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.
Readership: For advanced economics undergraduate and graduate students, quantitatively-oriented students in other social science disciplines, economics and data science researchers; practitioners and self-learners who are interested in updating their knowledge in quantitative economics techniques.
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