Multiparadigm Data Science

Multiparadigm Data Science is a new approach of using AI and modern analytical techniques, automation and human-data interfaces to arrive at better answers with flexibility and scale.

Many organizations are still doing traditional data science—confining themselves to problems that are answerable with traditional statistical methods—rather than utilizing the broad range of interfaces and techniques available today. Whether it's automated machine learning, interactive notebooks and report generation, natural language queries of data for instant visualizations or implementing neural networks with ease and efficiency, modern problem solving requires access to the right technology at every stage.

With a flexible, integrated multiparadigm workflow, problems too complex for traditional methods can get real, quantifiable answers.

Is Your Data Science

Is your process led by questions rather than techniques?

Do you have a broad collection of algorithms for any situation?

Can you connect to curated data resources for quick research?

Is your analysis fast enough for real-time answers?

Instead of confining your scope based on a limited set of tools, you should be able to choose computational methods based on the problem you're solving.

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Can you handle any data type?

Does that include images and audio?

What about text and linguistic data?

Can you work with hundreds of file formats?

You might think of data science as a numbers game, but the best insights often come from exploring images, sounds, text and other data.

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Can you combine multiple different types of computation?

Do you have algorithms for every subject and department?

Can you change techniques without a major code rewrite?

Is your data unified for compatibility across methods?

No technique is universal; real exploration requires the flexibility to test and combine different methods on your data.

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Do you have the right interface for your data?

Are you exploring your data interactively?

Does that include natural language queries?

Can you make point-and-click interfaces for your users?

Is your reporting process automated?

Every interface has its use—simple forms, interactive controls, sophisticated linguistic queries—and you need the full spectrum of options available to show your data effectively.

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Is your presentation tailored to your results?

Do you have specialized visualizations for all your needs?

Can you add symbols, diagrams and other details inline?

Are you able to edit expressions while presenting?

Can you deploy instantly to a range of platforms?

Representing complex results with the wrong visual can obscure your conclusions; you need a broad range of visualizations and deployments to express all your insights clearly.

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Do you have a start-to-finish workflow?

Can you import, analyze and visualize without swapping programs?

Is everything combined into one coherent document?

Can you rapidly develop, test and deploy models?

Do you present and publish directly from your working notebook?

Rather than constantly switching among disparate software packages, you need a single, integrated system that does it all.

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Congratulations! It sounds like you already have a multiparadigm solution for your data science workflow. Contact us to get in touch—we'd love to hear more about it.

My trading dashboards run on real-time data connections, high-precision computation and detailed charts. And since it's all in a cloud-based multiparadigm system, I can access it from anywhere for quick decisions.

To study the spread of the epidemic, we had to wrangle information from a variety of fields and sources, running several large-scale simulations. Our multiparadigm system let us consolidate the data quickly and adjust our models in real time for faster, more effective analysis.

To examine political partisanship in the US Senate, we sorted through multidimensional data and tried analytic techniques from a variety of fields. The multiparadigm approach let us discover patterns we otherwise wouldn't have seen.

My run data automatically goes from my device to the cloud, where I can immediately compute on it, connect it to other data and visualize it in all kinds of unexpected ways. The multiparadigm approach allowed me to answer questions I didn't even know I had.

Importing data from curated service connections, our intuitive interface lets us explore data without a lot of programming or math. We used the multiparadigm approach to make complex data look interesting—and discovered some unique insights along the way.

Interfacing with Your Data

Having the right interface to get answers from data is crucial; different interfaces are suited to different tasks. A multiparadigm system combines a broad range of intuitive options for interacting with data every step of the way.

Interactive Notebooks

Streamline your daily workflow using high-level notebook documents that combine text, images, code and interactive examples—editable and runnable on any platform for fast, easy collaboration.

Natural Language

Query your data instantly with natural language and get conversational answers for an intuitive, low-effort process that leads to more insights and better decisions.

Triggered Reporting

Automatically receive alerts and updated reports based on custom criteria, tracking trends in real time for critically timed decisions.

Presentations with Live Modeling

Run your meetings interactively, adjusting parameters to compute instant what-if scenarios for deeper discussions and stronger results.

Programmatic Access

Store your models as platform-independent packages and APIs, providing a centralized framework for computation and automation to power human-data interfaces across your enterprise

Classic Interfaces with Modern Integration

Seamlessly connect existing spreadsheets, databases and applications into higher-level computation, stepping up speed and accuracy with additional back end power.

Know Your Data Science Areas

The field of data science is constantly growing and changing. A multiparadigm workflow requires a broad algorithmic toolkit with the full suite of processing, analysis and visualization for ever-increasing computational needs.

Machine Learning

Generate adaptive models directly from complex datasets for object classification and predictive analytics, such as identifying which new advertising markets to enter.

Graph/Network Analysis

Explore and visualize systems of discrete relationships to analyze correlations and patterns, such as modeling demographics in a social network.

Time Series

Model, simulate and forecast sequences of events over time to track long-term trends and make predictions, such as expected sales for the next holiday season.

Neural Networks

Create and train layered processing networks for deep analysis and processing tasks, such as recognizing defective items coming off a production line.

Cluster Analysis

Group and analyze data based on similarity measures to extract underlying patterns and relationships, such as which customers are most similar to your top 100.

Semantic Text Analysis

Analyze underlying structures in linguistic data to clean up data and extract meaning, such as determining sentiment in customer comments.

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Dynamic Visualization

Display data in styled plots, charts and infographics, making it human-readable and interactive for quick analysis and decision making.

Survival Analysis

Compute survival functions and lifetime distributions to analyze time-to-event data, such as the expected lifetime of a piece of industrial equipment.

Report Generation

Display conclusions and insights in a styled, formatted document for meetings, ongoing projects or public information, like a quarterly earnings report.

Data Semantics

Standardize various incoming datasets into a unified framework for easier analysis, such as consolidating data with different unit systems.

Queueing Theory

Model and simulate systems of queues to analyze waiting times and resource allocation, such as the optimal number of tellers at a bank branch.


Deconstruct data signals into constituent parts for advanced manipulation and filtering of specific features, such as eliminating background noise from sensor data.

Systems Modeling

Model physical, electrical and other systems to inform design decisions, like the most effective heating installation for a building.

Statistical Distributions

Fit historic data to parametric distributions to make inferences about the underlying events, such as the likelihood of a customer clicking through an ad.

Random Processes

Model the progression of a system over time to make observations and predictions about its behavior, such as analyzing peak hours at a particular store location.


Use high-level mathematics to discover the "best values" for your data in relation to key criteria, such as the ideal allocation of portfolio assets.

Morphological Analysis

Use geometric transformations on images and higher-dimensional data to analyze spatial properties, such as counting particles in a microscopic image.

Computer Vision

Process visual data with machine learning and other sophisticated algorithms for analysis of features and patterns, such as identifying road hazards from a video feed.

Signal Processing

Process and filter images, audio and other collected data to analyze underlying patterns, such as detecting an irregular heartbeat from an ECG.

Custom Interface Construction

Make interactive onscreen controls for real-time adjustment of parameters in analyses and visualizations, allowing deeper exploration of data.

Parallel Computing

Distribute parallel tasks to available computation units for large-scale scientific computing and other high-performance applications.


Use precise geolocation data and powerful geodetic computations to accurately examine real-world situations, such as visualizing optimal routes for a bus service.

Mathematical Modeling

Drive systems of differential equations, recurrence relations and symbolic formulas with your data to test and refine models, such as computing the recovery rate of an epidemic.

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Thirty years of building the ultimate computation environment make the Wolfram technology stack ideal for Multiparadigm Data Science (MPDS)—a multitude of interfaces, language types, computational approaches and ready-to-use data all woven into one ecosystem.

A multiparadigm approach requires a broad, flexible computational toolkit that incorporates all aspects of a project into one start-to-finish workflow. The Wolfram technology stack does exactly this, enabling you to take data from hundreds of formats, carry out a full spectrum of analysis and visualization and immediately share or publish your results—all using the world's largest collection of algorithms and computable knowledge.

MPDS with the Wolfram Language
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