Financial analysis skills, built through practice
We run workshops where participants work with real software — not slides about software.
Where financial analysis becomes a usable skill
Suranebot started as a response to a specific gap: most financial education focuses on theory and certification while leaving participants unsure how to open the actual tools on day one of a new role. We wanted workshops that closed that gap directly.
Each workshop puts participants inside the software — Excel financial modelling, Bloomberg terminal navigation, Power BI dashboards, and Python-based analysis — with structured assignments that reflect scenarios from real working environments.
Structure of every workshop
Each format follows the same underlying logic, regardless of the tool or topic being covered.
Tool orientation
Participants start by navigating the actual interface — not a screenshot. The first session is always about orientation inside the real environment.
Guided assignments
Step-by-step tasks with clear input data and expected output. Each assignment mirrors a realistic workplace request rather than a textbook exercise.
Peer review rounds
Participants review each other's outputs against a structured rubric. Reading someone else's model often surfaces logic you missed in your own work.
Independent scenario
A final unguided task with no walkthrough. Participants apply what they built during the workshop to a new dataset or unfamiliar prompt.
Common starting point
- Can describe what DCF analysis is in general terms
- Opens Excel but defaults to manual calculation
- Avoids Bloomberg because the interface feels opaque
- Builds charts but cannot explain the underlying data model
What typically changes
- Builds a working DCF model from a raw input file
- Uses named ranges, structured references, and audit tools
- Pulls live data from Bloomberg into an analysis template
- Explains every number in their own Power BI report
People who run the workshops
Every instructor at Suranebot has spent time working in financial analysis roles before moving into teaching. They are not academics explaining concepts — they are practitioners explaining what they actually do, including where tools behave unexpectedly and why certain shortcuts matter.
Instruction quality depends on whether the person teaching has sat with the same frustrations as the person learning. That shared reference point changes how feedback lands.
Workshops are kept small enough that each participant gets direct feedback on their own work. Group sizes cap at 16 to preserve that.
Eight years in equity research before joining Suranebot. Leads all Excel and valuation workshops. Known for catching the one assumption that breaks the entire model.
Former analyst at a mid-size asset manager. Teaches Power BI and Tableau workshops with a focus on making dashboards legible to non-analysts.
Transition from quantitative risk into education. Runs Python-based analysis workshops aimed at analysts who can code a little but have never applied it to financial datasets.
