Mathematics And Programming For The Economist by Anthony Tay (.PDF)
File Size: 132.2 MB
Mathematics And Programming For The Quantitative Economist: Fundamentals by Anthony Tay, Daniel Preve, Ismail Baydur
Requirements: .PDF reader, 132.2 MB
Overview: 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 (AI). 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. We get computers to execute our instructions by expressing our commands in a programming language. Examples of general-purpose programming languages are Java, C++, and Python. At the time of writing, Python is the most popular general-purpose language, and the most popular language for data science. Together with R and increasingly Julia, it is widely used by quantitative economists and econometricians. Python is an interpreted programming language, meaning that it reads and runs code line-by-line and unlike compiled programming languages, you do not have to compile entire programs into machine code before running them. This makes it interactive in the sense that you can write and run code in real-time, getting results of commands as and when you enter them. It is also an object-oriented programming language, which allows you to organize data into special structures with structure-specific methods or actions for acting on that data. This makes coding easier, and makes code more readable. The minimal set of commands and functions in the Python programming language (called base Python) is powerful on its own. However, the real power of the free and easy to learn Python language lies in its large number of libraries designed for specific tasks. Examples include NumPy for numerical computing, Pandas for working with tabular data, Matplotlib for data visualization, SciPy for scientific computing, and statsmodels for statistical analysis. A typical Python project will use many such libraries.
Genre: Non-Fiction > Tech & Devices
Free Download links: