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Data Science Essentials

Enrollment in this course is by invitation only

About This Course

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

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

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

Course Staff Image #1

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

Course Staff Image #2

Cynthia Rudin

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.

Course Staff Image #3

Graeme Malcolm

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.

  1. Course Number

    DAT203.1x
  2. Classes Start

  3. Classes End

  4. Estimated Effort

    18-24 hours in total

Associated Programs:

  1. MicroMasters Program:

    Data Scientist AI Developer