Public course framework¶
IT4065C: Data Technologies Administration. This edition uses an open, local Ubuntu, PostgreSQL and dbt environment to teach governance, lifecycle, modeling, access control and evidence-based decisions. SQL/Python foundations help; no institutional account is required. Begin with the setup guide.
Institutional context¶
IT4065C is a University of Cincinnati School of Information Technology course. This independently maintained repository supports instruction and is not an official University syllabus or policy statement. University documents and course systems control in the event of inconsistency. Read the full institutional context and instructional authorship notice for attribution, ownership and licensing boundaries.
Optional Python preparation¶
If you are new to Python or would like a refresher, start with Isaac K. Nti's Introduction to Python for Information Technology. This beginner-friendly course includes 12 modules with guided notebooks and practice in variables, conditionals, loops, lists, functions and interpreting errors.
Follow its quick-start guide. New learners can work through the modules in order; returning learners can review the topics they need. The course provides approximately 12 hours of guided instruction, with additional time for independent practice. This is optional preparation, not a formal prerequisite or an additional graded requirement for IT4065C. Use the local foundations bridge for this course's terminal, SQL and database identity self-checks.
Official course description and learning outcomes¶
For the current official course description and University-issued course-level learning outcomes, see the University of Cincinnati School of Information Technology course descriptions and search for IT4065C — Data Technologies Administration. Those outcomes are referenced rather than reproduced here.
University-added AI governance outcome¶
The AI ethics and governance course-level outcome, identified here as SLO 6, was added by the University of Cincinnati after the repository's initial development. Isaac K. Nti developed the repository activities addressing it, not the outcome. As checked on 2026-09-18, the public UC course-description page lists the earlier five outcomes and does not yet display this addition. Consult the current official course materials for the authoritative outcome wording and applicable offering.
The repository addresses this outcome through Lab 7 and the capstone.
The module descriptions and module-level outcomes are instructor-developed; see institutional context and instructional authorship. The official institutional syllabus and LMS govern course administration.
Sequence and scope¶
Governance/classification → modeling → lifecycle → infrastructure comparison → access security → monitoring → integrated governance/AI → capstone defense. See the lab map and assessment map. Lab 7 is required evidence for the added AI governance outcome. Labs 8–11 extend retention, infrastructure, heterogeneous ingestion and quality-gated publication. Optional Labs 12–15 extend server audit, two-instance batch recovery, TLS/rotation and governance applicability. They do not change the seven-lab core.
The local toolchain provides open exercises alongside selected external learning. The instructor uses Alation University’s Data Intelligence Project and the optional Snowflake Data Warehousing Workshop (Module 4) as supplements; see the platform and access guide. The local sequence does not claim to assess product-specific Alation/Snowflake proficiency. Single-instance snapshot and client-observed monitoring exercises have explicit limits. Module 4 emphasizes infrastructure administration, including residency, sovereignty and cost. Instructors specify the assessed encryption, catalog and orchestration activities for their offering; the public lab index identifies optional extensions.
Keep credentials, student work, attendance, accommodations, current deadlines and institutional administration in the private course system. This public framework does not replace the official current institutional syllabus.
Disclose AI assistance and independently verify results. Never upload secrets or personal records to an AI service. Assessment emphasizes reasoning and demonstrated understanding rather than an unverifiable percentage of AI-generated writing.
See the detailed syllabus crosswalk for module-level coverage, optional extensions and assessment responsibilities.
Author: Isaac K. Nti. Citation and reuse terms.