Data Science with 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
About the Presenter
Heather Gorr holds a Ph.D. in Materials Science Engineering from the University of Pittsburgh and a Masters and Bachelors of Science in Physics from Penn State University. Since 2013, she has supported MATLAB users in the areas of mathematics, data science, machine learning, deep learning, and application deployment. Prior to joining MathWorks, she was a Research Fellow, focused on machine learning for prediction of fluid concentrations.
Recorded: 23 Jul 2020
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