Learning Outcomes:
a. Master the basic knowledge of Python, including data structure, object-oriented programming, loops and structure-programming, and have the basic skills of programming with Python for problem solving;
b. Master the basic knowledge and skills of data manipulation, transformation with package Numpy, Pandas, and data visualization with Matplotlib;
c. Master the basic techniques, approaches and knowledges of financial data science, such as financial time series, statistics and regressions;
d. Ability how to apply that theory into practice and real-life scenarios, for example, building a trading model, or evaluate the performance of trading model, and some extended topics like machine learning also be introduced for practise;
e. Capture the essences of financial system such as topological complexity, hierarchy, transmissibility, interaction, causality, etc;
f. Reflect the objective interrelations among economies or between economies and economic systems;
g. Reveal the inherent evolution of the cross-regional and even global economic systems.
Indicative Module Content:
• Getting Started with Python
• Basic data structure, Conditionals, and Loops
• Abstraction, Files and Exceptions
• Numeric Computing with NumPy
• Data Manipulation with Pandas
• Visualization with Matplotlib
• Advances for Data Analysis I:
• Probability and Statistics
• Advances for Data Analysis II:
• Time Series and Measuring Investment Risk
• What is Global Value Chain (GVC)?
• Measure the Importance of Economies based on Gravity Model
• Measure the Importance of Economies based on Markov Process
• Evaluate the Competition and Collaboration between Economies
• Find the Evolutionary Mechanism of GVC
• Depict the Nested Structure of Production System
• Connect the Structural Features and Economic Status