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MichiganX: Introduction to Data Analytics for Managers

Explore data science and analyze business data with Microsoft Azure through the use of SQL, machine learning, and other statistical techniques focused on business applications.

Introduction to Data Analytics for Managers
6 weeks
2–4 hours per week
Self-paced
Progress at your own speed
This course is archived
Future dates to be announced

About this course

Skip About this course

Through a combination of lectures, business case studies, and hands-on learning this course provides an introduction to data analytics techniques and their application in business.

The case studies explored will illustrate how companies are leveraging different sources of data, including “big data,” with different analytical techniques, to improve performance. You will receive hands-on learning through a free web-based graphical development environment that will allow you to practice using some of these tools themselves. You will also gain an understanding of the many possibilities for applying data science in business, and will be able to consider additional learning opportunities to gain further depth.

This course is an excellent resource for managers who see the opportunity to use data analytics in business but do not have the skills and background to engage with data analytics themselves.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills:Data Analysis, Data Science, SQL (Programming Language), Business Case, Microsoft Azure, Machine Learning, Development Environment, Analytical Techniques, Big Data

What you'll learn

Skip What you'll learn
  • The many different data science techniques and their applicability in business via case studies
  • Handling of data analytics with a graphical development environment, which makes advanced tools easily accessible without coding
  • How to conduct and interpret some basic data science activities, including:
    • A simple scatter plot, to visually assess relationships between two or more quantities;
    • A basic SQL query, to understand how to pull data from multiple interrelated sources;
    • A basic hypothesis test, to understand statistical significance and its impact;
    • A basic machine learning experiment, to understand what machine learning is and how to interpret its output.

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