Corporate Training
Technical training for finance, data and engineering teams, built around the Python, quantitative and AI workflows your people use every day.
Why train with Quantzivo
Close the gap between finance and code
Finance teams increasingly work in Python, notebooks and data pipelines, while engineering teams increasingly work with market data. Our training is designed for both sides, with exercises that look like the problems they actually solve.
- Role-specific content for analysts, researchers, engineers and data teams
- Hands-on exercises on public or sample datasets that mirror your workflows
- Methodological rigor around bias, validation, costs and risk
- Built from Academy courses, adapted to your team's level
Training areas
Six areas, combined to fit your team
Pick a single area for a focused workshop, or combine several into a longer upskilling program.
Professional upskilling
Structured programs that take staff from market and data fundamentals to confident, independent analysis.
- Market structure
- Data literacy
- Risk concepts
Python for finance teams
Replace fragile spreadsheet steps with reproducible Python, pandas and NumPy analysis.
- pandas
- Time series
- Visualization
Quantitative research training
Statistics, factor research, portfolio analytics and robustness testing with documented assumptions.
- Factor models
- Backtesting
- Robustness
Financial data engineering
Ingesting, validating, storing and serving market data, including corporate actions and audit trails.
- Pipelines
- Validation
- APIs
AI market analytics
Machine learning and NLP for market research, with time-series validation and clear limits on what models can tell you.
- NLP sentiment
- Feature engineering
- Validation
Custom technical workshops
Focused sessions on a specific tool, technique or problem your team is facing right now.
- Scoped with you
- Hands-on
- Focused
Who it suits
Teams and roles we design for
Training works best when it is pitched at a team's real starting point. These are the groups our content is built around.
Research and investment analysts
Move analysis into Python and test ideas with explicit, reproducible methods.
Risk and portfolio teams
Analyze exposures, drawdowns and scenarios with code instead of manual steps.
Software engineers in fintech
Learn the market concepts and data quirks behind the systems you build.
Data engineering teams
Build reliable market data pipelines with validation, storage and monitoring.
Data science teams
Apply time-series validation and leakage checks to financial modeling work.
Graduate and new-hire cohorts
Give incoming staff a shared foundation in markets, Python and research practice.
Delivery options
Choose how training is delivered
Formats can be mixed. Final options are agreed during scoping based on your team's location and schedule.
| Option | How it works | Best for |
|---|---|---|
| On-site workshops | Instructor-led sessions delivered at your office | Teams that learn best together in one room |
| Live virtual sessions | Instructor-led sessions run online with shared notebooks | Distributed teams across locations |
| Blended programs | Self-paced Academy lessons combined with live workshops and reviews | Longer upskilling around busy schedules |
| Team bootcamps | A private cohort working through one project with code reviews | Teams building a shared capability quickly |
| Custom workshops | Single focused sessions scoped around one problem or tool | Targeted needs and follow-up refreshers |
A typical engagement
How we work with your team
Discovery
We learn about your team's roles, tools, current skills and the outcomes you want.
Scoping
We propose topics, level, format and exercises, and refine them with you before anything is fixed.
Delivery
Training runs in the agreed format, with hands-on exercises and time for questions.
Review
We review participant feedback and project work with you and suggest sensible next steps.
How we teach
Rigorous methods your team can reuse
The aim is lasting capability, not a one-off demonstration. Participants practice methods they can apply to their own work after the session ends.
Reusable notebooks, code templates and checklists- Explicit coverage of bias, leakage, costs and risk
- Exercises on public or sample data, so no internal data is required
Educational training only. Sessions teach skills and methods. They do not provide investment advice, trading signals or recommendations.
Enterprise & institutional training
Engagements for teams and institutions
From a one-day executive workshop to a twelve-week enterprise program. Every engagement is scoped to your team, data and goals.
| Engagement | Length | Proposed fee (USD, per engagement) |
|---|---|---|
| 1-Day AI & FinTech Executive Workshop | 1 day | $2,500 |
| 3-Day Financial Data Engineering Workshop | 3 days | $5,000 |
| 5-Day Python & Quant Development Workshop | 5 days | $8,500 |
| 4-Week Corporate Quant Technology Training | 4 weeks | $15,000 |
| 8-Week Custom FinTech Engineering Program | 8 weeks | $25,000 |
| 12-Week Enterprise AI & Quant Training | 12 weeks | $40,000+ |
Enterprise fees are per engagement, not per participant. Cohort size, scope, travel and data licensing are defined in each proposal.
Corporate FAQs
Ask us directlyPlanning team training
Pricing depends on scope, format and group size, so it is quoted after a scoping conversation. Contact us with your team's needs to start that process.
By default, exercises use public or sample datasets designed to resemble your workflows, so no internal data needs to be shared. Anything beyond that is discussed during scoping.
Yes. Discovery helps identify starting points, and programs can include optional foundation material or separate tracks so nobody is lost or bored.
Both products are in development. Training currently uses standard Python tools; you can request a demo to follow the products as they progress toward early access.
Corporate training
Plan training around your team's real work
Tell us about your team, its current skills and what you want it to be able to do. We will follow up to scope a program with you.