About This Course
In this data science course, you will explore the theory and practice of select advanced methods commonly used in data science.
In the first two modules, you will learn about common applications of specialized data types. Then, in the remaining two modules, you will focus on unstructured data. You will work with tools such as R, Python, and Azure Machine Learning to solve advanced data science problems.
What you'll learn
- Explore analysis of time series and forecasting
- Take a look at spatial data analysis
- Learn about text analytics
- Review analysis of images
Meet the instructors
Graeme has been a trainer, consultant, and author for longer than he cares to remember, 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.
Cynthia 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.
Dr. Steve Elston
Steve is a 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