Technology literacy is financial literacy.
From data to AI and blockchain, the most useful skill is knowing how to ask better questions and evaluate the answers.
Data skills: start with a question
A useful learning project begins with a clear question: how has a company’s revenue changed, or how do different assets respond to changing economic conditions? Organise a small dataset, check units and dates, and describe the limits before drawing conclusions.
Spreadsheets, basic programming and visualisation can make research more reproducible. Keep a record of sources and assumptions. A clear chart should explain a relationship, not conceal uncertainty behind an impressive appearance.
AI in finance: a research assistant
AI can assist with organising documents, summarising information and exploring patterns. The AI Financial Lab described in Lin’s biography reflects his interest in quantitative trading and market prediction research. Exploring an application is different from demonstrating a reliable investment outcome.
A sensible learning exercise compares a model with a simple baseline, checks the original sources and keeps testing data separate from training data. Historical patterns may not persist, and confident language is not evidence that a model is correct.
Digital finance: understand the infrastructure
Blockchain education can begin with a ledger, a transaction and the distinction between holding an asset and relying on a custodian. Understanding these mechanics helps learners discuss digital finance without reducing the subject to token prices.
An accessible practice route is to analyse public data and document findings without depositing money or exposing wallet credentials. Treat security habits, source verification and clear communication as part of technical competence.