# ECBS5256 – Managing Data Science Teams (Draft) --- ## 1\. Course Information - **Course Code & Title:** ECBS5256 – Managing Data Science Teams - **Program:** MS in Business Analytics (MSBA) - **Instructor:** Eduardo Ariño de la Rubia, Professor of Practice - **Email / Office Hours:** By appointment, D-207 - **Meeting Pattern:** Two-day intensive, in person - **Dates:** Monday **March 16** and Monday **March 23** - **Class Blocks (each day):** - Block A: 11:00–12:40 - Block B: 13:30–15:10 - Block C: 15:30–17:10 - **Location:** CEU Vienna Campus, Room B-421 - **ECTS:** **TBA** - **LMS:** Moodle --- ## 2\. Course Description (Short) This course treats **management as praxis** for analytics leaders. Students build and practice a lightweight **Manager Operating System (OS)** for small analytics organizations, then learn how to adapt it for **small, medium, and large** company contexts. Emphasis on structured hiring, roadmapping & executive alignment, personal growth and performance with hands-on practice (SBI feedback, mock calibration), and cross-functional collaboration. In the age of Artificial Intelligence, all teams are hybrid human/AI teams, and everyone manages someone. This course will touch on the changing nature of management in the age of AI. The capstone is an **async QBR video** where students present their roadmap to a virtual executive panel. --- ## 3\. Learning Outcomes By the end of the course, students will be able to: 1. Construct a concise **Manager OS** (cadences, rituals, artifacts, decision hygiene). 2. Produce a **structured hiring packet** (JD, work sample, rubric, loop) that improves signal and fairness. 3. Build a 12-month **roadmap** tied to business outcomes and write a 2-page **executive narrative**. 4. Create a **Personal Growth Plan (PGP)** and a calibration-ready performance summary. 5. **Deliver structured feedback** using the SBI model and conduct **evidence-based performance calibration**. 6. **Align leadership** and collaborate cross-functionally using clear interfaces (RACI, decision memos, SLAs). --- ## 4\. Prerequisites & Target Audience - Suitable for MSBA students interested in product/analytics leadership. - No coding required; familiarity with basic analytics concepts (metrics, experiments) is helpful. --- ## 5\. Teaching Methodology - **Workshops & Role-plays:** interview loops, difficult conversations, QBR panel. - **Hands-on templates:** team charter, stakeholder map, RACI, decision memo, roadmap (RICE), PGP, performance summary. - **Red-team critiques:** structured peer feedback to stress-test assumptions. --- ## 6\. Readings ### Recommended (with specific chapters) All readings are recommended, not required. The course is designed so you can participate fully without them — but they will deepen your understanding. **Recommended before Day 1 (March 16):** - Camille Fournier — *The Manager’s Path*, Ch. 1–3. 1:1s, hiring basics, team management. (~70 pp) - Andy Grove — *High Output Management*, Ch. 4 ("Meetings"). The intellectual foundation for Manager OS. (~30 pp) **Recommended before Day 2 (March 23):** If time is limited, prioritize Grove Ch. 11 and Fournier Ch. 6 — these directly support the Day 2 exercises. - Camille Fournier — *The Manager’s Path*, Ch. 6 ("Managing a Team"). Managing up, performance. (~30 pp) - Andy Grove — *High Output Management*, Ch. 11 ("Performance Reviews"). Foundation for calibration. (~30 pp) - Will Larson — *An Elegant Puzzle*, Part 2 ("Organizations") & §5.3 ("Presenting to Senior Leadership"). Org design at scale and roadmap communication. (~80 pp) ### Further Reading - Matthew Skelton & Manuel Pais — *Team Topologies* — enriches Block A org design thinking - John Doerr — *Measure What Matters* — OKR context for Block C (read with caveats about mechanical goal-setting) - Forsgren, Humble, Kim — *Accelerate* — delivery and reliability metrics; enriches roadmap thinking - NIST AI RMF 1.0; Microsoft Responsible AI Standard — reference documents for AI governance context --- ## 7\. Assessment & Grading All artifacts compile into a single **Manager Portfolio** (PDF/repo). **Portfolio Submission Deadline:** **Sunday, March 30 at 23:59 CET.** ### Weights | Component | Description | Due | Weight | | :---- | :---- | :---- | :---- | | Participation & Preparedness | Active presence in discussions, role-plays, class discussions, infrastructure activity, red-team critiques. | Ongoing | 15% | | **Day 1 Checkpoint (drafts)** | **Team Charter \+ Stakeholder Map \+ Roadmap Outline (RICE sketch)** — pass/fail on completeness. | End of Day 1 | 10% | | Hiring Packet (final) | JD, work sample, rubric, interview loop. | Portfolio | 20% | | Roadmap \+ Exec Narrative (final) | 12-month roadmap \+ 2-page narrative. | Portfolio | 15% | | QBR Outline \+ QBR Video | QBR outline \+ 5-min recorded presentation. | Portfolio | 10% | | Manager OS (final) | 2–4 pages: operational playbook synthesizing charter \+ stakeholder map into cadences, artifacts, KPIs, and decision hygiene. | Portfolio | 15% | | PGP \+ Performance Summary (final) | Growth plan \+ calibration-ready performance summary. | Portfolio | **15%** | ### Milestones & Deadlines - **End of Day 1:** Submit **Charter \+ Stakeholder Map \+ Roadmap Outline (draft)**. - **Day 2 (async):** **QBR Video** (5-min recorded presentation of roadmap & narrative). - **Wednesday, March 25 at 23:59 CET:** Submit **peer feedback** (2 peers). - **Sunday, March 30 at 23:59 CET:** Submit **Manager Portfolio (final)**. **Late work:** Designed for in-class production; late submissions accepted only for documented reasons. --- ## 8\. Schedule & Topics ### Day 1 — Monday, March 16 **Block A (11:00–12:40) — The Manager’s OS for Analytics** - Interfaces: Manager vs. Tech Lead vs. PM; team topologies; rituals & artifacts (1:1s, reviews, decision logs) - Tools: Team charter, stakeholder map, RACI, decision memo **Hands-on:** Draft **Team Charter** & **Stakeholder Map** **Block B (13:30–15:10) — Hiring & Team Formation** - Role design, leveling basics; structured interviews; work-sample design; rubric calibration; candidate experience **Role-play:** Paired structured interview using your **scoring rubric** **Deliverable (portfolio):** JD \+ Work sample \+ Rubric \+ Interview loop **Block C (15:30–17:10) — Roadmaps, Bets, and Alignment** - RICE; guardrails vs. North Star; communicating trade-offs with Product/Eng; leadership narratives **Hands-on:** Build a 12-month **roadmap**; draft a 2-page **Exec Narrative** **Red-team:** Kill-the-project critique **Day-1 Checkpoint (by 17:10):** Submit **Charter \+ Stakeholder Map \+ Roadmap Outline** (draft) --- ### Day 2 — Monday, March 23 **Block D (11:00–12:40) — Growth & Performance** - Career ladders; **PGPs**; SBI feedback model; performance cycles & calibration; ethical PIPs **Role-play:** SBI feedback practice with scenario cards (pairs, 2 rounds) **Exercise:** Mock calibration session — rate 3 ambiguous profiles, reach group consensus **Homework:** Draft **PGP** at home using template and starter template (growth areas \+ development actions) **Block E (13:30–15:10) — Infrastructure & Cross-Functional Interfaces** - Cross-functional interfaces and bidirectional SLAs; the canonical data stack; stack blueprints by org size; build vs. buy framework; RFP process overview; IT and procurement realities; privacy and governance basics **Activity:** Data Infrastructure One-Pager (30 min) — sketch your case context's data stack, identify gaps, propose one change with build-vs-buy reasoning **Block F (15:30–17:10) — Leading Up & Executive Communication** - Executive communication frameworks (Pyramid Principle, BLUF); communicating failure \+ class discussion; first 90 days \+ class discussion; async QBR briefing; portfolio checklist; course close --- ## 9\. Deliverables (Portfolio Checklist) ### Required (6 artifacts) 1. **Manager OS Document** (2–4 pages) — your operational playbook, synthesizing your team charter and stakeholder map into a single framework covering cadences, rituals, artifacts, and decision hygiene (Block A) 2. **Hiring Packet** — JD, work sample, rubric, interview loop (Block B) 3. **Roadmap \+ Executive Narrative** — 12-month roadmap with RICE, risk register, and 2-page narrative (Block C) 4. **QBR Outline \+ QBR Video** — QBR outline and 5-minute QBR video (Block F) 5. **Personal Growth Plan** (Block D) 6. **Performance Summary** (1-2 pp) — calibration-ready assessment of a hypothetical direct report from your case context. Use behavioral evidence and SBI framework to differentiate performance levels. (Block D) ### Optional (for a richer portfolio, not separately graded) 7. Data Infrastructure Blueprint (see `resources/data-infra-blueprint-template.md`) 8. RFP Scoring Matrix (see `resources/rfp-scoring-matrix-template.md`) 9. Decision Memo (Block A template) 10. RACI Matrix (Block A template) --- ## 10\. Workload & ECTS Mapping (**TBA by Program**) **Estimated student workload (subject to ECTS confirmation):** - **Contact hours:** \~10 (two intensive days) - **Reading & artifact drafting between days:** 12–16 - **Post-course portfolio polishing:** 8–12 - **Total:** \~30–38 hours --- ## 11\. Policies - **Attendance (intensive):** Presence for **both** days is expected; key activities occur in-class. - **Academic Integrity:** CEU policies apply. Cite sources for any external templates or AI assistance. - **Use of AI Tools:** Allowed for drafting/ideation; you are responsible for accuracy and originality. Note AI assistance in a short footer in each artifact. - **Accessibility:** Students requiring accommodations should contact the instructor and Student Services early. - **Communication:** All announcements via LMS; check notifications daily during the intensive. --- ## 12\. Instructor **Eduardo Ariño de la Rubia** is a data scientist and technologist focused on analytics leadership and practical ML. He has led teams across product analytics, infra, and applied research, and teaches in CEU’s MSBA. Office hours by appointment, D-207. --- ## 13\. Case Contexts (Choose one to anchor your artifacts) 1. **Small (0→1):** Seed-stage B2C app; messy logging; need basic analytics, experiments, and KPIs fast. 2. **Medium (1→N):** Series B marketplace; partial event tracking; privacy backlog; PMs want trustworthy self-serve metrics & experiments. 3. **Large (N→Scale):** Regulated enterprise; data-mesh initiatives; strict procurement/IT; leadership wants ROI on prior analytics investments. --- *ECTS credit value to be confirmed by program.*