Resources

Books

419frpsBEGL._SX348_BO1,204,203,200_ Written by Charles Severance, Associate Professor at the University of Michigan School of Information, Python for Informatics serves as a beginner guide for anyone new to Python (or programming!). Divided into 16 chapters, the book introduces basic concepts and programming syntax of Python, such as list, dictionary, tuple, to more complex applications such as web services, visualisation. The book is available under an open copyright and is used along many Python Coursera courses. It is also provided with hands-on example codes for post-chapter programming problems. Refer here for more details.

 


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Introduction to Econometrics focuses on multivariate regression to analyse relationships between different socio-economic variables. While economics-oriented, the book is a wonderful resource to learn in dept about regression methods through mathematics and analytics analysis. In particular, parts of the book are devoted to explain real-life questions such as the causal relationship between elementary class size and student performance or the prediction of inflation rate based on predictive factors. Written in layman’s terms, the book truly makes econometrics an interesting study subject.


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Networks, Crowds, and Markets is truly the fundamental textbook for any individuals interested in the Social Network field. Composed of topics from various areas, from economics, sociology, to information science and computing, the book introduces social network theories to explain how individuals, organisations, and businesses are connected in the real world. The most common topics such as graph, strong and weak ties, homophily, and power laws, are carefully studied in the book. Refer here for further details.

 


Online Resources

SEASLogo_RGBHarvard’s CS109 Data Science: the online course, while mainly uses Python as the main programming language for learning platform, introduces five key facets of data analysis: data preparation, database management, exploratory data analysis, predictive methods, and analytical visualisation. Labs and homework are very useful and can be applied in larger-scale programme. Refer here for the course materials and details.