Mastering Data-Driven Management and Decision Making Course
Overview
Organizations generate more data than ever before, yet many professionals rely on analytics and AI outputs without fully understanding how those results are produced or how they should be interpreted. Developing the ability to work confidently with data-driven insights is now a critical leadership skill.
Mastering Data-Driven Management and Decision Making provides a practical foundation in data analytics and generative AI for professionals who use data to inform strategy, operations, and decision-making. Rather than focusing on programming or building models, the course emphasizes understanding how analytics and AI systems work, what their outputs mean, and how those results should be interpreted responsibly in real-world contexts.
Learners explore how data is collected, prepared, analyzed, and interpreted using both traditional analytics methods and modern AI approaches. The course highlights the differences between explainable analytics techniques and more complex AI systems, helping professionals understand the trade-offs between transparency, capability, and uncertainty.
By the end of the course, participants will be better prepared to commission and interpret analytics outputs, evaluate insights critically, and communicate findings clearly to support informed business decisions.
What You’ll Learn
In this course, you will develop a practical understanding of how analytics and AI systems produce insights and how those insights can inform better decisions.
Key learning areas include:
- The differences between data, information, and actionable insights
- How data is collected, structured, and prepared for analysis
- The role of databases and queries in extracting and organizing data
- How analytics techniques such as clustering and classification uncover patterns
- How neural networks, generative AI, and embeddings interpret complex information
- The differences between transparent analytics models and “black-box” AI systems
- How APIs, infrastructure, and cloud systems support modern AI tools
- How to critically interpret analytics and AI-generated outputs
- Ethical considerations and responsible use of data and AI
- How to communicate insights effectively to non-technical stakeholders
These concepts help professionals strengthen their ability to use analytics and AI insights to guide decision-making across organizations.
Who Should Register?
This course is designed for professionals who regularly use analytics or AI outputs to inform decisions, supervise and collaborate with analysts or technical teams, or evaluate data-driven work, but who do not need to build or implement models themselves.
Curriculum
8 Week Course
Data Foundations
- Distinguish discrete, continuous, structured, and unstructured data types.
- Identify characteristics that make data “big” and how scale influences how data is managed.
- Describe common methods used to collect data, including basic web crawling approaches.
- Distinguish between quantitative and qualitative data and identify when each type is used.
Preprocessing, Storage & Retrieval
- Identify common data quality issues and appropriate cleaning strategies.
- Identify approaches used to detect and handle outliers.
- Describe the purpose and basic structure of relational databases.
- Interpret what happens conceptually when data is queried for analysis.
Unsupervised Learning & Clustering
- Interpret where clustering fits within the broader landscape of analytics methods.
- Distinguish between unsupervised and supervised learning approaches.
- Describe how k-means clustering works at a conceptual level.
- Identify appropriate use cases and limitations of clustering in decision contexts.
Supervised Learning & Classification
- Describe the supervised learning process, including training, testing, and prediction.
- Distinguish between classification and regression tasks.
- Interpret the logic of decision trees and ensemble methods at a high level.
- Evaluate classification results using common performance measures.
Neural Networks, Generative AI & Embeddings
- Describe the basic components of a neural network at a conceptual level.
- Explain what embeddings are and how they represent meaning.
- Describe how qualitative data can be processed by AI systems.
- Evaluate the implications of using black-box models for interpretation and accountability.
APIs, Local Models & Infrastructure
- Describe what happens when an AI system is accessed through an API.
- Distinguish between cloud-hosted and locally deployed AI models.
- Explain why specialized hardware is used for AI workloads.
- Ask informed questions about cost, scale, and data security.
Extracting Meaning & Integrating Methods
- Explain how clustering groups similar items using embeddings rather than raw numerical data.
- Identify opportunities to integrate qualitative and quantitative analysis.
- Evaluate prompt design choices that improve AI-generated outputs.
- Evaluate whether AI-generated responses meaningfully address a given question.
Interpretation, Communication & Commissioning
- Interpret analytical and AI outputs critically, including identifying limitations.
- Communicate findings effectively to non-technical stakeholders.
- Assess ethical implications of data collection and data analytics.
- Evaluate the quality of analytics work produced by conventional or artificial intelligence methods.
If you’re going to pay by credit card, you can either get started with installment payments or pay in full. Just let your enrollment representative know the option that work best for you.
Corporate and Military Tuition Assistance
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Corporate and Military TA
Corporate tuition assistance is paid by your employer. You will need to provide appropriate forms for processing, prior to enrollment. Air Force tuition assistance is available for active-duty service members. You will need to provide a valid military tuition assistance voucher. Both TA options are subject to employer benefit policies.
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Deferred Corporate TA
Pay tuition now and have your employer reimburse you. Additional documentation will be needed to process this payment. Subject to employer benefit policies.
Military Benefits
Active Duty
Michigan State offers a 15% savings, per certificate course, to active-duty servicemembers, Guardsmen and Reservists (upon verification of military status).
Veterans
Michigan State offers a 15% savings, per certificate course to veterans (upon verification of military status).
Spouses and Family
Michigan State offers a 15% savings, per certificate course, to active-duty servicemembers, veterans, Guardsmen, Reservists and their spouses and dependents (upon verification of military status).
If you are interested in learning more about the steps you need to complete payment, please contact a Student Success Representative. This reduction is valid off the standard tuition fee rate of the listed courses offered by Michigan State University with online administration by Bisk. This reduction is not stackable with other reductions, and you may not use this reduction in conjunction with other reductions.
