This course targets software developers and data scientists looking to understand the initial steps in a machine learning solution. The content will showcase methods and tools available using Microsoft Azure.
No data science project of merit has ever started with great data ready to plug into an algorithm. In this course, Cleaning and Preparing Data in Microsoft Azure, you'll learn foundational knowledge of the steps required to utilize data in a machine learning project. First, you'll discover different types of data and languages. Next, you'll learn about managing large data sets and handling bad data. Finally, you'll explore how to utilize Azure Notebooks. When you're finished with this course, you'll have the skills and knowledge of preparing data needed for use in Microsoft Azure. Software required: Microsoft Azure.
Course Overview Hi everyone. My name is Jared Rhodes, and welcome to my course, Cleaning and Preparing Data in Microsoft Azure. I'm a Microsoft MVP for the Azure platform at QiMata Technologies, a consulting and training firm based out of Atlanta, Georgia. In this course, we are going to examine data needs, languages, and common data formats with demos in Python and R. After that, we'll move into discussing large datasets and bad data. Then, we will demo both with SQL. Then we're going to move on to discussing data aggregation strategies, Jupyter Notebooks, and then moving them into Azure Notebooks where we will demo getting started with Azure Notebooks. Then we will discuss data labeling and encoding, and once we have covered all of that, we will discuss working with multiple datasets. By the end of this course, you'll know the concepts of data science in Microsoft Azure well enough to get started cleaning and preparing data. I hope you'll join me on this journey to getting started with machine learning and data science in Microsoft Azure with the Cleaning and Preparing Data in Microsoft Azure course, at Pluralsight.