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Predictive Analytics with Machine Learning and Data Mining

 UT Campus | $3,350

Upcoming Sessions

There are no sessions of this course scheduled at this time. Please join our 'interest list' below to express your interest in the course, and stay informed on scheduling and updates.


    Evaluate data-driven business intelligence challenges and tools, such as data mining and machine learning techniques. Apply data-driven intelligence to improve decisions and competitive strategies, and to estimate expected impact on performance. Prepare to analyze unprecedented volumes of rich data to predict the consequences of alternative courses of action and guide decision-making.

What You'll Learn

This course provides foundational frameworks, critical thinking skills and hands-on training with predictive analytical models.

Data-Driven Intelligence

  • Apply effective data-driven intelligence to improve your decisions and systematically estimate the expected impact on relevant performance objectives
  • Develop a data-analytical approach to problem-solving, and identify opportunities to derive value from data-driven intelligence
  • Discuss data-driven business intelligence challenges and tools like data mining and machine-learning techniques


Data-Driven Intelligence Tools & Techniques

  • Discuss the landscape of data-driven intelligence tools, the basics of data mining techniques, and their applications in practice
  • Acquire hands-on experience to follow up on ideas or opportunities that present themselves


Attending this Course

  • Individuals

    This course is designed for analysts and leaders who want to understand and become conversant in predictive analytics.
  • Teams

    Organizations often send pairs or small teams, to support the launch of new initiatives.
  • Requirements & Credit

    There are no prerequisites for this course. Participants earn 1.4 CEUs and/or 14 CPEs, as well as a certificate of completion. Our classes are available for university credit. Please contact us for more information.
Eye Opening Experience
I personally found several new ways of looking at old problems and started working on them before the training ended. I am now looking to apply the methods learned to automate decision making processes and cost reduction. These classes are highly recommended for anyone who likes to innovate and is interested in changing the future of business.
Buildings on the University of Texas campus.

Who Should Attend?

  • Analysts

    Analysts and leaders who want to understand and become conversant in predictive analytics.
  • Leaders

    Analysts and leaders who want to find new ways to solve complex business challenges using predictive analytics.
  • Managers

    Managers who want to get better leading and managing ongoing projects that leverage these types of analytical techniques.
Maytal Saar Tsechansky Headshot
  • Maytal Saar-Tsechansky

    Dr. Maytal Saar-Tsechansky is a Professor of Information, Risk and Operations Management at the McCombs School of Business, The University of Texas at Austin.

Demonstrate Your Expertise with a Certificate

  • Reimbursement Options

    Learn more about course credits and options for course reimbursement. Get tips on the best way to approach your manager and download a customizable template to facilitate making the ask.
  • Course Location

    In person courses take place at the AT&T Executive Education and Conference Center and adjoining Rowling Hall on the UT campus in Austin. These world-class facilities provide a comfortable and convenient learning environment, with direct access to the 40 acres of campus and within walking distance of downtown Austin. Live online and on-demand course options are available for many courses.

Earn a Badge

We offer digital badges for select courses, which enable you to verify your skills and achievements. When you complete this course, you will earn a digital badge that you can showcase on your LinkedIn profile.

Business Analytics

Additional Courses

Data Analytics

Discover, analyze and forecast relationships among large data sets (“Big Data”).

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