Data Science and Machine Learning 2

ECON62012 Data Science and Machine Learning 2 15 Semester 2 Postgraduate Taught Level 7 Karim Chalak Unit coordinator Economics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This unit is a continuation of ECON61351.This unit helps students on the MSc Economics and Data Science in the development of vital study, employability, and programming skills. This course supports students in their development of a comprehensive understanding of advanced data science and machine learning methods for the analysis of economic data. It helps students acquire an understanding of both the statistical theory behind the methods and the practical issues surrounding their implementation in computer languages such as R and Python. Throughout the unit, and through the work described above, students will be supported in developing vital employability skills, such as communicating results to a variety of audiences.  The unit aims to provide the MSc Economics and Data Science students with a good understanding of key methods in econometrics and machine learning. This includes methods for prediction and causal inference that are commonly used in research and empirical practice. Students will gain familiarity with the theory underlying the methods and their use in empirical work and will develop skills that are vital for advanced study and employability. The unit runs in parallel with ECON62020 “Programming and other Skills for Data Scientists,” and provides students with the opportunity to engage in directed work that implements econometric and data science methods.    In order to be able to take up positions in government, central banks or private sector organisations as a data analyst/economist, students will benefit from being able to demonstrate strong skills in the areas supported by this unit. This includes statistical methods for data-scientific analysis such as machine learning; the mathematical theory behind data-scientific methods; the implementation and interpretation of empirical data-scientific analysis of economics data; and communication (written).  Students will be able to explain features, assumptions and estimation methods used by econometric and data science methods.Students will be able to identify issues arising from the use of large dimensional datasets and be able to apply appropriate data reduction techniques to make problems amenable to analysis.  Students will be able to identify whether particular economic problems can be investigated empirically and if so, what strategy is to be used.Students will be able to justify the application of appropriate econometric and data science methods to analyse empirical economic questions (e.g. descriptive analysis, forecasting, causal analysis).  Students will be able to implement appropriate econometric and data scientific techniques (using e.g. R and Python) to address empirical problems.    Students will be able to analyse real life data to understand and describe empirical issues across a range of disciplines and real-world settings.Students will develop an ability to recall and communicate, effectively and quickly, key data-scientific concepts, drawing on their basic properties and shortcomings, in a dynamic professional environment.  Density estimation (parametric and nonparametric estimation); regression (linear, parametric, and nonparametric regression); classification methods; tree based methods; neural networks; structural equations and potential outcomes; methods for identification; graphical methods.    Student work will be organised around lectures and tutorial sessions. The latter is centred around problem sets and empirical exercises, and during the sessions students finalise or continue work prepared asynchronously.Any learning materials required will be delivered through the unit’s Canvas site.   Lecture attendance: 22h (11 weeks x 2h)  Tutorial Attendance: 11h (11 x 1h)  Preparation and consolidation work for lecture material: 88h  Preparation and consolidation work for tutorial material:  29h   Sum: 150h   0 Other 100% Written exams (midterm (30%) final (50%))  80%Problems sets (4 x 5% each) 20%  ECON61001 Econometric Methods Co-Requisite Compulsory ECON61351 Data Science and Machine Learning 1 is a co-requisite.  N • An Introduction to Statistical Learning, with Applications in R, James, Witten, Hastie and Tibshirani, 2nd ed   • An Introduction to Statistical Learning, with Applications in Python, James, Witten, Hastie, Tibshirani, and Taylor   • The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) by T. Hastie, R. Tibshirani, J. H. Friedman; 2nd ed • Chernozhukov, V., C. Hansen, N. Kallus, M. Spindler, V. Syrgkanis (2024). Applied Causal Inference Powered by ML and AI.  • Causality: Models Reasoning & Inference. J. Pearl. Second edition Lectures 22 Tutorials 11 0 117