Data Analytics Microcredential

In-person courses provide a social and ethical lens to a traditionally mathematical topic by ensuring that students understand the potential socioeconomic impacts of using and manipulating data to draw conclusions.

Additionally, upon completing this microcredential you will have the ability to:

• Apply tabular and graphical methods to summarize, analyze, and visualize data in practical business situations.
• Compute, examine, and interpret statistical measures.
• Apply probabilistic and stochastic models to quantify uncertainty.
• Build and analyze predictive models.
• Use optimization techniques to identify the best alternatives to minimize or maximize an objective.
• Use data to arrive at informed conclusions and recommend decisions.

Throughout the course, students will apply methods discussed in class to analyze data on a topic of interest and discuss their understanding of the problem.

A final capstone project will demonstrate the participant's skill in achieving the targeted competencies outlined in the learning objectives.

During the capstone project, participants will collect data on a topic of interest - preferably from their workplace - and apply the methods discussed in class to analyze the data.

The capstone project will assess the overall competency gained by the participant and determine whether the participant has gained an adequate level of skill to qualify for earning this Microcredential.

The Data Analytics Microcredential is not eligible for academic credit.

Per student cost: $2,450

The Data Analytics Microcredential is an introductory course and covers descriptive, predictive, and prescriptive analytics.

There are no prerequisites for this non-credit program.

Students can expect to spend 16 weeks towards attaining this microcredential.

Course topics include:

• Introduction to Business Analytics and basic Excel skills
• Summarizing and visualizing data in spreadsheets
• Statistical measures and probability distributions
• Statistical sampling
• Hypothesis testing
• Regression analysis and forecasting techniques
• Introduction to data mining
• Spreadsheet modeling
• Risk analysis
• Optimization models