Course Outline


  • Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks


  • Experience with Matlab
  • No previous experience with data science is required
  21 Hours


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