Are you interested in pursuing a career in data analytics? In this course, instructor Robin Hunt brings you into the mind of an analyst. She defines and explains foundational concepts, such as how to think about data, how to work with others in different roles to get the data you need, and the tools you need to work with data, such as Excel and Microsoft Access. She introduces you to SQL queries, PowerBI, and more. Robin goes into syntax and explains how to interpret the data you see, find the data you need, and clean the data for effective data work. She explains data governance and how to ask the right questions of different departments to gather the data you need. Robin shows how to work with data, including how to import data, work with flat files such as CSVs, and create datasets for others. Robin goes into what cleaning and modeling mean, as well as how to use Power Query in Excel. She has also added challenge/solution sets in each chapter to help you evaluate your skills. This course is part of a Professional Certificate from Microsoft.
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🔅 Data Literacy: Exploring and Describing Data
🌐 Author: Barton Poulson
🔰 Level: Beginner
⏰ Duration: 5h 14m
📗 Topics: Data Science
📤 Join Data Analysis for more courses
🌀 Learn the fundamentals of data fluency and how data can help you make better decisions.
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Data analysis isnt just for specialists who need to make sense of massive datasets. Decision-makers in every industry can benefit from a basic understanding of the goals and concepts of applied data analysis. In this course, Barton Poulson focuses on the fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps. Barton shows how exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions. Instead of focusing on particular tools, he concentrates on general procedures that can help you solve specific problems. Barton covers how to prepare and adapt data, explore it visually, and use statistical methods to describe it. He goes in depth on probability and interference and also touches on data ethics and explainable AI.
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