Roman Jurowetzki, roman@business.aau.dk Tampere, 14/8 - 2019
Here you will find 3 notebooks with the tutorials for today. They are probably more detailed than what we can cover in one day. The Notebooks run using Google Colab, which is an online version of a Jupyter Notebook with Python 3.
-
Part1 – Exploratory Data Analysis and brush-up:
-
Part2 – Introduction to Machine Learning:
-
Part3 – Statistical Natural Language Processing 101:
Rakas, M and Hain, D.S. (under review). "Innovation System Research: Where It Came From, and What It Is Now – A Bibliometric Network Analysis" --> Available open access here
Vosviewer Easy software for bibliometrics
Citespace More complex bibliometrix software including geospacial features and mapping.
-
Jake VanderPlas: Python Data Science Handbook - Essential Tools for Working with Data https://jakevdp.github.io/PythonDataScienceHandbook/index.html
-
Steven Bird, Ewan Klein, and Edward Loper: Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit; https://www.nltk.org/book/
Athey, S., & Imbens, G. W. (2017). "The state of applied econometrics: Causality and policy evaluation". Journal of Economic Perspectives, 31(2), 3-32. --> Available open access here
Athey, S. (forthcoming). "The Impact of Machine Learning on Economics", in The Economics of Artificial Intelligence: An Agenda. University of Chicago Press, January 2018 --> Available open access here
Varian, H. R. (2014). "Big data: New tricks for econometrics". Journal of Economic Perspectives, 28(2), 3-28. --> Available open access here
Einav, L., & Levin, J. (2014). "The data revolution and economic analysis". Innovation Policy and the Economy, 14(1), 1-24. --> Available open access here
Mullainathan, S., & Spiess, J. (2017). "Machine learning: an applied econometric approach". Journal of Economic Perspectives, 31(2), 87-106. --> Available open access here
Datacamp Online courses. Intro to R, Python, Github, Excel and Sheets are free Recommended courses:
- R basics: "Introduction to R" (free course)
- R unsupervised ML: "Unsupervised Learning in R" (chapter 1 free)
- R Supervised ML: "The Machine Learning Toolbox" (chapter 1 free, introduces to the caret train-control workflow)
- R Data visualization: "Data Visualization with ggplot2 (Part 1)" (chapter 1 free)
- Datacamp Tutorials: Free R & Python Tutorials for specific problems and methods
Dataquest Similar to datacamp. Python focused. Also more advanced courses on data engineering
Open Data Science Masters Curriculum Collection of free online resources on all kinds of Data Science topics.
Data and scripts from the ML A-Z course from Udemy R and Python scripts from the course including the course data. The course can be found on Udemy and is usually available for around 12USD.
Installing the RStudio IDE on your machine
Network analysis and visualization software
Stackoverflow: Programming help & advice forum
Informative podcast about professional analytics
R-Bloggers:R news and tutorials