u/GotTheLyfe

I just finished my BSc in Economics and Business Economics. My background is such:

• Statistics
• Mathematics for Economists (no linear algebra or matrix calculus)
• Econometrics — standard OLS, assumptions, basic inference (stata)
• Applied Microeconometric Techniques
• Introduction to data science in R + python

I have no minor or extra pure math courses (no real analysis, measure theory, advanced linear algebra, etc.) and no prior exposure to ML methods.

I have an admission offer for a 1-year MSc Urban, Port and Transport Economics at Erasmus School of Economics. The programme is very applied / policy-oriented and heavy on empirical work.

Below is the micro econometric toolkit it provides me with:

Core compulsory econometrics :

• Applied Microeconometrics – refreshes linear regression + causality, then instrumental variables / endogeneity, linear panel data models (fixed effects, random effects, difference-in-differences), binary outcome models. Heavy Stata hands-on with real datasets.
• Advanced Empirical Methods – discrete / categorical / count data models, randomised experiments, regression discontinuity designs, difference-in-differences (again, deeper), synthetic control methods. Again full Stata implementation.

Complementary quantitative / ML courses I can take as electives or seminars:

• Data Science and HR Analytics – LASSO, ridge, elastic net, prediction & classification, intersection of ML & econometrics (causal inference, optimal policy estimation, counterfactuals), replication of ML methods in a human-resources / business setting. Programming-focused.
• Seminar Supply Chain Management and Optimisation – optimisation modelling, location problems, cost & CO₂ trade-offs; uses Excel + R for real-world logistics networks.

The rest of the programme (Port Economics, Real Estate Economics, strategy seminars, etc.) is very applied but not method-heavy.

My questions for you:

1. How does this toolkit look for private-sector roles (consulting, transport/logistics analytics, port/shipping companies, real-estate/infrastructure analytics, data science in policy-adjacent firms, etc.)? What kind of jobs or tasks would this prepare me well for?
2. Is the coverage too rudimentary compared with what you typically see in strong pure econometric / data-science master’s programmes?
3. I have zero pure-math background beyond the standard econ-math sequence. Will this bite me later (e.g. when implementing more advanced methods, reading papers, or moving into more technical roles)? Or is the applied focus + heavy Stata/R practice enough for most private-sector work?

Any honest feedback is super welcome — especially from people who went through similar programmes or work in industry. Thanks in advance!

I’ve been accepted into the 1-year MSc Urban, Port and Transport Economics at Erasmus University Rotterdam.

The programme is very applied and quantitative. The specific courses and seminars I plan to take are:

• Applied Microeconometrics & Advanced Empirical Methods (Stata-heavy: regression, panel data, endogeneity, causal inference)
• Port Economics
• Real Estate Economics
• Economics of Strategy
• Seminar Supply Chain Management and Optimisation ( Excel/R modelling of costs, CO₂ emissions, transport modes, network design, location optimisation)
• Seminar Ports and Global Logistics: Disruptive Scenarios (scenario planning for logistics disruptions and long-term strategy)

I’m trying to understand how well this combination translates to supply chain / logistics roles (analyst, consultant, port & shipping operations, sustainability positions, etc.).

Anyone who has done this programme (or a similar transport economics master)? How does it compare to a straight Supply Chain MSc for job prospects?

Any insights would be really appreciated! Thanks!

I just received an admission offer for a 1-year MSc programmes at Erasmus University Rotterdam and I'm trying to get a clear picture of the applied econometrics / causal-inference toolkit I'll actually leave with from the MSc Urban, Port and Transport Economics specialisation.

My Background is a Bsc in Economics and Business Economics ( also in NL)

• Standard first- and second-year econ core (Micro, Macro, Stats, Mathematics for Economists)
• Introductory Econometrics
• Applied Microeconometric Techniques (bachelor-level)
• Introduction to R + Programming with Data
• I have not learnt Linear algebra, Matrice calculus etc

The masters Programme would teach me the following:

• Core methods block :
  • Applied Microeconometrics – refresher on linear regression + causality, specification tests, model selection. Then endogeneity/IV estimation, linear panel data models (random/fixed effects, difference-in-differences), models for binary outcomes. Very hands-on with Stata, real datasets, group assignments interpreting results.
  • Advanced Empirical Methods – discrete/ordered categorical models, randomised experiments, regression discontinuity designs, difference-in-differences (deeper), synthetic control groups. Again theory + heavy Stata implementation, focused on policy evaluation and causal inference.
  • Seminar Supply Chain Management and Optimisation → quantitative supply-chain design/optimisation (costs, time, CO₂), Excel + R for modelling, visualisation, location optimisation, data handling, and writing technical reports.
  • Seminar Ports and Global Logistics: Disruptive Scenarios → scenario planning and strategic foresight in ports, shipping and supply chains (trends, disruptions, Covid-19 shocks, deglobalisation, non-linear risks), business intelligence synthesis from multiple sources, scenario report writing for real-world international companies, group-based strategic decision-making under time pressure and uncertainty.
• Electives – can include Port Economics, Real Estate Economics, Urban Economics, Economics of Strategy, and also Data Science and HR Analytics (ML for causal inference, regularisation, prediction/classification, counterfactuals, policy estimation – open-source software).

My questions for you :

1. How comprehensive/strong is this toolkit for applied microeconometrics work compared to a full Msc in Econometrics ?
2. I have not learnt Linear algebra, Matrice calculus etc, is this going to bite me in the ass ?
3. What obvious gaps should I expect (spatial econometrics? time-series? more programming depth (Python/R advanced)? modern ML/causal-ML integration? theoretical econometrics?)?
4. How well would this prepare me for:
   • Industry / consulting / logistics / transport-policy analytics jobs?
5. Does the very specialised context (ports, supply chains, urban transport) actually help or hinder learning transferable econometric skills?