Applied Data Modeling – Intermediate Level with Guided Mentorship

This is a mentor-led, application-focused Data Modelling programme for professionals who already understand basic data concepts and want to strengthen their modelling skills, design thinking and architectural judgement.

The programme focuses on conceptual, ER and dimensional modelling, using real-world business scenarios, guided discussions and selected modelling exercises. Emphasis is placed on design decisions, business alignment, modelling alternatives and architectural trade-offs, rather than learning specific modelling software.

Advanced modelling approaches such as Data Vault 2.0, Graph Modelling and NoSQL Modelling are introduced at an awareness level to provide broader industry context.

The objective is to help participants understand how experienced Data Modellers and Data Architects approach modelling challenges in enterprise environments.

🎯 Who This Course Is For

✅ Data Analysts, Data Engineers and BI professionals

✅ Professionals working with databases, data warehousing or analytics

✅ Aspiring Data Modellers and Data Architects seeking stronger modelling fundamentals

✅ Professionals who understand basic data concepts but want to improve their modelling judgement

⚠️ Not suitable for complete beginners.

📚 Course Format

🔹 Mode: 1:1 Guided Mentorship

🔹 Duration: 12 sessions (60 minutes each)

🔹 Tools: Excel / draw.io for illustrating modelling concepts and exercises

🔹 Approach: Conceptual discussions, business scenarios, independent exercises and guided reviews

🔹 Focus: Architecture thinking, modelling principles and design decisions

💡 Important: Participants may use their preferred modelling tools independently. However, this programme does not provide tool-specific training in Erwin, ER/Studio or similar software.

🧠 What You Will Learn

✔️ Translate business requirements into conceptual and logical data models

✔️ Understand entity identification, relationships, keys and cardinality

✔️ Apply normalisation principles and evaluate design alternatives

✔️ Understand dimensional modelling, including facts, dimensions and grain

✔️ Evaluate star and snowflake schema design approaches

✔️ Identify common modelling mistakes and architectural trade-offs

✔️ Understand how data models support enterprise data architecture and analytics

✔️ Gain awareness of semantic layers and selected advanced modelling approaches

🛠️ Applied Learning and Exercises

The programme includes two focused modelling case studies.

📌 Case Study 1 - ER Modelling

• Understanding business requirements

• Identifying entities, attributes and relationships

• Discussing conceptual and logical modelling approaches

• Evaluating keys, cardinality and normalisation decisions

• Reviewing modelling assumptions and design alternatives

📌 Case Study 2 - Dimensional Modelling

• Understanding analytical requirements

• Identifying facts, dimensions and measures

• Determining the appropriate grain

• Evaluating star and snowflake schema designs

• Discussing common dimensional modelling challenges

🔍 Guided Model Reviews

Participants complete selected exercises independently between sessions.

Reviews focus on modelling logic, design quality, business alignment and architectural reasoning, rather than step-by-step software operation.

🚫 What This Programme Does NOT Include

❌ Erwin, ER/Studio or other modelling software training

❌ Step-by-step tool configuration or software demonstrations

❌ Extensive hands-on model development during live sessions

❌ Coding, database implementation or platform administration

❌ Full-scale implementation projects or capstone development

❌ Job placement guarantees or Data Architect certification

💡 The emphasis is on understanding why a modelling approach is appropriate, not simply how to create a model using a particular tool.

📘 Workshop Resources

Participants receive:

📄 Course Presentation (PDF) - Presentation material used during the sessions

📋 Case Study Material - Business scenarios used for ER and dimensional modelling discussions

📚 Further Reading - Selected references for continued independent learning

🧭 Mentor Feedback - Guidance on modelling approaches, design decisions and areas for improvement

🏅 Certificate of Participation - Provided upon successful completion of the programme. This acknowledges participation and does not constitute professional certification.

🌟 Expected Learning Outcomes

By the end of the programme, participants should be better equipped to:

✅ Interpret business requirements from a data modelling perspective

✅ Evaluate conceptual, logical and dimensional modelling approaches

✅ Explain and justify common modelling decisions

✅ Recognise design trade-offs and potential modelling issues

✅ Understand how data modelling fits into broader enterprise data architecture

✅ Approach modelling discussions with greater confidence and independence

The objective is to develop stronger modelling judgement and architectural thinking through guided mentorship, real-world perspectives and selective practical application.