Data Science Essentials

About this course

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.

What you'll learn

Explore the data science process

Probability and statistics in data science

Data exploration and visualization

Data ingestion, cleansing, and transformation

Introduction to machine learning

The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

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Course Syllabus

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

Meet the instructors

Graeme Malcolm

Senior Content Developer Microsoft Learning Experiences

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.

Dr. Steve Elston

Managing Director Quantia Analytics, LLC

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

Cynthia Rudin

Associate Professor MIT and Duke

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.

Microsoft: DAT203.1x Data Science Essentials