Le Grand Syllabus 2017/2018
Teachers : Constance DE QUATREBARBES (Agent contractuel /Entrepreneur d'Intérêt Général / Architecte des données), Célya GRUSONDANIEL (Ingénieure de recherche). Prerequisite : No technical pre-requisite is required. Students will be guided step by step. This course is designed to overcome the ﬁrst reluctance for technical environment often perceived as a « black box ». An exercise platform will allow the student to advance at his own pace. Pedagogical Format : Elective Course validation : Students will complete a series of programming exercises as well as an individual or group project. The notes will cover : - completion of programming exercises (20%). - presentation of the ﬁnal project (20%). - completion of the ﬁnal project (60%). - technical (50%) quality and rigor of the implementation. Workload : Exercises between sessions (1h). Individual or group project preparation (1h). Pedagogical Method : The session will mix several formats : technical and reﬂexive lighting talks combined with exercies on platform and students project monitoring. Course Description : This course offers an introduction to data science combining a pragmatic approach (initiation to programming in social sciences using python language) with a reﬂexive perspective. Each session will follow the different steps of data processing (from data collection to their visualization). Applied exercises will enable students to learn about programming so as to develop a critical thinking of the technical and socio-political stakes undertaking these practices (Science Technologies Studies approach). The aim of this course is not to train engineers but to give technical autonomy to students. They will be prepared to dialogue with developers, data scientists, computer engineers, project managers, product owners, etc. in order to lead collaborative projects involving data science. Required reading : Rogers, R. Digital methods. MIT press. 2013 ; Gitelman, L. Raw data is an oxymoron. MIT Press. 2013. 1464
INTRODUCTION TO ECONOMETRICS USING STATA : APPLICATION TO POLICY EVALUATION
Semester : Spring Number of hours : 24 Language of tuition : English
Teachers : Dylan GLOVER (PhD candidate, Teaching Assistant). Prerequisite : Content will be accessible to a broad audience, but the discipline is based on mathematics and statistics. Hence, students will be expected to have taken a college-level statistics course. Knowledge of differential calculus is also recommended. In addition, approximately half of the course will be spent on problems using Stata. Students should be accustomed to using a computer and be enthusiastic about learning a new programming language. Finally, most examples and exercises will be about public policy evaluation in developed and developing countries. Students should preferably have some interest in this ﬁeld. Pedagogical Format : Seminar Course validation : Participation (15%). Mid-term exam (35%). Final exam (50%). Workload : 2-3 hour/week. Pedagogical Method : For each course, I will present using lecture slides and give out weekly homework. Course Description : The objective of the course is to introduce the fundamental concepts of econometrics to students with little quantitative background, but interested in the ﬁeld of public policy and impact evaluation. The course will be divided into two sections : 1) Introduction to basic skills and knowledge needed to understand quantitative analysis (descriptive stats, inferential stats, OLS). 2) Application to experimental and non experimental evaluation techniques (before and after, simple comparison, difference-in-differences, multivariate regression, instrumental variable and randomized evaluation). Each 2 hours course will be divided as follow : 1) Lecture 2) homework corrections/work in Stata. Required reading : to be deﬁned.