Econometrics and Data Analysis

Our flagship course helps high school students thrive in their future academic endeavors by providing hands-on instruction covering the fundamentals of quantitative analysis and reasoning.

Course Description

Data analysis has become a staple feature of modern society. Questions related to public policy, private business, charity work and international NGO's all increasingly rely on data to make informed decisions. To have access to high quality data and to analyze it correctly, however, are two different things. The aim of this course is to introduce students to the basics of data science using econometric techniques and machine learning. Econometrics is a subfield of economics that applies statistical methods to data in order test economic relationships, theories and ideas. Often, it is used to distinguish causation from correlation. In this course, we will cover the basics of linear regression as a first step to establishing causal relations between different observed phenomena, economic or otherwise.

The course consists of six weeks of lectures and data tutorials (two sessions per week), followed by four to six week of guided research supervision.

Students will receive the following:

  • Live instruction from university professors.
  • Training to distinguish causation from correlation
  • Coding skills in Python to run OLS regressions, including models with panel data and fixed effects, as well as to conduct basic data analysis.
  • Supervision to write and publish a research paper.
  • A personalized recommendation letter for college applications.

Because we offer highly personalized courses, please contact us for more detailed information on pricing, availability and course structure that fit your needs.

Format and Structure

This course has three parts.

The first part includes lectures from university faculty trained in economics and data science on the fundamentals of econometric analysis and machine learning.

The second part is focused on applied data science. It provides students with an opportunity to apply the concepts in lecture in order to develop data analysis skills. Each week there is a data tutorial in which students will analyze real-world datasets. Students start with basics - plotting trends, distributions, scatterplots - and work up to linear regression and panel data methods. Students will learn coding skills in Python.

Finally, in the third part, students will produce an empirical research paper, either on a topic of their choice or a topic assigned by the lecturer. Each student will write their own academic article suitable to be published in an undergraduate or high school journals. In this part of the course, students will work independently with personalized weekly supervision from our faculty. In the past, students have written on such subjects as the gender-wage gap, racial differences in AI-exposure, as well as the factors that determine entry into elite private colleges.

Requirements

The course is tailored for students in the last or second to last year of High School or early stage Bachelor students.