Seminar Data Science for Economics

Table of Contents

Introduction

this lecture

  • in this lecture, we look at:
    • motivation to learn datascience
    • first assignment

motivation

  • in this course we teach you
    • what is datascience?
    • difference with econometrics
    • prediction vs causality
    • give policy advice based on "unstructured data"
    • to use python, pymc3 and tensorflow

things to learn

  • when you finish this course, you can
    • obtain data
    • clean data (in a reproducible way)
    • do data-project management
    • work with high dimensional data (tensors)
    • simulate your own estimation techniques
    • use a (simple) neural network
    • use cross validation

finishing the course

  • gives you a better understanding
    • how statistics/econometrics work
    • how to do a Bayesian analysis
    • what datascience and AI is
    • what the advantages and limits are of datascience techniques

What do you need?

Software

  • you will be working with jupyter lab
  • if you also want to install things on your computer, use the anaconda distribution
  • use github but for this you do not need to install anything

Rules of the game

to learn python we use datacamp and lectures:

  • you need to finish the datacamp courses in time
  • see the Lecture schedule for the deadlines
  • first try the notebooks yourself, then watch the video if you get stuck
  • you do not learn datascience by passively typing in answers!
  • participate actively by posting github issues for your questions
  • these will be discussed in the tutorials

to get a grade for this seminar:

  • finish Assignment 1 before the deadline
  • Final Assignment
  • check the deadlines for the final assignment

Date: Dept. of Economics, Tilburg University

Author: Jan