## Duration

21 hours (usually 3 days including breaks)

## Requirements

- Experience with Matlab
- No previous experience with data science is required

## Overview

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

- Create predictive models to analyze patterns in historical and transactional data
- Use predictive modeling to identify risks and opportunities
- Build mathematical models that capture important trends
- Use data from devices and business systems to reduce waste, save time, or cut costs

**Audience**

- Developers
- Engineers
- Domain experts

**Format of the course**

- Part lecture, part discussion, exercises and heavy hands-on practice

## Course Outline

Introduction

- 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