MATLAB for Data Science
Explore data; build machine learning models;
do predictive analytics
MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.
- Access data stored in flat files, databases, data historians, and cloud storage, or connect to live sources such as data acquisition hardware and financial data feeds
- Manage and clean data using datatypes and preprocessing capabilities for programmatic and interactive data preparation, including apps for ground-truth labeling
- Document data analysis with MATLAB graphics and the Live Editor notebook environment
- Apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
- Explore a wide variety of modeling approaches using machine learning and deep learning apps
- Fine-tune machine learning and deep learning models with automated feature selection and hyperparameter tuning algorithms
- Deploy machine learning models to production IT systems, without recoding into another language
- Automatically convert machine learning models to standalone C/C++ code
Why Use MATLAB for Data Science?
Exploratory Data Analysis
Spend less time preprocessing data. From time-series sensor data to images to text, MATLAB datatypes significantly reduce the time required to preprocess data. High-level functions make it easy to synchronize disparate time series, replace outliers with interpolated values, filter noisy signals, split raw text into words, and much more. Quickly visualize your data to understand trends and identify data quality issues with plots and the Live Editor.
Applied Machine Learning
Find the best machine learning models. Whether you’re a beginner looking for some help getting started with machine learning, or an expert looking to quickly assess many different types of models, apps for classification and regression provide quick results. Choose from a wide variety of the most popular classification and regression algorithms, compare models based on standard metrics, and export promising models for further analysis and integration. If writing code is more your style, you can use hyperparameter optimization built into model training functions, so you can quickly find the best parameters to tune your model.
Deploy machine learning models anywhere including C/C++ code, CUDA® code, enterprise IT systems, or the cloud. When performance matters, you can generate standalone C code from your MATLAB code to create deployable models with high-performance prediction speed and small memory footprint. You can also export machine learning models for use in Simulink® or deploy models to MATLAB Production Server™ for integration with web, database, and enterprise applications.