Data Analytics

What's New in Data Analytics

Explore the latest MATLAB® functions and features for developing machine learning models, working with big data, and operationalizing analytics to production systems.   


Developing Machine Learning Models

Train regression models using supervised machine learning.

Requires Statistics and Machine Learning Toolbox

Train classifiers in parallel.

Requires Parallel Computing Toolbox and Statistics and Machine Learning Toolbox

Tune machine learning algorithms by searching for optimal hyperparameters.

Requires Statistics and Machine Learning Toolbox

Use neighborhood component analysis (NCA) to choose features for machine learning models.

Requires Statistics and Machine Learning Toolbox


Working with Big Data

Manipulate and analyze data that is too big to fit in memory. (Introduced in R2016b.)

Requires MATLAB

Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data.

Requires Statistics and Machine Learning Toolbox

Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters.

Requires Parallel Computing Toolbox

Run applications on your desktop or Spark using tall arrays or the MATLAB API for Spark.

Requires MATLAB Compiler


Managing and Preprocessing Data

Manage time-stamped tabular data with time-based indexing and synchronization. (Introduced in R2016b.)

Requires MATLAB

Manipulate, compare, and store text data efficiently​​​​. (Introduced in R2016b.)

Requires MATLAB

  • Find, fill, and remove missing data
  • Detect and replace outliers
  • Smooth noisy data 

Requires MATLAB

Detect formats and automatically return appropriate data types with the Import Tool and functions for import and export.

Requires MATLAB


Operationalizing Analytics

Generate C code for prediction by using linear models, generalized linear models, decision trees, and ensembles of classification trees.

Requires MATLAB Coder and Statistics and Machine Learning Toolbox

Develop clients for MATLAB Production Server in any programming language that supports HTTP.

Requires MATLAB Production Server

Configure and manage multiple server instances using a web-based interface.

Requires MATLAB Production Server