Econschool runs workshops and courses in economics, mathematics, and statistics.
Workshops are short, live sessions on a single topic, such as Python, R, and AI tools, along with their applications in economics, mathematics, and statistics. The sessions build from the ground up, so no prior programming experience is required.
Courses run over a longer period and cover a subject in depth.
Open for registration
Quantitative Foundations for Economics
A weekly course covering the quantitative material that graduate study in economics assumes: mathematics, probability, statistics, econometrics, and R programming.
| Type | Course |
| Instructor | Amit Goyal |
| Starts | Sunday, 30 August 2026 |
| Schedule | Sundays, 8:30–10:00 AM IST |
| Lecture time | About 45 hours, across roughly 30 sessions |
| Format | Live online; recordings are not provided |
| Fee | ₹40,000 |
| Registration | Register |
Concepts covered
- Logic and sets
- Real analysis
- Linear algebra
- Convexity and optimization
- Probability and statistics
- Econometrics
- R programming
Recent and past sessions
Seeing Probability: The Law of Large Numbers, the CLT, and Python
A two-day live workshop that used Python to make two central results in probability easier to see: the Law of Large Numbers and the Central Limit Theorem. Students who already knew the theory used simulations to observe what convergence looks like, when it fails to occur as expected, and why that matters. Python was a tool for exploring the mathematics, not a separate subject.
\[ \begin{aligned} \bar{X}_n &\xrightarrow{\;p\;} \mu && \text{(Law of Large Numbers)}\\[6pt] \sqrt{n}\,(\bar{X}_n-\mu) &\xrightarrow{\;d\;} \mathcal{N}(0,\sigma^{2}) && \text{(Central Limit Theorem)} \end{aligned} \]as \( n \to \infty \).
| Type | Workshop |
| Instructor | Amit Goyal |
| Dates | 25 July and 1 August 2026 |
| Schedule | Two 3-hour live sessions, 10:00 AM – 2:00 PM IST |
| Format | Live online via Google Meet; limited to 20 students |
| Fee | ₹2,500 |
| Registration | Closed |
What the sessions covered
- Building sampling and simulation from first principles in NumPy
- Computing expectations and variances by Monte Carlo, and the statistical properties of the estimates themselves
- Watching the Law of Large Numbers converge, and watching it fail when its assumptions are violated
- Seeing the Central Limit Theorem emerge from different distributions.
- A problem set between the two sessions, with selected submissions discussed at the start of day two
- All code run in Google Colab, so no installation was required
Introduction to Python
Recordings from an earlier workshop.
Introduction to R
Recordings from an earlier workshop.
LaTeX
Future sessions
To hear about upcoming workshops and courses, register your interest.