Professional Course

Introduction to Data Analytics for Managers

edX, Online
Length
6 weeks
Price
49 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Length
6 weeks
Price
49 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Visit this course's homepage on the provider's site to learn more or book!

Course description

Introduction to Data Analytics for Managers

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.

Upcoming start dates

1 start date available

Start anytime

  • Self-paced Online
  • Online
  • English

Who should attend?

Prerequisites

None

Course delivery details

This course is offered through The University of Michigan, a partner institute of EdX.

2-4 hours per week

Costs

  • Verified Track -$49
  • Audit Track - Free

Certification / Credits

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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