Economics Meets Data Science

How economists increasingly work with big data, machine learning and programming, and why economic thinking adds value to data science.

The rise of big data has created new opportunities for economists.

Where economists and data meet

Tech companies hire economists to design pricing, auctions, marketplaces and experiments.
E-commerce and fintech firms analyse customer behaviour and credit risk.
Governments use data for targeting and policy evaluation.
Research uses satellite data, mobile data and online prices.

What economists add

Causal thinking: distinguishing correlation from causation, crucial for decisions.
Experiments: designing A/B tests and randomised trials.
Understanding incentives and markets.

Skills to learn

Programming: Python or R.
SQL for databases.
Statistics and machine learning basics.
Data visualisation, with accessible formats.

Career paths

Data analyst, data scientist or economist at tech companies.
Quantitative researcher in finance.
Policy data roles in government and NGOs.

Accessibility

Programming languages work well with screen readers, and many blind programmers and data scientists work successfully, though charts and dashboards need accessible alternatives like tables, text descriptions and sonification.

The A/B test

An economist at an e-commerce company runs an experiment: half of users see free delivery above 499 rupees, the other half above 299. Comparing results shows which threshold increases profits.

Thinking data science replaces economics

Economic thinking about causation and incentives makes data analysis more useful.

Key takeaways

Tech firms, fintech and governments hire economists for data work.
Economists add causal thinking and experiment design.
Python, R, SQL and statistics are key skills.
Programming can be accessible for blind and low-vision professionals.