Syllabus¶
Overview¶
Course
- A.116 | Espace Numérique
- Wednesdays 15h45-19h00
What will you learn in this course? Our goal is to give you the ability to understand, explain, and perform modern social science research with a special focus on data analysis and inference. You will be able to read and understand the methodology of most academic articles in the social sciences, but more importantly you will have a foot in the door of the data science world. The ability to collect and analyze data in a sophisticated manner has become a crucial skill set for the modern job market across industries. Through a combination of lectures, hands-on exercises, and a final project, you will learn how to clean, visualize, and interpret complex datasets, gaining valuable insights into citizens' attitudes, behaviors, and broader political trends. Finally, you will obtain data literacy that will help you be a critical consumer of evidence for the rest of your life.
This course also introduces to cutting-edge open source research tools. These tools will be incrementally introduced to students. Learning support will be provided for at least one programming language, such as C++, Julia, Python, or R, but the choice of language supported may vary between years, depending on judged benefits to students, whether in terms of pedagogy or resulting skills. This year, we will rely on Python!
Goals¶
- Visualize, summarise, and analyse real-world data using reproducible code based on cutting-edge open source tools for data analysis.
- Empirically test theories, including the derivation of hypotheses, conceptualization, measurement and inference.
- Understand the scientific method and critically evaluate scientific information.
By the end of the course, you will be able to perform univariate and bivariate data analyses, have an understanding of multiple linear regression and statistical inference, and gain exposure to data science and computational methods.
Requirements¶
The course is designed for social science students with no previous experience of quantitative methods, statistics or computer programming.
- The most important requirement is motivation to work hard on likely unfamiliar material.
- The second most important requirement is to have access to a computer in order to participate and complete the various activities. From there, we will learn as we go!
Warning
In order to fight against digital inequalities among students, Sciences Po Bordeaux sets up a support system under certain conditions (presentation of a purchase invoice; N-1 tax notice of your reference tax household ; notification of CROUS scholarship; certificate of attendance for the current year).
For any questions relating to this device and in order to submit your request, please write only to the following address, specifying your needs:
Structure¶
This course has 12 modules that span over two semesters of 6 class meetings. During the class meetings students have the opportunity to accomplish various activities. The activities allow students to incrementally assimilate critical concepts that will lead them to deliver a fully reproducible end to end scientific paper.
Class Meetings¶
Each class meeting is divided into three parts: theory, application and a code.
In the theory part, a group of students carries out a presentation a 20 minutes presentation that is followed by a comment from another student and a question period. The teacher ends this part by providing extra colour to the reading and the presentation.
In the application part, students have the opportunity to articulate theoretical concepts through practical exercises to help them understand critical aspects of research methods and set them for success.
In the code part, students have the opportunity to learn about cutting-edge open source research tools that will be useful to them throughout their future careers.
Grading¶
The course is evaluated over two semesters through various activities allowing students to incrementally improve data science skills. By the end of the course, students submit an original and fully reproducible scientific paper that leverages the acquired data analysis skills.
| Activities | Percentage |
|---|---|
| Participation | 30% |
| Milestones | 30% |
| Paper | 40% |
Communication¶
We will use an interactive Chat platform as the primary communication medium for this course. When you have questions outside of class meetings, click the Chat tab at the top of this page and ask your question in the appropriate channel. The onboarding activity should help you get started!
Asking questions publicly and providing answers to your peers' questions allows everyone to learn dynamically. Furthermore, if you have a question on a topic, it is likely that someone else has the same question. Finally, being active on the platform, whether by answering the questions of your peers or by asking questions, is strongly encouraged and will have a positive impact on your participation grade.
Reserve private messages with the instructor for personal matters.