Predictive Data Analysis

Overview

This course introduces students to a selection of powerful tools in Power BI Desktop that can be used for predictive analysis. Concepts such as classification, regression, and forecasting are discussed in detail, and various techniques are explored with different data sets, allowing students to practice predictive data analysis with a hands-on approach. The content covered in this course can help an enterprise to understand future risk, analyze different markets or make other decisions critical to an organization.

Syllabus

Introduction to Predictive Data Analytics

This lesson will introduce predictive analytics concepts and a general overview of when to use it in your organization. We will also review the project and the primary tool we will use: Power BI.

Regression

We will review one Machine Learning technique that involves numeric data: Regression. We will learn about its main features, how to interpret it and how to construct it using Power BI.

Classification and Clustering

We will learn additional ML techniques: Classification and Clustering. We will review its main features, some clustering techniques, how to interpret them and how to construct clusters with Power BI.

Time-Series Forecasting

We will review what a time-series is. Also, we will review deterministic and probabilistic forecasting methods, how to read and interpret time-series and how to create forecasts using Power BI.

Additional Tools and Techniques

We will review additional ML and AI-powered tools using Power BI: we will focus on Power BI dataflows and its AutoML features and use two AI-powered visuals: the Q&A and the smart narratives.

Commercial Flight Analysis in the State of New York

In this project, students will use the data of past flights departing from the State of New York to analyze and give recommendations about flight delays.