Mathematics and Programming for the Quantitative Economist:Fundamentals

Anthony Tay, Daniel Preve and Ismail Baydur
Additional contact information
Anthony Tay: Singapore Management University, Singapore
Daniel Preve: Singapore Management University, Singapore
Ismail Baydur: Singapore Management University, Singapore

in World Scientific Books from World Scientific Publishing Co. Pte. Ltd.

Abstract: This book covers calculus, linear algebra, probability and statistics, and computer programming (using Python) at the level required in strong, technically-oriented undergraduate and masters level economics programs. It is the first of a two-volume set, with the second volume focusing on mathematics and programming for networks and dynamic models. Together, the two volumes cover mathematics and programming used in economics, econometrics, data science, machine learning and artificial intelligence. It is intended to be used as a main text for mathematics for economics courses ranging from intermediate to masters levels, and as a resource or reference text for other economics, econometrics and data science courses. It should also be useful as a reference text for economics and data science practitioners who wish to learn more about the mathematics of these subjects.

Keywords: Mathematics for Economics; Mathematics for Econometrics; Mathematics for Data Science; Mathematics for Machine Learning; Mathematics for Social Scientists; Python Programming for Economists; Python for Science; Computational Economics; Calculus; Differential Calculus; Integration; Multivariable Calculus; Machine-based Calculus; Optimization; Constrained Optimization; Numerical Optimization; Linear Algebra; Matrix Algebra; Vector Spaces; Matrix Calculus; Matrix Projections; Matrix Factorizations; Eigenvalues and Eigenvectors; Singular Value Decomposition (SVD); Probability Theory; Random Variables and Distributions; Expectation and Variance; Joint and Conditional Probability; Law of Large Numbers; Central Limit Theorem; Law of Iterated Expectations; Sampling; Statistical Inference; Hypothesis Testing; Estimation Methods; Regression Analysis; Ordinary Least Squares; Maximum Likelihood Estimation; Principal Component Analysis; LASSO; Python Programming; Python for Beginners; Numerical Methods In Economics; Scientific Computing; Computational Methods for Economists; Coding for Economics; Numpy Tutorials; Pandas Tutorials; Matplotlib Visualization; Algorithmic Thinking; Econometrics; Mathematical Economics; Economic Modelling; Quantitative Methods; Quantitative Analysis In Economics; Econometrics with Python; Data Science for Economists; Undergraduate Economics Textbook; Graduate Economics Textbook; Reference Book for Data Science Practitioners; Self-study Guide for Economists; Python Exercises for Economists; Practical Guide to Programming and Mathematics; Integrated Math and Programming Approach (search for similar items in EconPapers)
JEL-codes: A22 A23 C02 C40 C60 C61 C63 (search for similar items in EconPapers)
Date: 2026
ISBN: 9789819826926
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