MSEM Data Analytics Specialization
Technical Managers are Critical in Organizations
Technical data analytics managers must answer crucial questions such as:
Does the organization’s architecture for a critical data analytics model support the addition of a real-time data feed?
What is the effort and work hours needed to design and implement a new data analytics model for a business partner?
The company’s scientists have proposed a solution, but the system has significant runtime limitations; can this be worked around?
Managing in technical industries has grown increasingly complex over time. Business concerns often have many layers and competing objectives. In reality, even the best organizations often solve critical business problems with intuition that may not necessarily be fact-based. The Masters in Engineering Management with an emphasis in Data Analytic provides the technical knowledge and training to solve critical business questions with a scientific foundation, and guide your organization with thoughtful, calculated insights.
MSEM Data Analytics program outcomes
Data Models: Provide guidance on modeling paradigms in advanced data analytics to support solution design, communicate technical concepts to senior leadership, and evaluate and manage internal and external teams for successful execution.
System implementation: Guide core strategic infrastructure and software decision-making in the data analytics spaces to support the organization in a rapidly evolving technology landscape. Understanding of data architecture and governance principles.
Leadership: Manage projects and personnel in engineering and high tech spaces, including alignment with strategic initiatives, risk assessment, deadlines and deliverables, driving and determining KPIs, and ensuring ROI. Ethical and legal considerations in data analytics.
MSEM Data Analytics 3 Course Specialization
This three course series covers primary topics in data science and data engineering taught from the lens of a middle manager in Technology or Engineering. Students will be exposed to a broad range of topics in all areas of the data analytics ecosystem, with the end goal of advising and managing data analytics programs and projects. Learning items can be categorized as:
- Students will gain an understanding of data architecture concepts (cloud-based and on-premises) in order to advise executives on strategic system decisions and guide implementation teams on the architectural impacts to analytics initiatives.
- Students will learn key data engineering concepts and tools, including ETL pipeline management, enabling them to assess technical feasibility, advise on critical data flow design, and bridge communication between leadership and technical teams.
- Students will develop foundational knowledge in data science, machine learning, and AI methodologies, equipping them to evaluate their applicability to business challenges. Students should be able to advise technical teams and executives on the tractability of various modeling approaches and advise on the supporting infrastructure and talent required.
- Students will build skills in visualizing and communicating complex analytics systems and model outputs. Presentations should be clear and relevant to all strategic stakeholders, both technical and executive.
A detailed list of learning items for each of the above categories is given below. This does not follow the course sequence, as topics will be presented throughout the course in an integrated manner.
- Data Architecture / Governance Learning Items
- Understand the structure and purpose of different data architectures:
- Data Mesh, Lakehouse, Data Fabric
- Cloud vs On-Prem vs Hybrid
- “All-in-one” vs modular systems
- Evaluate trade-offs in architectural decisions:
- Cost models (subscription, per transaction)
- Tool integration (Google BigQuery, Vertex AI, Looker, etc.)
- Performance, scalability, and system complexity
- Understand roles/responsibilities in the analytics ecosystem:
- Data Engineer vs Analyst vs Scientist
- Ownership and interaction across architecture components
- Design architecture for specific analytics/modeling needs:
- How descriptive, predictive, and prescriptive models affect architecture
- Memory, compute, and scheduling considerations
- Governance principles:
- Data quality (accuracy, consistency, completeness)
- Metadata management
- Data lineage and cataloging
- Security and compliance (e.g., GDPR)
- Present and defend architectural decisions to stakeholders
- Data Engineering Learning Items
- Build and manage ETL pipelines:
- Bulk and stream ingestion using BigQuery, Dataflow
- Data staging and transformation
- Hands-on experience with: BigQuery, pandas, NumPy, Google Cloud Storage
- Code in Python and SQL to manipulate and transform data
- Implementation in both Python, SQL and a combination of the two
- Data structures (lists, dictionaries, arrays)
- I/O operations and memory management
- Transformation operations: pivoting, cleaning, filtering, aggregating, standardizing
- Automate workflows:
- Orchestrate data refresh
- Scheduling with Google Cloud Composer
- Integrate custom algorithms into ETL flows
- Monitor and manage data pipelines:
- Payload volume, failure tracking, performance metrics
- Alerts and logging based on system and data behavior
- Data Science / Machine Learning/AI Learning Items
- Understand types of models and their use:
- Descriptive, Predictive, Prescriptive
- Learn and apply machine learning techniques:
- Regression (Linear, Logistic)
- Classification (Confusion matrix, Precision/Recall)
- Clustering (K-Means, PCA)
- Decision Trees, Neural Networks, Bayesian Networks
- Special topics (time series forecasting, NLP, pricing models)
- Evaluate model performance:
- Accuracy, assumptions, limitations
- Model drift, monitoring over time
- Apply models in practice:
- Build and evaluate ML models using Scikit-learn and Python
- Use Vertex AI notebooks and workbench for deployment
- Store and use ML features in BigQuery
- Integrate models into ETL flows:
- Design AI-driven solutions:
- Learn the algorithms behind AI
- Architectural, operational, and ROI impacts of AI
- Data Communication / Visualization Learning Items
- Best practices for data visualization
- Choose appropriate chart types for various data stories
- Creating visually appealing and appropriate graphics
- Visualize data using: Python, Looker Studio, and/or Power BI/Tableau
- Prepare executive summaries of data projects
- Present to “executives” using clear, concise narratives
- Explain architecture and model decisions with visual support
- Communicate technical details and assumptions:
- Describe model accuracy, evaluation metrics, limitations
- Defend model/architecture choices in presentations and discussions
- Create business cases for AI integrations, including ROI and implementation plans
|
Semester |
Course ID |
Course Title |
Credits |
Description |
|
Summer |
TBD |
Foundations of Data Analytics & Pipeline Management |
3 |
TBD |
|
Fall |
TBD |
Applied Data Analytics Modeling for Managers |
3 |
TBD |
|
Spring |
TBD |
Advanced Data Analytics, AI and Governance for Managers |
3 |
TBD |
Ready to Take the Next Step?
Whether you’re aiming for career advancement, leadership opportunities, or a stronger foundation in engineering management, MSEM is built for you.