Business Intelligence Analytics

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In this lesson, we will introduce you to data analysis and what we will be covering in this course.

In this lesson, we will learn how to solve data analysis issues using each step of the data analytics life cycle.

In this lesson, we will review the importance of data integrity in data analysis, the components of data integrity, and how to identify and fix it.

In this lesson, we will review each of the different types of data analytics and when to use them, along with statistics that pertain to each analytics type.

In this lesson, we will introduce Power BI and its capabilities as an analytics and data visualization tool.

In this lesson, you'll learn how to set up a Windows desktop workspace to use Power BI

Introduction to Data Preparation & Modeling lesson introduces students to the topics, highlights, and prerequisites of the course. And requirements to successfully complete the course are explained.

Relational Data & Table Structure lesson teaches the core concepts around working with data from multiple sources and tables. It explains what relational data is and how it works.

Power Query & M Overview lesson highlights the Power Query data wrangling system that is used within Power BI. Specific cleaning and organization tools and techniques are explored.

Data Cleaning & Organization lesson takes a practical deep dive into Power Query. Here more complex data cleaning and organization strategies are practiced, and the M language is explained.

DAX Language & Quantitative Analysis lesson dives into DAX, which is the primary analytical language of Power BI. Different foundational concepts around DAX calculations are covered.

In this lesson, you'll learn how to set up a Windows desktop workspace to use Power BI

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.

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.

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.

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.

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.

In this lesson, you'll learn how to set up a Windows desktop workspace to use Power BI

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