You can register for it here: https://academy.microsoft.com/en-us/professional-program/data-science/
The Course Outline is:
1. Data Science Orientation
Get started on your data science journey, as you learn what it takes to become a Data Scientist. Learn to work with and explore data using a variety of visualization, analytical, and statistical techniques.
2. Query Relational Data
Explore Transact-SQL, an essential skill for database professionals and developers working with SQL databases. Go from your first SELECT statement through to implementing transactional programmatic logic. Focus on querying and modifying data in Microsoft SQL Server or Azure SQL Database.
3. Analyze and Visualize Data
Explore tools in Excel that enable the analysis of more data than ever before, with improved visualizations and more sophisticated business logic. Learn how to import data from different sources, create mashups between data sources, and prepare data for analysis.
Learn how to connect and visualize your data with Microsoft Power BI. Find out how to import your data, author reports using Power BI Desktop, and publish those reports to the Power BI service. Create dashboards and share with business users—on the web and on mobile devices.
4. Understand Statistics
Learn how statistics plays a central role in the data science approach and how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions, and construct models for predicting future trends.
5. Explore Data with Code
Learn the R statistical programming language, the tool of choice for data science professionals. Discover its basic syntax, starting with variables and basic operations, and then learn how to handle data structures, such as vectors, matrices, data frames, and lists.
Learn the basics of Python, including simple arithmetic operations, variables, and data structures. Explore Python functions and control flow, and create your own visualizations based on real data.
6. Understand Core Data Science Concepts
Learn key concepts and techniques used to perform data science; including statistical analysis, data cleansing and transformation, and data visualization with R, Python, and Microsoft Azure Machine Learning.
7. Understand Machine Learning
Learn how to build, evaluate, and optimize machine learning models; including classification, regression, clustering, and recommendation.
8. Use Code to Manipulate and Model Data
Get up to speed with programming in R. Explore R data structures and syntaxes, see how to read and write data from a local file to a cloud-hosted database, work with data, get summaries, and transform them to fit your needs.
Learn to use Python to apply efficient, well-known mining models to unearth useful intelligence. Explore data visualization, feature importance and selection, dimensionality reduction, clustering, classification, and more.
9. Develop Intelligent Solutions
Learn how to apply machine learning to solve common predictive problems, including text analytics, spatial data analysis, image processing, and time series forecasting.
Learn how to use Spark in Microsoft Azure HDInsight to create predictive analytics and machine learning solutions. Find out how to cleanse and transform data, build machine learning models, and create real-time machine learning solutions using Python, Scala, and R with Apache Spark.
Learn how to develop smart applications that use machine learning to engage intelligently with users in imaginative ways.
10. Final Project
Put everything you've learned into practice by participating in a data science challenge and competing with your fellow students. Take on a challenge on the Cortana Intelligence platform, where you develop and deploy a solution that is tested and scored to determine your grade.
And you can go ahead to get the Microsoft Professional Program Certificate in Data Science (conditions apply). Register for the Microsoft Professional Program Certificate in Data Science