Demo files for the historical data analysis from Data Analytics with MATLAB Webinar found at: http://www.mathworks.com/videos/data-analytics-with-matlab-99066.html
Using Data Analytics to turn large volumes of complex data into actionable information can help you improve engineering design and decision-making processes. However, developing effective analytics and integrating them into business systems can be challenging. In this webinar you will learn approaches and techniques available in MATLAB to tackle these challenges.
- Accessing, exploring, and analyzing data stored in files, the web, and data warehouses
- Techniques for cleaning, exploring, visualizing, and combining complex multivariate data sets
- Prototyping, testing, and refining predictive models using machine learning methods
- Integrating and running analytics within enterprise business systems and interactive web applications
Who Should Attend:
- Engineers, analysts, and data scientists interested in developing analytics
- Managers interested in using data analytics to improve design and design-making
- IT managers and systems engineers interested in integrating analytics into business systems
Current version of the demo files were developed on R2017b. The 'required products' are the minimum to run the main DataAnalyticsScript shown in the webinar. Parallel Computing Toolbox will accelerate execution of the script but is not required to run it.
Also included with the files are eight additional scripts which show the workflow in greater detail. Script9 also shows an extension of the analysis past what is shown in the webinar. Additional product requirements for the scripts not shown in the webinar:
Script6_WeatherCleaning requires Mapping Toolbox
Script8_ModelDevelopment requires Neural Network Toolbox
Script9_UnitCommitment requires Optimization Toolbox
Adam Filion (2022). Data Analytics with MATLAB Webinar Files (https://www.mathworks.com/matlabcentral/fileexchange/49063-data-analytics-with-matlab-webinar-files), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired: Machine Learning for Regression
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