Python and R for Data Science |
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| Lecture Notes: | |||||||||||
| Week | Topics | Notes | Assignments |
Due date/ Remarks |
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| 1 |
Deepening the basics of syntax and basic constructions of Python and R languages |
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| 2 | Basics of working with data and data files and their visualization | download | |||||||||
| 3 | Advanced techniques for working with data and data files (import, data cleaning, etc.) | download | |||||||||
| 4 | Advanced data visualization techniques | download | |||||||||
| 5 | Exploratory data analysis, selected advanced statistical methods (correlation, regression analysis, factor, cluster analysis, etc.), inference statistics | download | |||||||||
| 6 | Exploratory data analysis, selected advanced statistical methods (correlation, regression analysis, factor, cluster analysis, etc.), inference statistics | download | |||||||||
| 7 | Basic applications of machine learning methods (selected classifiers or algorithms for regression and clustering) | download | |||||||||
| 8 |
Basic applications of machine learning methods (selected classifiers or algorithms for regression and clustering) |
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| 9 |
Basics of text analysis, sentiment analysis |
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| 10 | Network analysis | download | |||||||||
| 11 | Reports, dashboards and interactive data visualization | download | |||||||||
| 12 | Reports, dashboards and interactive data visualization | download | |||||||||
| 13 | Summary, discussion of assignment of seminar papers | download | |||||||||
