Data Modelling for Analytics and AI Corporate Training
Instructor-led corporate training for teams that model data for analytics and AI.
Edstellar's Data Modelling for Analytics & AI program teaches your team to design analytical data models that power reliable dashboards and AI pipelines. Through guided hands-on labs, learners build star schemas, governed semantic layers, and ML-ready feature sets on realistic datasets, then finish with a capstone model your team can reuse in production.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a modelling-ready analytics team
The Edstellar DATA MODELLING Capability Model
Diagnose
Assess your current modelling maturity, data sources, and pain points so the program targets the gaps that slow your analytics.
Standardize
Align on naming, grain, and KPI definitions so every team models and measures the business the same way.
Practice in Labs
Build star schemas, SCD logic, and semantic layers hands-on until the patterns become team habit.
Measure
Track skill growth with lab checkpoints and assessments, plus quality improvements in models the team ships.
Scale
Leave with templates, standards, and a reviewed capstone model that new team members can build on.
Need more than one program?Get a group quote
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Program Overview: What is Data Modelling for Analytics and AI?
Data Modelling for Analytics & AI is an instructor-led corporate training program for analytics and data teams who design the models behind dashboards and machine learning. Over 24 hours, participants move from dimensional modelling foundations through source discovery, star schema construction, and semantic layer design, finishing with AI-ready feature engineering. Every practice module runs guided hands-on labs, so the team learns by modelling real datasets.
Instruction stays short and labs stay central: teams build star schemas, define conformed dimensions, and design metrics layers against realistic business data. Instructors review each model, challenging grain choices, slowly changing dimension strategies, and semantic definitions before the cohort advances. Feedback sessions compound across the week toward the capstone, where teams deliver a complete analytics data model. All exercises adapt to the organization's own sources and reporting needs.
Bad models tax everything downstream: conflicting metrics, dashboards that disagree, and AI features built on shaky joins. This program teaches teams to model deliberately, with clear grains, documented semantics, and designs that serve both BI and machine learning. They return as the people who settle metric arguments instead of starting them.
The capstone asks each team to produce an end-to-end analytics data model, from source discovery and dimensional design through star schema build and semantic layer, ready to feed both dashboards and AI features. Instructors interrogate it the way stakeholders will: is the grain right, do the metrics agree, and can it evolve. Graduates take home a modelling playbook, a metrics dictionary template, and a reviewed model they can extend into production.
Most analytics teams struggle with inconsistent models, conflicting KPIs, and datasets that are not ready for AI. This program fixes that at the source: disciplined modelling. Your team learns naming and grain conventions, slowly changing dimension patterns, validation practices, and documentation discipline that keep models trustworthy as data grows. The capstone delivers a reviewed, end-to-end analytics data model your team owns.
What's Included in this Workshop for Your Enterprise Teams
- 1
Live instructor-led sessions
Expert-led delivery with live demos and Q&A throughout
- 2
Guided hands-on labs
Structured lab exercises on realistic datasets in every practice module
- 3
Capstone build
End-to-end team build reviewed against a modelling rubric
- 4
Modelling templates
Reusable schema, semantic layer, and documentation templates
- 5
Assessment and feedback
Lab checkpoints, quizzes, and a graded capstone review
- 24 instructor-led hours
- Virtual, onsite, or hybrid delivery
- Cloud-based lab environments
- Cohorts of 8 to 20 learners
- Tailored to your data stack
- Post-program support window
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Data Modelling for Analytics and AI Course Topics and Program Outline
A 24-hour, 6-module instructor-led program. Learners build from dimensional modelling foundations through dimensional design, hands-on star schema labs, semantic layers, and AI-ready feature engineering, ending in a capstone end-to-end analytics data model reviewed by the instructor.
Built by Edstellar with data modelling and analytics expertise
Built by data modellers and analytics engineers who design warehouse models and AI pipelines in production, shaped around your stack and use cases.
We map your team's current modelling practices, data sources, and analytics goals to shape the lab focus for your cohort.
Instructor-led sessions teach dimensional design, schema patterns, and semantic modelling with live demonstrations and examples.
Guided hands-on labs where your team builds star schemas, SCD logic, and governed semantic layers on realistic datasets.
A capstone build: your team ships an end-to-end analytics data model, from source discovery to AI-ready feature sets.
Assess your team's maturity, learn the patterns, practice in guided labs, and ship a capstone analytics data model.
Delivered as
Delivered instructor-led over three to four days, virtual or onsite, with cloud lab environments for every learner.
Compare sample OLTP and OLAP datasets and sketch a first star schema on paper.
learna)Transactional systems vs analytical systems
b)Facts, dimensions, and the grain of a dataset
c)Star and snowflake schema patterns
d)When to model and when to keep it raw
Given a sample CRM extract, draft a dimensional blueprint for two business processes.
learna)Business process discovery workshops
b)Dimension tables, attributes, and hierarchies
c)Fact tables, measures, and additive rules
d)Conformed dimensions across processes
Build a full star schema with SCD type 2 history from a messy source extract, then run quality checks.
practicea)Building star schemas step by step
b)Slowly changing dimensions: types 1, 2, and 3
c)Handling nulls, defaults, and duplicate keys
d)Validating model quality before release
Design a governed semantic layer with certified KPIs and test query performance on a large dataset.
practicea)Semantic model architecture and layers
b)KPI definitions, hierarchies, and drill paths
c)Measures, calculated fields, and aggregation rules
d)Performance tuning for large models
Engineer a feature set from the star schema and document lineage for an AI use case.
applya)Feature engineering from warehouse models
b)Training and serving data consistency
c)Model-ready datasets and labelling discipline
d)Privacy, lineage, and documentation for AI
Deliver a complete, documented analytics data model with semantic layer and feature set for review.
applya)End-to-end source-to-model build
b)Semantic layer and KPI sign-off
c)AI-ready feature set packaging
d)Design review and documentation handover
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Your team leaves with practical modelling skills they can apply immediately to your warehouse, dashboards, and AI pipelines.
Dimensional Modelling Design
Design star and snowflake schemas with the right grain, facts, and dimensions for each business process.
Source Discovery
Run structured discovery on source systems to identify processes, entities, and modelling requirements.
SCD Implementation
Implement slowly changing dimension patterns that preserve history without breaking downstream consumers.
Semantic Layer Design
Build governed semantic layers with certified KPIs, hierarchies, and consistent aggregation rules.
Model Quality Validation
Validate models against quality checks for duplicates, nulls, and late-arriving data before release.
Feature Engineering for AI
Engineer reusable ML features from warehouse models with documented lineage and training-serving parity.
Modelling Documentation
Document models, KPIs, and lineage so teams can maintain and audit analytics assets confidently.
Teams that Benefit from this Data Modelling for Analytics and AI Group Training
This program is built for data engineering, BI, data science, and analytics teams that design or consume analytical models. Content adapts to each cohort's role and experience, so every team practices the modelling workflows it owns in its own data environment.
Build warehouse models that downstream teams can trust
Your data engineers learn dimensional design, SCD handling, and validation practices so pipelines land in clean, analytics-ready models.
Workflow friction
- Models that break when sources change
- Inconsistent definitions across pipelines
- Slow dashboards from weak modelling
How the program helps
- Production-grade star schema design skills
- Repeatable SCD and data-quality patterns
- Faster, more reliable pipeline handoffs
Practice use cases
- Star schema builds
- SCD implementation
- Model validation
- Warehouse refactoring
Ship dashboards on governed, trustworthy models
BI analysts learn semantic layer design and certified KPI definitions so every dashboard tells the same story from the same truth.
Workflow friction
- Conflicting KPI numbers across reports
- Fragile models under growing data
- Endless rework on metric definitions
How the program helps
- Certified KPI and measure design
- Semantic layers that business users trust
- Faster dashboard delivery cycles
Practice use cases
- Semantic layer design
- KPI governance
- Hierarchy design
- Performance tuning
Get ML-ready features from trusted models
Data scientists learn to engineer features from warehouse models with training-serving consistency, lineage, and documentation.
Workflow friction
- Feature drift between training and serving
- Unclear lineage for AI datasets
- Slow feature sourcing from raw data
How the program helps
- Feature engineering from warehouse data
- Consistent training and serving datasets
- Documented, auditable AI inputs
Practice use cases
- Feature engineering
- ML data pipelines
- Lineage docs
- Model monitoring
Standardize modelling across analytics teams
Analytics leads learn governance, naming, and review practices that keep models consistent as teams and use cases grow.
Workflow friction
- Inconsistent models across teams
- No review discipline before release
- Growing backlog of model fixes
How the program helps
- Shared modelling standards and reviews
- Clear ownership of model layers
- Scalable analytics operating model
Practice use cases
- Modelling standards
- Design reviews
- Team enablement
- Governance
Cohorts can mix functions or run role-specific, depending on how your teams collaborate on models.
Prerequisites: Familiarity with relational databases and basic SQL helps learners get more from the labs, but it is not required. The program starts from dimensional foundations and scales to advanced topics. No prior AI experience is needed. Content adapts to your team's tools and context.
Data Analyst to Analytics Engineer
Progression tracks how learners move from modelling basics to owning production-grade analytical models across the program's learn, practice, and apply phases.
One program that grows with data careers, from dimensional foundations to confident production modelling.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Choose the format that fits your team's locations and schedule. Every mode is instructor-led with the same labs and capstone.
Hybrid Training
Part of the cohort joins in a room while others join live online. Cloud labs keep everyone on the same exercises with the instructor coaching both groups.
- Live instructor for room and remote learners
- Shared cloud lab environments for all
- Flexible scheduling across time zones
- Same capstone and coaching standards
Onsite Training
The instructor delivers at your premises, which works well when the capstone should use your own data or when teams want face-to-face lab coaching.
- Delivered at your office or venue
- Capstone can use your own datasets
- Direct coaching during guided labs
- Team alignment sessions built in
Live Virtual Training
Fully live online delivery with cloud-based lab environments. Learners build models hands-on while the instructor demos, reviews, and answers questions in real time.
- Live instructor-led online sessions
- Cloud labs, no local setup needed
- Breakout coaching during exercises
- Recordings for post-session review
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Learn from industry experts.
Every session is led by a practitioner who has run monitoring for models in production, not a generalist working from slides.
Moinak
Tableau and Data Analytics Trainer in Gurgaon
9+ years of Experience
Tushar
Data Analytics Trainer in New Delhi
11+ years of Experience
Oluwadamilare
Data Analytics Trainer in Lagos
8+ years of Experience
Shilpi
Power BI Trainer in New
11+ years of Experience
Monushus
Data Analytics Trainer in Kanniyakumari
7+ years of Experience
Camille
Data Analytics Trainer in Birmingham
4+ years of Experience
Jethro
Data Science with Python Trainer in Mabalacat
7+ years of Experience
Trainers are matched to your stack and delivery window at scheduling. We confirm your assigned trainer, with a full profile, before the program is booked.
Frequently Asked Questions
Share your team size, location, and preferred dates through the request form, and we send a tailored proposal within one business day.
Yes. A short discovery call aligns the curriculum, labs, and dates with your team's modelling maturity and data stack.
Yes. The full program runs instructor-led and virtual, with cloud-based lab environments for every learner, no local setup needed.
Yes. Onsite delivery runs at your premises for teams that want in-person coaching through the guided labs and capstone.
The program is 24 instructor-led hours, typically delivered over three to four days depending on your team's schedule.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Modelling for analytics and AI builds on warehouse concepts, so they come first. With good models in place, your team can design a modern data architecture and then prepare that data for AI with checks on quality, lineage and consent.
Why Choose Edstellar as Your Data Modelling for Analytics and AI Corporate Training Provider
Edstellar is a corporate training company with 5,000+ certified trainers and 2,000+ courses delivered to global teams over 5+ years. Our analytics programs are instructor-led, practice-based, and tailored to your data stack, so your team models data the way your business actually uses it.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Modelling labs adapt to your warehouse stack and source systems, so practice reflects the data your team actually owns.
Proven Methodology
A diagnose, standardize, practice, measure, and scale arc guides teams from first schema sketch to governed production models.
Practice-Based Learning
Most classroom time is hands-on: your team builds schemas, semantic layers, and feature sets instead of watching slides.
Expert Trainers
Instructors are working data modellers who have shipped warehouse and AI-ready models in production environments.
Global Scale
Consistent instructor-led delivery across your regions, with cohorts sized so every team gets coaching during labs.
Measurable Outcomes
Pre and post assessments track modelling skill growth, and the capstone gives a reviewable, reusable team artifact.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Data Modelling for Analytics & AI Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Data Modelling for Analytics and AI Training for Your Team
Tell us about your team size, data stack, and goals. We will tailor the labs and capstone to your environment and send a proposal with dates, pricing, and delivery options.