Learning
1- Data Analysis
As a Ph.D. student and depending on the project, I needed to learn how to work with R, Python, MATLAB and supercomputers. I decided to keep a public record of my learning path, in case it might be useful for younger fellow students:
1- Completed a course on Python at Codeacademy in August 2021.
2- Completed basics of working with MATLAB (MATLAB OnRamp and Optimization OnRamp) at MathWorks in September-October 2021.
3- Completed a course on R at Codeacademy in January 2022.
4- Attended a graduate-level course on Spatial Data Analysis at the IU Geography department in Fall 2022.
5- Completed Basic Unix/Linux Skills at IU in November 2022 (I completed it after the "Introduction to Supercomputing course". But based on the content, I think the correct order must be learning "Basic Unix/Linux Skills" before the Supercomputing one).
6- Completed Introduction to Supercomputing at IU in October 2022.
7- Watched videos about working with supercomputers at Indiana University on its Youtube channel in October-November 2022.
8- Completed Learning Linux Command Line on Linkedin Learning in January 2023.
9- Watched videos on spatial econometrics at the Econometrics Academy in February 2023.
10- Attended a workshop on working with Google Earth Engine at the IU Geography department in May 2023. Find a good reference for this tool here.
11- Completed the first three titles in Quantitative Methods with Julia during June-July 2023.
12- Completed Fundamentals of GIS on Coursera during September-November 2023.
13- Reviewed the lecture notes from two courses API-209 and API-210 on quantitative methods during February-April 2024.
14- Studied chapters 3 and 4 of the 2008 book of Angrist and Pischke, Mostly Harmless Econometrics in May 2024.
1-1 Spatial Data Analysis
1- Introduction to the concepts: Econometrics Academy by Ani Katchova
2- Introduction to Geospatial Data Analysis in R by Philippe Marchand
1-1-1 Online Seminars on Spatial Data Analysis
1- Biweekly Spatial and Urban seminars
2- Monthly seminars of the Spatial Econometrics Association
1-2 Other Websites*
1- Machine Learning for Economists by Dario Sansone.
2- Quantitative Economics with Julia by Jess Perla, Thomas Sargent and John Stachurski.
3- Collection of publicly available learning resources in computational economics and structural modeling by Fedor Iskhakov.
4- Computational Economics by Kenneth Judd.
5- Computation Methods for Economists by Jesus Fernandez-Villaverde.
* It's for the introduction. I have not studied their materials yet.
2- Doing Research
1- Read A Guide for the Young Economist by William Thomson in summer 2021.
2- Watched videos of Andrew Stapleton about how to do research and write thesis in July 2022.
3- Watched videos of Andrew Stapleton about how to use ChatGPT for doing research in April 2023.
4- Read Kristin Van Gaasbeck's instruction on how to write economic papers in September 2023.
5- Read John Cawley's guidance about US job market for the economics Ph.D. graduates in February 2024.
2-1 Other Websites
1- Advice for Ph.D. students in economics, by Chris Roth and David Schindler.
2- Resources for Ph.D. students by Shanjun Li.
3- Webpage of Krostoph Kronenberg.
3- Professional Skills
1- Attended Linkedin course on business etiquette in May 2023.
2- Attended Linkedin course on AirTable in May 2023.
3- Attended a workshop on grantsmanship as the Indiana Resilience Institute in June 2023.
4- Completed Grant Management Training Modules at Environmental Protection Agency over June and July 2023.
5- Completed How to Develop a Budget Modules at Environmental Protection Agency in July 2023.