Dates: 8, 9, 13 and 14 October 2020
Time schedule: 16:00-20:00 (GMT+2)
Location: Online learning thorough UNIA’s virtual campus. This comprehensive webinar is hosted through Blackboard and runs over a total of 16 hours. Four days of online instruction for four hours per day. Although we recommend joining the seminar live, participants also have the option to take the course asynchronously by viewing the lecture videos at your convenience. The video recordings will be available within 24 hours of each session, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.
Delivered by: Achim Ahrens, PhD. ETH Zürich, Public Policy Group
Overview
The proliferation of large and complex datasets has opened several avenues for research. Researchers of different disciplines are exploring and using alternative approaches that go beyong the traditional methods in their repective scientific fields. A prominent example is the field of Machine Learning, which has its origins in the intersection of statistics and computer science, but has recently gained traction in social science, economics, psychology, epidemiology and marketing, among others.
The advantages of Machine Learning for classficiation and prediction tasks have generated a growing interest for these methods. Responding to this trend, this course offers a first approach to Machine Learning techniques using Stata, aimed at Stata users that are not yet fimilar with Machine Learning.
This workshop will attempt to demystify the field of Machine Learning and compare it with traditional statistical approaches in economics and social sciences. We discuss relative strengths and weaknesses and how machine learning can facilitate causal inference.
Prerequisites
Knowledge of basic statistics and econometrics (e.g. OLS, logit), Stata (introductory level at least) is required. The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the socio-economic and life sciences.
Methodology
Over four days, participants will join the course via Blackboard to participate in lectures and interact with the instructor. Although we recommend joining the seminar live, participants also have the option to take the course asynchronously by viewing the lecture videos at your convenience. The video recordings will be available within 24 hours of each session, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously. Temporary, time limited licences for the software(s) used in the course will be provided. You are required to install Stata (provided prior to the start of the course) and Python prior to the course.
Registration
Registration closes 2 calendar days prior to the start of the course (6th october 2020)
Fees: 168 euros
How to pay: Fill in and submit the application form (http://unia.es/solicitudCursoSTATA) to larabida@unia.es. The payment must be done by bank transfer. Please send us proof of the transaction by e-mail joint to your identification card or passport.
Instructor
Achim Ahrens joined the Public Policy Group in September 2019 as a Data Scientist. Achim has an undergraduate degree in Economics and Social Sciences from the University of Erfurt, an MSc in Economics from the University of Edinburgh and holds a PhD from Heriot-Watt University. Before joining the Public Policy Group, he was a Post-doctoral Research Fellow at the Economic and Social Research Institute in Dublin, Ireland. Achim has worked on empirical projects in a wide range of fields including housing markets, energy economics and conflict research. Achim has a strong interest in applied econometrics, causal inference, machine learning and spatial econometrics.
| Coordination | |
| Emilio Congregado | |
| More info: escuela.stata@gmail.com | |
Sponsored and organized by:
| Fundación Pública Andaluza Centro de Estudios Andaluces |
| Universidad Internacional de AndalucíaTimberlake Consulting |
| MSc and PhD en Economía, Finanzas y Computación, Universidad de Huelva y Universidad Internacional de Andalucía) |