Michigan State University: Master Certificate in Business Analytics and Applied AI

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.

AI-Assisted Analysis and Statistical Foundations

• Explore the goals, strengths, and limitations of AI-assisted analytics. Learn essential statistical concepts needed to accurately interpret results, including correlation vs. causation, prediction vs. interpretation, descriptive vs. predictive analytics, and statistical vs. practical significance.

Gaining Insight Through Data Visualization

• Create and evaluate visualizations using Excel, Power BI, and AI tools. Learn to interpret and critique commonly used charts, including histograms, scatter plots, box plots, and decomposition trees, and assess when visualizations are appropriate for the data.

Customer Segmentation Using AI-Assisted Clustering

• Learn when clustering methods such as k-means and hierarchical clustering are appropriate. Use AI-assisted tools to calculate and visualize customer segments and interpret resulting graphs and summary statistics.

Simple Linear Regression for Business Insights

• Assess when linear regression models are appropriate, interpret regression outputs, and understand common misinterpretations related to slope, R-squared values, and p-values.

Evaluating Multiple Regression Models

• Interpret AI-assisted multiple regression output, understand what it means to control for variables, evaluate when to include categorical inputs, and compare modeling approaches using training datasets versus full datasets.

Comparing Groups and Experimental Analysis

• Use A/B tests, t-tests, and ANOVA to compare outcomes across groups. Extend regression models to evaluate differences while controlling for other variables.

Interpreting Percentage Change Models

• Analyze models that estimate percentage changes rather than absolute changes. Interpret exponential, logarithmic, and power regression models and apply elasticity concepts to business scenarios.

Predicting Binary Outcomes and Customer Churn

• Analyze yes/no outcomes using decision trees and logistic regression. Estimate probabilities, interpret results, and compare model performance when predicting customer churn.

Module 1: Understanding AI and ML in the Workplace

• What are AI and ML; overview of automation, ML, deep learning, Gen AI, and agentic AI.

• How different AI types impact workplace culture and workforce management.

• Understanding the organization’s AI strategy and future vision.

• Real-world use cases across operations, marketing, sales, and decision support.

• Role clarity in AI governance.

• Importance of employee digital literacy and relevant frameworks.

Module 2: People and Process Transformation through AI/ML

- Readying the people: how functional management works.

• Leadership dilemmas: Kotter, Adair, Herzberg, Senge, Covey.

• Kolb’s Learning Styles questionnaire.

• Motivating employees to try new methods and processes.

• Data-driven decision-making.

Module 3: People Management in a Tech-Augmented Environments

• Leading change using frameworks like Kolb, PESTE, and organisational change types.

• Structuring goals, teams, and management systems in tech-enabled environments.

• Building trust and transparency in AI/ML adoption.

• Leading modern teams effectively.

Module 4: Developing of AI/ML Teams of the Future

• Building performing teams

• Three elements of delegation

• Performance management using control systems and leadership habits.

• Time and priority management using the Eisenhower Matrix.

Module 1: Understanding Technology-Driven Transformational Change

- Change Through the Ages: Change varies by individual, role, and context; Historical shifts through PESTLE and generational transitions; Organisation’s relationship with change maturity; Frameworks: Tannenbaum & Schmidt, Porter’s 5 Forces, McKinsey 7S; Strategic Lenses: Generic Strategies and Value Discipline Model

- Force Field Analysis: Understanding forces driving and resisting change; Applying the model to people and process transformation; Overview of AI, ML, and emerging technologies; Current applications vs future possibilities; Increasing pace and scale of technological change

• Why do People (and Organisations) Resist Change?: Theories and perspectives: AA Milne, Mannheim, VUCA, Hunt; Comfort zones, perceived risk vs reward dynamics; Leadership role in navigating and reducing resistance; Root cause analysis: cause–effect and Fishbone methods; AI as a continuum: automation to ML, deep learning, Gen AI, agentic AI; Impact of AI types on strategy, culture, and workforce dynamics; Mapping AI use cases to business KPIs and value creation; Business impact areas: customer satisfaction, speed, quality, innovation

Module 2: People and Process Transformation Through Advanced Technology

• Why Don’t organizations change?: Six layers of change in business; Change frameworks: Lewin, ADKAR, Kotter’s 8 Steps, Kubler-Ross; Automation of routine tasks across functions; Identifying opportunities and associated risks; Role of data-driven decision-making; Use of tools, dashboards, and analytics systems

- Drivers of Change in organisations External and Internal Drivers: External vs internal drivers shaping transformation; Understanding impact of change across the organisation; Strategy at three levels: corporate, business, functional; Environmental and competitor analysis; Product lifecycle and portfolio analysis (BCG, 9-box); Scenario planning using MIT frameworks

Module 3: Management & People Bias that Delays Technical Progress

- Overcoming bias: Recognizing inherent flaws in human decision-making; Anchoring bias and its influence on judgments; Framing effect in strategic choices; Sunk cost fallacy in ongoing investments; Overconfidence and status quo bias; Backfire effect in resistance to new information; Labeling and semantic slanting in communication

Module 4: Implementing an AI/ML Strategy

- Implementing Organisation Change through AI/ML: Building open-system organisations and teams; Five fundamental IT shifts shaping transformation; Practicing agility in operations and decision-making; Tools: stand-up meetings, Kanban boards; Empowering employees and aligning with customer needs; Using risk as guardrails for innovation; Risk analysis and comfort zone re-evaluation

• Practice Design Thinking

- Innovate to Transform your organisation: Driving transformation through AI/ML-led innovation; Leveraging new and existing technologies; Building a customer experience strategy; Mapping and optimising the customer journey; Practicing disruptive innovation approaches; Applying McKinsey People Change model; Road mapping using sprint techniques and configurations; Enhancing frontline excellence through agile methods