You’ll master the in-demand skills necessary to become a successful data analyst like data pre-processing, visualization and analysis using Power BI as the primary tool.
Introduction to Preparing and Modeling Data
Where do you even start when the data you need for a single report lives in a bunch of different files and software systems? This is where preparing and modeling data becomes essential! This course is a crucial step in Power BI for anyone who needs to mash together multiple data sources, clean them, restructure them, and harmonize them into a single and efficient data model to support reporting. We’ll cover Power BI’s built-in Extract-Transform-Load (ETL) tool, Power Query, learn foundational data modeling principles, cover some introductory DAX (Data Analytics Expressions), and touch on troubleshooting and optimization.
Creating Visualizations with Power BI
In this course, students will learn how to carry Power BI beyond mere bar charts and transform their reports into data exploration & storytelling tools that companies can use to better understand their data. Students will start by learning about a variety of common and more advanced data visualizations. Then, students will learn how to design reports around these data visuals in order to focus user attention on key insights, help users navigate different features and report pages and enable accessibility options for diverse audiences. Next, the student will learn how to use filters and slicers. Finally, students will deliver a couple of advanced features capable of elevating how users navigate and engage with visuals and the report itself.
Advanced Data Analysis
In this course, students will focus on the techniques and skills needed for data analysis in Power BI. The course is centered around building a strong foundation and intuition of analytics so that students can take their skills beyond simply aggregating data in Power BI and into the realm of statistics, forecasting and strategy. We first start with an introduction to data analysis looking at different terms and techniques such as descriptive and inferential statistics, histograms, linear regression and an introduction to the concepts of correlation and probability. After taking the introductory lessons on data analytics, the course then moves to M, the language of Power Query, and learning to build custom formulas as part of the data transformation process. The overarching goal of the course is to help students become effective at the process of retrieving, analyzing and visualizing data in order to answer questions and draw conclusions.
nd331 Lenore R Flower, nd331 Sean Chandler and nd331 Joseph Lozada