The book uses a very simple and accessible language. All the descriptions of Python functionalities come with intuitive examples to make you learn by doing. This is not a theoretical book and does not cover some of the most internal features of Python. The intention of the authors is to allow business oriented people to start using Python. The main reason for such a choice of style is due to the increasing number of requests by non-technical professionals to solve daily problems and tasks. Whether we want to append multiple spreadsheets or profile the customer base, being able to use a solid infrastructure which enables to collect, check, process, analyze data, and report results has become a basic requirement in most industries. The structure of this book replicates the structure of a grammar book. It first introduces the building blocks of Python - i.e., objects - which can be compared to the different types of words (e.g., verbs, nouns) of a language. It introduces readers to the syntax rules of Python - i.e., statements, functions, and classes. It then provides introduces how to use this new language for managing data. One thing that we found particularly helpful for our students is to provide wrong Python codes with the error message that Python provides, and ask them to fix the code in order to make it work. As making mistakes is very common, we would say normal, in programming, learning how to interpret the error messages and fix codes is extremely important from a pedagogical perspective. The book always challenges students with a series of "fix the code" questions at the end of each a topic. We adopt this structure because we found that students learn how to successfully write algorithms in Python when they understand its logic and the meaning of every single element of the algorithm. We have experimented that this natural progression, from the most basic elements to very complex scripts, raises students' self-confidence and proficiency in writing their own algorithms. Hence, the structure of the textbook greatly helps students and instructors to gradually acquire the "programming" mindset. Also, our teaching experience indicates that, as with any other language, students learn by practicing. For this reason our textbook, is rich in exercises of increasing complexity.
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Francesco Grossetti is Assistant Professor at Accounting Department at Bocconi University. He is involved in several research projects in which he is in charge of the development of all the computational and statistical methodology.
Gaia Rubera is full Professor of Marketing at Bocconi University where she is the director of the MSc in Data Science and Business Analytics. She has received the following recognition: NVIDIA GPU Grant, 2017. 2017 Emerald Citations of Excellence Award.
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