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