Top 12 Economist Skills to Put on Your Resume

In today's highly competitive job market, standing out as an economist takes more than degrees and a handshake. A sharp, well-balanced skill set on your resume signals you can wrestle messy data into insight, forecast with discipline, and inform decisions that actually move the needle.

Economist Skills

1. Econometrics
2. Stata
3. R
4. Python
5. SAS
6. MATLAB
7. SQL
8. Tableau
9. SPSS
10. Excel
11. Forecasting
12. Machine Learning

1. Econometrics

Econometrics applies statistical and mathematical methods to economic data to test theories, estimate relationships, and forecast outcomes.

Why It's Important

It turns theory into evidence. With econometrics, you quantify effects, stress-test assumptions, and build forecasts that stand up to scrutiny.

How to Improve Econometrics Skills

Level up by growing statistical depth, software fluency, and hands-on practice.

1. Strengthen statistical foundations: Probability, inference, causal identification, and regression diagnostics. Revisit core texts and problem sets until they click.

2. Master econometric software: R, Stata, or EViews—learn syntax, help files, and workflows for reproducible analysis.

3. Practice on real data: Use public sources (national accounts, labor surveys, FRED, World Bank) to build models end to end.

4. Stay current: Read journals and working papers to track new estimators, robust methods, and best practices.

5. Engage with peers: Seminars, forums, and study groups sharpen intuition and expose blind spots.

Keep iterating—model, diagnose, refine. Rinse and repeat on fresh datasets.

How to Display Econometrics Skills on Your Resume

2. Stata

Stata is a statistical platform favored in economics for data management, econometric modeling, and crisp graphics.

Why It's Important

It’s fast, disciplined, and built for serious empirical work—perfect for panel data, survey analysis, and publication-grade output.

How to Improve Stata Skills

1. Nail the basics: Syntax, help files, and do-files for reproducibility.

2. Automate: Write do- and ado-files, use macros, and structure projects with versioned scripts.

3. Data work: Master merges, reshaping, labeling, and auditing datasets.

4. Econometrics depth: Fixed effects, IV, GMM, limited dependent variable models, and robust SEs.

5. Graphics: Build templates and schemes for clear, consistent visuals.

6. Programming: Scalars, matrices, loops, and Mata for performance.

7. Community and learning: User-written commands, forums, and conferences keep you sharp.

Design clean workflows; future you will thank current you.

How to Display Stata Skills on Your Resume

3. R

R is a programming language for statistical computing and graphics, widely used for data analysis, econometrics, and visualization.

Why It's Important

Open, extensible, and powerful. It handles messy data, complex models, and beautiful visuals—all in one ecosystem.

How to Improve R Skills

1. Foundations: Data structures, functions, and the tidyverse. “R for Data Science” is a strong primer.

2. Wrangling and visualization: dplyr, tidyr, and ggplot2 for fluent analysis and storytelling.

3. Econometrics in R: Time series, panel data, causal inference. “Introduction to Econometrics with R” maps methods to code.

4. Advanced methods: Regularization, resampling, and ML workflows; “Applied Predictive Modeling” broadens the toolkit.

5. Reproducibility: rmarkdown, knitr, and targets for clean, repeatable pipelines.

6. Community: R-bloggers, Q&A forums, meetups—learn from real problems and solutions.

7. Practice: Build projects with public economic data; iterate quickly.

8. Stay updated: Track CRAN task views and package vignettes relevant to econometrics.

Small scripts grow into robust libraries if you let them.

How to Display R Skills on Your Resume

4. Python

Python is a versatile language prized for readable code, data pipelines, and a rich scientific stack.

Why It's Important

It scales from ad hoc analysis to production systems—ideal for large datasets, modeling, and automation.

How to Improve Python Skills

1. Data stack: Get fluent with Pandas, NumPy, and tidy plotting with Matplotlib/Seaborn.

2. Econometrics and ML: Use Statsmodels for inference and scikit-learn for predictive tasks; practice end-to-end workflows.

3. Projects: Analyze real economic series, build forecasting scripts, and stress-test assumptions.

4. Habits: Write clean functions, add tests, use notebooks wisely, and practice regularly (coding challenges help).

From prototype to pipeline—tight loops make better code.

How to Display Python Skills on Your Resume

5. SAS

SAS is an analytics suite used for data management, statistical analysis, and econometric modeling, common in government and regulated industries.

Why It's Important

It handles large, structured datasets with governance in mind—reliable, auditable, and enterprise-ready.

How to Improve SAS Skills

1. Core programming: Data steps, proc steps, formats, and robust data cleaning.

2. Econometric procedures: PROC REG, PROC LOGISTIC, PROC ARIMA/UCM, and time-series diagnostics.

3. Macro language: Automate repetitive work; build parameterized, reusable code.

4. Visualization: PROC SGPLOT, SGSCATTER, SGPANEL for publication-ready charts.

5. Scalability: Explore modern SAS platforms (e.g., cloud-enabled) for bigger data and faster runs.

6. Continuous learning: Manuals, communities, and internal brown-bags keep skills fresh.

Think pipelines, not one-off scripts.

How to Display SAS Skills on Your Resume

6. MATLAB

MATLAB is a high-level environment for numerical computing, modeling, and visualization, with toolboxes tailored to econometrics and finance.

Why It's Important

It shines for simulation, optimization, and matrix-heavy work—great for structural models and time-series analysis.

How to Improve MATLAB Skills

1. Start solid: Syntax, indexing, vectorization, and the editor/debugger.

2. Data and plotting: Import pipelines, tables/timetables, and layered visualizations.

3. Econometrics Toolbox: VAR/VECM, state-space models, panel methods, and forecasting utilities.

4. Financial Toolbox: Portfolio, risk, and pricing workflows if your work leans that way.

5. Courses and docs: Official tutorials and training paths accelerate mastery.

6. Projects: Replicate papers, then extend them—simulation studies teach fast.

7. Community: User forums and shared files save time and spark ideas.

8. Keep up: Track release notes for new functions and performance gains.

Vectorize when you can; profile when you can’t.

How to Display MATLAB Skills on Your Resume

7. SQL

SQL is the language of relational databases—querying, joining, aggregating, and shaping data at scale.

Why It's Important

Economists live in data. SQL lets you pull precisely what you need from large, messy stores without breaking a sweat.

How to Improve SQL Skills

1. Model the data: Understand normalization, keys, and schemas so queries align with structure.

2. Write powerful queries: Master joins, subqueries, window functions, CTEs, and conditional aggregation.

3. Analyze economic questions: Build cohort analyses, index transformations, seasonal adjustments, and trend extractions directly in SQL.

4. Optimize: Use indexes wisely, examine query plans, and reduce scans with tidy predicates.

5. Bridge tools: Connect SQL to R (DBI/dbplyr) or Python (SQLAlchemy/connector libraries) for integrated workflows.

6. Practice: Daily reps on realistic datasets; challenge sites help build speed and accuracy.

7. Stay current: Features differ across Postgres, MySQL, SQL Server, and cloud warehouses—learn the nuances.

Good SQL means faster insight and fewer bottlenecks.

How to Display SQL Skills on Your Resume

8. Tableau

Tableau turns datasets into interactive dashboards and visual stories, making patterns jump off the screen.

Why It's Important

It helps economists communicate—clearly, quickly—so audiences grasp trends, uncertainty, and implications at a glance.

How to Improve Tableau Skills

1. Core visuals: Master timeseries, bar/line/area charts, maps, and highlight tables that reveal structure.

2. Dashboard design: Arrange views, use actions, and build narratives that guide a user’s eye.

3. Analytics features: Calculated fields, table calcs, parameters, trendlines, and forecast tools.

4. Inspiration: Study public workbooks and reverse-engineer great designs.

5. Community: Forums and user groups shorten your learning curve.

6. Real economic data: Build dashboards with official statistics; iterate with stakeholder feedback.

7. Keep pace: New releases add capabilities—learn them and refine your style.

Design for clarity; decorate sparingly.

How to Display Tableau Skills on Your Resume

9. SPSS

SPSS is a software suite for statistical analysis and data management, common in survey-heavy and policy contexts.

Why It's Important

It streamlines standard analyses and documentation—handy when consistency and auditability matter.

How to Improve SPSS Skills

1. Build a strong base: Data import, variable types, transformations, and syntax versus GUI.

2. Statistical depth: Choose the right tests and models; understand assumptions and diagnostics.

3. Tutorials and practice: Work through step-by-step guides and replicate published analyses.

4. Datasets: Practice on public microdata—labor, health, or household surveys.

5. Community: Discussion boards and user groups surface practical tips.

6. Events: Workshops and webinars expose features you might miss on your own.

7. Case studies: Read how practitioners frame questions and interpret results.

Document everything—syntax saves time and reduces errors.

How to Display SPSS Skills on Your Resume

10. Excel

Excel is the workhorse for quick analysis, modeling, and visualization—still ubiquitous, still powerful.

Why It's Important

Fast what-if analysis, clean charts, and transparent models make Excel a staple in economic workflows.

How to Improve Excel Skills

1. Advanced formulas: INDEX/XMATCH, LET, LAMBDA, dynamic arrays—precision tools for flexible models.

2. PivotTables/Charts: Summarize and slice with confidence; design for clarity.

3. Power Query: Import, clean, and reshape data reproducibly.

4. Power Pivot & DAX: Build data models with relationships and measures for robust analysis.

5. Macros & VBA: Automate repetitive tasks; create reliable workflows.

6. Data visualization: Apply principles from “Storytelling with Data” for crisp, honest visuals.

7. Financial/economic modeling: Forecasts, scenarios, and sensitivity analysis with discipline.

Structure sheets, label everything, and protect formulas—future-proof your work.

How to Display Excel Skills on Your Resume

11. Forecasting

Forecasting in economics blends data, structure, and judgment to project future conditions—growth, inflation, labor markets, and more.

Why It's Important

Budgets, policy, investment—so many choices hinge on expectations. Better forecasts, better bets.

How to Improve Forecasting Skills

1. Elevate data quality: Systematic cleaning, outlier checks, revisions tracking, and relevant feature selection.

2. Use appropriate models: ARIMA/ETS, VAR/VECM, state-space models, regime-switching, and machine learning when signal-to-noise warrants it.

3. Leverage timely indicators: Nowcasting with high-frequency series, composites, and real-time data.

4. Scenario planning: Build baseline, upside, downside; stress test with shocks and elasticity assumptions.

5. Cross-disciplinary insight: Fold in behavioral, political, and sector knowledge to shape priors and interpret shifts.

6. Evaluate relentlessly: Backtest, track forecast errors, and recalibrate models and judgment rules.

7. Communicate uncertainty: Intervals, fan charts, and clear narratives beat point estimates alone.

Adapt fast when reality swerves—models should, too.

How to Display Forecasting Skills on Your Resume

12. Machine Learning

Machine Learning uses algorithms to learn patterns from data and make predictions—handy for classification, risk scoring, and non-linear forecasting.

Why It's Important

It can capture complex relationships and scale across massive datasets, expanding what economists can model and monitor.

How to Improve Machine Learning Skills

1. Data first: Rigorous cleaning, feature engineering grounded in economic theory, and leakage checks.

2. Model fit for purpose: Compare linear, tree-based, gradient boosting, and regularized models; use cross-validation and robust metrics.

3. Interpretability: Favor transparent models when stakes are high; apply SHAP or LIME to explain predictions.

4. Causality awareness: Distinguish prediction from inference; combine ML with causal tools when policy questions demand it.

5. Maintain and monitor: Retrain with new data, watch for drift, and build feedback loops with domain experts.

Useful models aren’t only accurate—they’re explainable, stable, and aligned with the question.

How to Display Machine Learning Skills on Your Resume