You will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using R, and Python on Azure stack.
Along the course, you will explore the data science process, understand probability and statistics in data science, learn data exploration and visualization, data ingestion, cleansing, and transformation, and you will receive an introduction to machine learning. The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning.
Explore the data science process – An Introduction
Understand data science thinking
Probability and statistics in data science
Understand and apply confidence intervals and hypothesis testing
Working with data – Ingestion and preparation
Know the basics of data ingestion and selection
Data Exploration and Visualization
Know how to create and interpret basic plot types
Introduction to Supervised Machine Learning
Understand the basic concepts of supervised learning
Lab: K-means clustering with Azure Machine Learning
Graeme Malcolm, Senior Content Developer, Microsoft Learning Experiences; trainer, consultant, and author, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.
Steve Elston, Managing Director, Quantia Analytics, LLC; big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies.
Cynthia Rudin, Associate Professor, MIT and Duke; leads the Prediction Analysis Lab at MIT, and is associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management. She holds a PhD in applied and computational mathematics from Princeton University, and was previously, an associate research scientist at the Center for Computational Learning Systems at Columbia U.
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