Recommended readings to everyone who is interested about data science, economics, future design, mathematics, and statistics.

Data Science for Economics

Python for data science

  1. McKinney, W. (2022). Python for data analysis: Data wrangling with pandas. numpy, and jupyter.

R for data science

  1. Wickham, H., and Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly.

  2. Llaudet, E., and Imai, K. (2022). Data analysis for social science: A friendly and practical introduction. Princeton University Press.

  3. Imai, K. (2017) Quantitative social science: An introduction. Princeton University Press.

  4. Wickham, H., Navarro, D., and Pedersen, T. (2023) ggplot2: Elegant graphics for data analysis (3e).

  5. Heiss, F. (2016). Using R for introductory econometrics. Düsseldorf, Germany: Florian Heiss.

Economics

Econometrics

  1. Wooldridge, J. (2019) Introductory econometrics: A modern approach. Cengage Learning.

  2. Wooldridge, J. (2010) Econometric analysis of cross section and panel data. MIT press.

  3. Angrist, J. and Pischke, J. (2009) Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.

  4. Cameron, C. and Trivedi, P. (2022) Volume I: Cross-sectional and panel regression methods. Stata Press.

  5. Cameron, C. and Trivedi, P. (2022) Volume II: Nonlinear models and causal inference methods. Stata Press.

Environmental economics

  1. Kolstad, C. (2011), Environmental economics. New York : Oxford University Press.

Experimental economics

  1. Friedman, D. (1994) Experimental methods: A primer for economists. Cambridge University Press.

  2. Gibbons, R. (1992) Game theory for applied economists. Princeton University Press.

Geoeconomics

  1. Lovelace, R., Nowosad, J., and Muenchow, J. (2019). Geocomputation with R. CRC Press.

  2. Comber, L., and Brunsdon, C. (2020). Geographical data science and spatial data analysis: An Introduction in R. Sage.

  3. Kopczewska, K. (2020). Applied spatial statistics and econometrics: Data analysis in R. Routledge.

International Economics

  1. Krugman, P. (2018). International economics: Theory and policy. New York: Prentice Hall.

  2. Krugman, P., Obstfeld, M., and Melitz, M. (2018). International trade: Theory and policy. Boston: Pearson Education

  3. Krugman, P., Obstfeld, M., and Melitz, M. (2017). International finance: Theory and policy. Boston: Pearson Education

Microeconomics

  1. Kreps, D. (2013). Microeconomic foundations I: Choice and competitive markets. Princeton University Press.

  2. Kreps, D. (2023). Microeconomic foundations II: Imperfect competition, information, and strategic interaction. Princeton University Press.

  3. Cameron, A. and Trivedi, P. (2005) Microeconometrics: Methods and applications. Cambridge University Press, New York.

  4. Mas-Colell, A., Whinston, M., and Green, J. (1995). Microeconomic theory. New York: Oxford University Press.

Future design

Behavioral economics

  1. Daniel, K. (2017). Thinking, fast and slow.

Psychology

  1. Frankl, V. (1946) Man’s search for meaning.

Mathematics

Linear algebra

  1. Lay, D. (2022). Linear algebra and its applications. Pearson Education Limited.

Mathematical statistics

  1. Rice, J. (2013) Mathematical statistics and data analysis. Cengage learning.

Vector calculus

  1. Marsden, J. and Tromba, A. (2012). Vector calculus. Macmillan.

  2. Thomas, G., Finney, R., Weir, M., and Giordano, F. (2018). Thomas’ calculus. Reading: Addison-Wesley.

Statistics

  1. Wonnacott, T., Wonnacott, R. (2010) Introductory statistics (5th Edition). Wiley.

Updated on: 2024-10-17