programming languages.
Read More

by Harish K

28 Feb 2024

Inheritance in Python | Python Inheritance [With Example]
Python is one of the most popular programming languages. Despite a transition full of ups and downs from the Python 2 version to Python 3, the Object-
Read More

by Rohan Vats

27 Feb 2024

Data Mining Architecture: Components, Types & Techniques
Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a
Read More

by Rohit Sharma

27 Feb 2024

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About
What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes
Read More

by Rohit Sharma

19 Feb 2024

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon