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Lead A.I., Machine Learning, and Data Science Projects With Confidence

This 3-day immersive course from the University of Georgia demystifies machine learning and data science and focuses on the practical applications of these revolutionary technologies in business. Designed for people with little to no coding experience, Practical Machine Learning and Data Science for Executives provides hands-on experience building and implementing data science projects. Upon course completion, you’ll earn a Digital Badge that signifies your new skillset and expands your career opportunities.

Snapshot

Format: Classroom, Instructor

Credits: 18 hours/1.8 CEUs

Where: UGA's Athens campus

When: July 29 - 31, 2019

Cost: $2,759 - Course Fee Details

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Learn Cutting-Edge Technologies to Advance Your Career

There is a common misconception that artificial intelligence (AI), machine learning (ML), and data science are difficult topics meant only for the technical elite who have an education in the field and programming experience. However, it’s becoming increasingly beneficial for business executives, owners, and manufacturing specialists to understand the basics of data science technologies and be able to implement them efficiently.

This 3-day in-person course addresses these challenges and requires no prior coding experience to succeed. Taught by an expert in the field with over 30 years of practical experience, it provides you with the conceptual, theoretical, and industrial knowledge you need to lead AI, ML, and data science projects with confidence.

What You Will Learn:

  • The basics of data science and data analytics using machine learning
  • The basics of developing use cases for business impact and the resources required
  • How a data science project is executed and its results are interpreted
  • About data sources, data creation, data pre-processing, and the data analytics model building process based on machine learning
  • How to implement data science and machine learning in your company projects
  • How to use non-programming type machine learning tools and software for the non-practitioner
  • How to use data to build predictive analytics models and to measure their performance
  • About cutting-edge technologies like Deep Learning and their applications
  • Data generation, data pre-processing, building a model, predicting, inferencing, and telling a story
  • Key terminology of AI, machine learning, and data science

For a complete listing of Learning Objectives, please download the file.

Successful enrollees earn a Digital Badge and 1.8 University of Georgia Continuing Education Units (CEUs).

Who Should Attend?

  • Mid to senior level executives who are/could get involved in a data science project or who want to start one in their business environment
  • Business managers and plant managers involved with their company’s digital transformation programs
  • Small and large business owners who need to understand the basics of cutting-edge data science technologies and how and where to apply them
  • Product management professionals who are involved in the development of data-driven products (IoT, wearables, etc.)
  • Business executives seeking to expand their basic skillset in the space of data science

Course Information

Course Number: 

91971

Course Date: 
Monday, July 29, 2019 to Wednesday, July 31, 2019
Course Date Info: 

For Frequently Asked Questions, please click on the downloadable file.

Location: 
Georgia Center for Continuing Education Conference & Hotel
1197 South Lumpkin Street, Athens, GA 30602
United States
US
Course Fee(s): 

For more details on course fee and cancellation policy, download the Program Fees and Lodging pdf.

Continuing Education Information: 

Students will be awarded 1.8 Continuing Education Units (CEUs) upon successfully completing the course, along with a Digital Badge to display on your social media assets.

Visit the University of Georgia Continuing Education Units CEU policies for more details.

Attendance Policy and Other Important Requirements:

To receive the Continuing Education Units (CEUs) the following is required:

  • Attendance at all sessions
  • Participation and completion of the hands-on projects

Details are subject to change.