AI and Predictive Analytics

1 Units
Online Self-Paced
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Course Description

Add to Your Professional Toolbox: Learn to Utilize AI With Predictive Analytics

This course teaches business professionals - with no prior statistics or programming background - how to direct AI tools through a complete prediction problem: framing the business question, preparing data, building and comparing models, and testing the results for accuracy and fairness. The curriculum covers both classification and regression problems using accessible, explainable methods: logistic and linear regression, alongside decision trees. Models are built using Orange Data Mining, a free, open-source visual tool where workflows are assembled by connecting components rather than by writing code.  An AI assistant serves as an analytical partner throughout — profiling data, explaining outputs, sanity-checking conclusions. Additionally, students learn how to expand a dataset rather than simply accept the one provided — joining a second internal source and incorporating external variables such as seasonality, geography or published indicators.

Course Details

Number of Units:1.0 graduate-level extension credit(s) in semester hours

Who Should Attend: This course is built for working professionals who make, influence, or answer for recurring decisions that a model could inform — and who already use AI tools in their everyday work.

Technical Requirements

 

Course Options

Course Date Units Price

What You Will Learn

  • Translate a business question into a prediction problem - naming the target, the inputs, and the baseline any model must beat - and recognize the questions prediction cannot answer
  • Assemble the inputs for a prediction from more than one source, including external variables, and judge what each is expected to contribute
  • Distinguish predicting a discrete event from estimating a continuous value, and identify which of the two a given business question requires
  • Build, test and compare predictive models in Orange Data Mining, a free visual tool, directing an AI assistant to profile the data and interpret the results - without writing code
  • Interpret model performance - accuracy, precision, recall, and the confusion matrix for a classification, along with typical error size for regression - in terms of what each error would cost the business
  • Recognize and correct the characteristic failure modes of AI-assisted analysis, including data leakage, overfitting, and an assistant that is confidently wrong
  • Test a model for disparate impact using the four-fifths rule, and interpret the results
  • Judge which data may be placed in a third-party AI tool, substituting public, synthetic, or de-identified data where sensitive data cannot be used
  • Document and defend a model-driven decision in writing at a standard a non-technical stakeholder, an affected customer, or an auditor would accept

Instructors

Professional development courses offered by the 亚色影库鈥檚 Division of Professional & Continuing Education are taught by faculty that possess a depth and breadth of academic and real-world professional experience.

Why 亚色影库?

The Professional and Continuing Education program nurtures key partnerships on the local, national, and international level. The goal is to better serve working professionals who seek to enhance or build their careers and help achieve their highest value and potential. Contact us today to learn more.

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Learning Method Information

Courses offer a convenient, yet rigorous style of learning that allows you to structure your education to suit your schedule while keeping you on pace toward achieving your educational goals.

Online Self-Paced

Our online self-paced courses are designed to provide flexibility and autonomy for learners. Unlike traditional classroom methods, which require attendance at scheduled times, self-paced courses allow you to progress through the material at your own speed within a six-month period from the time of enrollment.  Your instructor will provide feedback via written responses on your assignments and exams. Grades are based on completed projects, assignments and exams.

Key differences from traditional classroom methods:

  • No scheduled class times or required attendance.
  • No assigned due dates for assignments; you set your own schedule.
  • Asynchronous learning, meaning you can work on assignments anytime.
  • No discussion forums and no interaction with others enrolled in the course.
  • No scheduled class times or required attendance.

Structure of Online Self-Paced Courses:

The content is divided into learning modules, each covering one or more topics. Within each module, you can expect the following components:

  • Module Introduction: Outlines what you can expect to learn in the module.
  • Required Readings and Presentations: Includes textbooks, articles, journals, websites, and audio/video presentations.
  • Assignments: May include written assignments, quizzes, blogs, and other activities based on the readings and presentations.
  • Module Conclusion: Reviews the topics covered and what you should have learned.

Typically, there is a final project, paper, or exam due in the last module that synthesizes all the topics covered throughout the course. The design of the learning modules follows a consistent rhythm to help you manage your time effectively. Grades will be available within two weeks of final submission. Complimentary transcripts for EDU and CV courses will be automatically mailed out following grades being posted and approved. Please allow 3-4 weeks from when final grades are posted to receive a transcript.