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2017年度 経済学部 0704254 Data Science for Practical Economic Research Fabinger Michal
横断型

This course is designed to help students use their time efficiently when performing economic data analysis. Topics include: Data manipulation: dataset transformation, visualization, data cleaning, web data scraping, conversion of data for the purposes of econometric estimation. Supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Unsupervised machine learning: clustering, factor analysis, principal component analysis, independent component analysis. Semi-supervised learning. Distributed data representation: word embedding. Nonlinear dimensionality reduction. Computational graphs and functional programming. Practical aspects of high-performance computing: GPU computing, cloud computing, model parallelism, and data parallelism. The course will include a first introduction to Python, TensorFlow, R, Scala, and Mathematica. For specialized tasks other software will be introduced. The students are encouraged to bring to the class their own datasets, which could then be used for the purposes of instruction and practical demonstration.

2017 Economics 0704254 Data Science for Practical Economic Research(Graduate Level) Fabinger Michal
University-wide

共通科目コード Common Course Code FEC-CE5801L3
開講学期 Semester S1
開講時限 Period
火曜3限 水曜3限 Tue 3rd Wed 3rd
単位数 Credits 2
学年 Academic Year B3 B4 B5 B6
他学部聴講 Open to other faculties 可 YES
USTEP生聴講 Permitted to USTEP Students 可 YES
教室 Classroom
経済学研究科棟 第3教室
授業使用言語 Language in Lecture 英語 English
講義題目 Title Data Science for Practical Economic Research
授業の方法 Teaching Methods Class instruction and individual research projects.
成績評価方法 Method of Evaluation Evaluation criteria will include homework, class presentations, class participation and/or research projects.
学部横断型教育プログラム
University-wide Undergraduate Education Program
国際総合日本学教育プログラム Global Japan Studies Program

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