Data Science Essentials

This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.

Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.

In this data science course, 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 Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Explore the data science process – An Introduction
• Understand data science thinking
• Know the data science process
• Use AML to create and publish a first machine learning experiment
• Lab: Creating your first model in Azure Machine Learning Probability and statistics in data science
• Understand and apply confidence intervals and hypothesis testing
• Understand the meaning and application of correlation Know how to apply simulation
• Lab: Working with probability and statistics
• Lab: Simulation and hypothesis testing Working with data – Ingestion and preparation
• Know the basics of data ingestion and selection
• Understand the importance and process for data cleaning, integration and transformation
• Lab: Data ingestion and selection - new
• Lab: Data munging with Azure Machine Learning, R, and Python on Azure stack Data Exploration and Visualization
• Know how to create and interpret basic plot types
• Understand the process of exploring datasets
• Lab: Exploring data with visualization with Azure Machine Learning, R and Python Introduction to Supervised Machine Learning
• Understand the basic concepts of supervised learning
• Understand the basic concepts of unsupervised learning
• Create simple machine learning models in AML
• Lab: Classification of people by income
• Lab: Auto price prediction with regression
• Lab: K-means clustering with Azure Machine Learning