Name: Lukas Jürgensmeier
Type: User
Company: TechAcademy e.V. and Goethe University
Bio: Hi! I'm a Ph.D. student in Quantitative Marketing and a member of the executive board at TechAcademy, a non-profit teaching coding to students.
Location: Frankfurt, Germany
Blog: lukas-juergensmeier.com
Lukas Jürgensmeier's Projects
Research Paper: Economic Consequences of the 1918 Flu Pandemic: Reviewing Evidence from the Historical Precedent of Covid-19
📝 Easily create a beautiful website using Academic, Hugo, and Netlify
Research Paper: "Bayesian Beer Market Estimation: Simulating Nash Equilibrium Market Outcomes with Bayesian Analysis of Choice-Based Conjoint Data"
The Economist's model to estimate excess deaths to the covid-19 pandemic
Visualizing the Coronavirus Outbreak
Shared repository for the Customer Satisfaction and Consumer Choice term paper project.
Repository for the market estimation
Data Science Project Guide for TechAcademy's Summer 2020 Python and R Track
Data Science Course in a Box
Repository for the tutorial R scripts in Advanced Econometrics
Course materials and website for "Introduction to Data Science with R and Tidyverse"
Course materials and website for "Introduction to Data Science with R and Tidyverse" for January 2022 Course
Course materials and website for "Introduction to Data Science with R and Tidyverse" for the May 2022 Course
This repository contains the exercise, solution, and data used by the application described in Jürgensmeier and Skiera (2024): "Generative AI for Scalable Feedback to Multimodal Exercises in Marketing Analytics"
This repository contains the exercise, solution, and data described in Skiera, Bernd and Lukas Jürgensmeier (2024): "Teaching Marketing Analytics: A Pricing Exercise for Quantitative and Substantive Marketing Skills," forthcoming in the Journal of Marketing Analytics.
This repository serves the source files and compiled html files for my personal website
A repository storing code-based slides
An updated R Markdown thesis template using the bookdown package
Research Paper "Un-Black-Boxing Artificial Neural Networks: Predicting and Explaining Bank Customer’s Cross-Sell Likelihood"