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Program overview
Online
On-site
Hybrid
Cursor for Data and ML Engineering
Cursor applied to data work rather than application development. Covers notebook and exploratory workflows, pipeline and SQL generation with proper review, data quality testing, documentation and lineage, and the guardrails that matter when a wrong query is expensive rather than just broken.
Duration:
2 Days
Level:
Intermediate
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1500+ users onboarded
Who will Benefit from this Training?
Training Objectives
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
Download Curriculum
- Cursor in a Data Workflow
- Setting up Cursor for notebooks, scripts and analytics repositories
- Environment, dependency and kernel considerations
- Indexing a data project so context is actually useful
- Where AI assistance helps in data work and where it misleads
- Hands-on: configure Cursor against a real data repository
- Exploratory Analysis and Notebooks
- Accelerating initial exploration of an unfamiliar dataset
- Generating transformations and iterating on them quickly
- Plotting, profiling and summarising with AI assistance
- Keeping analytical reasoning yours rather than delegating judgement
- Hands-on: profile and explore a dataset end to end
- Building and Refactoring Pipelines
- Generating ETL and ELT pipeline code from a specification
- Refactoring legacy transformation logic safely
- Working with transformation frameworks and model files
- Feature engineering code and reusable transformation patterns
- Hands-on: refactor an existing pipeline with test coverage first
- SQL Generation and Review
- Generating queries from a natural language description
- Reviewing generated SQL for correctness, not just syntax
- Join logic, granularity and the errors that produce plausible wrong numbers
- Query cost, scan volume and performance considerations
- Hands-on: review a set of generated queries and find the broken one
- Testing and Data Quality
- Generating data tests alongside transformations
- Schema validation, constraints and freshness checks
- Test fixtures and representative sample data
- Catching silent failures that pipelines do not surface
- Hands-on: add a quality test suite to an untested pipeline
- Documentation, Lineage and Guardrails
- Generating pipeline, dataset and model documentation
- Column descriptions, lineage notes and handover artefacts
- PII, sample data and what must never reach a prompt
- Review standards for AI-assisted data work
- Hands-on: document a pipeline and agree team guardrails
Hands-on Experience with Tools
Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
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100% Live
on-site/online training
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Hands-on
Labs and capstone projects
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Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
1
Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
3
Why this course
- Built for data work: Notebooks, pipelines and SQL rather than generic application development examples.
- Generated SQL needs scrutiny: A wrong query is expensive and often silent. Learn what to check every time.
- Documentation that finally happens: Pipeline and model documentation produced as a by-product rather than a backlog item.
- Quality built in: Data tests and validation generated alongside the transformations they protect.
Training objectives
- Configure Cursor for data projects, notebooks and analytics repositories
- Accelerate exploratory analysis without losing analytical rigour
- Generate and refactor ETL and ELT pipeline code
- Produce and critically review SQL and transformation logic
- Build data quality tests, schema validation and fixtures
- Generate pipeline, model and dataset documentation
- Apply guardrails around PII, sample data and expensive queries
- Establish review standards for AI-assisted data work
Who will benefit
- Data engineers and analytics engineers
- Machine learning engineers and data scientists
- BI developers working in code-based transformation tools
- Analytics teams adopting AI-assisted development
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Frequently Asked Questions
2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.