Datascience for Finance
- Teacher(s): Z.Zhao
- Course given in: English
- ECTS Credits: 6 credits
- Schedule: Autumn Semester 2022-2023, 4.0h. course + 1.0h exercices (weekly average)
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sessions
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course website
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Related programmes:
Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science
Master of Science (MSc) in Finance, Orientation Asset and Risk Management
Master of Science (MSc) in Finance, Orientation Corporate Finance -
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ObjectivesThe objective of the class is to provide students with:
This class should be a "hands-on" class on dealing with data. It will be composed of two parts, in the first part of each session, we will go over econometric models; in the second part, we will use software such as STATA, python, etc to analyze related examples. Contents1. Introduction Introduction to Econometrics Tools used for economic analysis 2. The Linear Regression Model The Linear Regression Model Least Squares Regression Hypothesis Tests and Model Selection 3. Estimation Methodology 4. Cross Sections, Time Series and Panel Data * Important: I might adjust the pace of the class and this syllabus according to our progress throughout the semester. Please pay close attention to the Moodle system. ReferencesRequired Textbook: W. Greene (2018), "Econometric Analysis" (8th edition, Prentice Hall). Peter Kennedy (2008), " A Guide to Econometrics" (6th Edition, The MIT Press). Other (read if interested): J. Hamilton (1994), "Time Series Analysis" (Princeton University Press). W. McKinney (2013), "Python for Data Analysis" (O'Reilly Media Inc) EvaluationFirst attempt
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