Data AnalyticsBeginner
SQL for Analysts: From Joins to Insight
Master querying, window functions, and metric design, then turn results into dashboards stakeholders trust.
Courses
Eight applied, mentor-led courses across four tracks. Small cohorts, real projects, and feedback on the work you actually ship.
The catalogue
Pick a course, not a playlist
Every course runs as a fixed-length cohort with weekly mentor reviews. Filter by track to find your next step, then enrol when a start date opens up.
Data AnalyticsIntermediate
Analytics Storytelling with Python & Tableau
Go from messy CSVs to a polished, decision-ready narrative using pandas, charts, and honest framing.
Data EngineeringIntermediate
Building Batch & Streaming Pipelines
Design dbt models, orchestrate with Airflow, and stream events through Kafka into a warehouse you can trust.
Data EngineeringAdvanced
Lakehouse Foundations: Spark & dbt in Practice
Architect partitioned, well-tested data lakes and tune Spark jobs that stay fast as volumes grow.
ML EngineeringIntermediate
Productionizing ML Models
Package, serve, and monitor models with feature stores, CI/CD, and drift alerts that catch problems early.
ML EngineeringAdvanced
MLOps on Kubernetes End to End
Stand up reproducible training, model registries, and scalable serving on a real K8s cluster.
AIIntermediate
LLM App Engineering: RAG, Agents & Evals
Build retrieval pipelines, tool-using agents, and rigorous evaluations so your AI features stay reliable.
AIAdvanced
Fine-Tuning & Evaluating Open Models
Adapt open-weight LLMs with LoRA, build evaluation harnesses, and ship cost-aware inference.
Four tracks
How the catalogue is organised
Each course belongs to one of four tracks. Tracks share a teaching style but aim at distinct roles, so you can specialise without losing sight of the bigger picture.
Data Analytics
Turn raw tables into decisions with SQL, dashboards, and clear analytical storytelling.
Data Engineering
Build the pipelines, warehouses, and orchestration that keep data flowing reliably at scale.
ML Engineering
Take models from notebook to production with solid training, serving, and monitoring practice.
AI
Design, evaluate, and ship LLM-powered applications and agents that actually hold up in the wild.
Learning paths, not loose courses
A single course teaches a skill. A learning path takes you from where you are now to a role you can actually apply for. We sequence courses so each one builds on the last, and your mentor keeps an eye on the whole arc — not just this week's assignment.
Most people start with one course to find their footing, then stack two or three over a few months. Here is how the pieces usually fit together:
- ✓Start with fundamentals. Beginner and intermediate courses give you the language and habits the harder material assumes.
- ✓Specialise deliberately. Pick a track and go deep, or bridge two when your target role sits between them.
- ✓Finish on production. Advanced courses end with the unglamorous ops work — serving, monitoring, testing — that actually gets people hired.
- ✓Carry a portfolio. Every course leaves you with reviewed, real work you can point a hiring manager to.
6,400+learners since 2021
92%cohort completion rate
40+practitioner mentors
4.8/5average course rating
From the cohorts
What finishing one of these looks like
The honest version, in graduates' own words.
I came in writing clumsy SELECT statements and left building dashboards my whole team relies on. The mentor feedback on real queries was the part that stuck.
Data Analyst, fintech
The pipelines course was hard in the best way. We broke things, fixed them, and I finally understood why orchestration matters. Landed my first DE role six weeks after finishing.
Junior Data Engineer
Most courses stop at the notebook. Smart Power pushed me through serving, monitoring, and the boring-but-critical ops work that actually gets you hired.
ML Engineer
Before you enrol
Common questions
Not strictly. Each course lists a level so you can gauge the jump. If you are new to a track, start at beginner or intermediate; advanced courses assume you are comfortable with the fundamentals that come before them.
Budget six to ten hours a week, including the live mentor review. Cohorts are paced so you can hold down a job alongside them, but the project work is real and the deadlines are real too.
The listed price covers the full cohort: all sessions, weekly one-to-one feedback on your submissions, the project materials, and access to the alumni channel afterwards. There are no upsells once you are in.
Yes. A lot of our learners are sponsored. Email our team and we will send a proper invoice and a short summary of outcomes you can forward to whoever signs off.
Still deciding which course is right?
Tell us where you are and where you want to be. We will point you at the course — or the path — that fits, with no pressure to sign up.