- project-based
- student collaboration via github/discord
- mainly MGIMO students and partner organisations
- free to all who qualify
- sandbox
->many, many hardships->space rockets - map your learning trajectory (who is the trajectory owner?)
- data / model / domain application (in business/research)
- skills outside programming matter (personal/team communication, publicity, management, budgeting)
- what 'data science' means for finance / economists?
- CLI vs GUI
- git is ok (code reuse + remote repos + issues + collaboration + CI)
- power of plain text (markdown, todo.txt)
- programming languages - start from print("Hello, world!")
- 'blackbox' / 'silver bullet' / 'I need just push a button' / hypes (vs small wins)
- tribalism (eg R vs Python), vendor overprimising ('Nobody gets fired for buying IBM')
- gatekeepers (everything is so hard)
- scams, overpromising (everything is easy)
- map realistic learning trajectory in reproducible data analysis, including risks and obstacles
- demonstrate and discuss finished/published work as motivational examples in economic data analysis
- get acquainted with personal success stories in data analysis and programming
- delimit 'data science' vs economic analysis vs data analysis in other fields (NLP, law)
- try and practice various parts of 'data-model-decision' workflow, bridge gaps between textbook and real world examples