Data Science using MATLAB


Overview

A variety of new tools for data science have been recently added to MATLAB. These include functions for exploratory data analysis and apps for quickly exploring machine learning models and deployment to multiple platforms and languages.

In this session, we explore the fundamentals of data science using MATLAB. We will use an example to address a typical data science problem including data access, preprocessing, machine learning model development, and finally deployment of the model to a web application.

Highlights

  • Accessing and exploring large data sets 
  • Preprocessing and analyzing various types of data including textual and time-stamped data using MATLAB data types: table, timetable, string, categorical, datetime, duration, tall arrays 
  • Working with messy data including outliers, missing, and noisy data, joining tables, synchronizing data by time, and calculating statistics by group 
  • Visualizing various data types: time series plots, heatmaps, wordclouds, geographic plots, boxplots 
  • Training and validating machine learning models using the Classification Learner and Regression Learner apps 
  • Integrating model predictions into a web application running on the cloud

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