DA changed to Part I and II

README.md

@@ -155,18 +155,18 @@ As an example for a coding course, which takes one 100-mins class per week for a
 | Class 04 | Finish: [lecture05-data-exploration](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture05-data-exploration), [lecture06-rmarkdown101](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture06-rmarkdown101)| At this point, should assess students that they understand the basics of coding and make sure nobody is struggling. From this class they should be able to prepare for submitting a project for 6th week's assessment, which should be 2 weeks from this point. |
 | Class 05 | [lecture07-ggplot-indepth](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture07-ggplot-indepth), [lecture08-conditionals](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture08-conditionals) | This class provides some room for repetition or clarifying concepts.
 | Class 06 | [lecture09-loops](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture09-loops), [lecture10-random-numbers](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture10-random-numbers) and [lecture11-functions](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture11-functions) | Should be a more relaxed class as during these days there are many (other) assessment for student and concentrate more on the joy of programming. Many students may already know this material, try to come up with some entertaining tasks for them as well. |
-| Class 07 | [lecture12-intro-to-regression](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture12-intro-to-regression), [lecture13-feature-engineering](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture13-feature-engineering) | Feature engineering is new material, but fits here quite well. Class 07 should be **after** first class from Data Analysis 2, which discusses Chapter 7. |
+| Class 07 | [lecture12-intro-to-regression](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture12-intro-to-regression), [lecture13-feature-engineering](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture13-feature-engineering) | Feature engineering is new material, but fits here quite well. Class 07 should be **after** first class from Part II, which discusses Chapter 7. |
 | Class 08 | [lecture14-simple-regression](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture14-simple-regression) | Great opportunity for in-class (team) work for students with live coding. |
 | Class 09 | [lecture15-advanced-linear-regression](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture15-advanced-linear-regression) | Make sure students covered Chapter 10 from the book. If not, spatial data visualization is a great substitute here. |
-| Class 10 | [lecture16-binary-models](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture16-binary-models) | In some cases this material is covered as a Data Analysis 2 seminar class. This provides an opportunity to fill any gaps or make class 12 not so dense, by jumping to the next class's material. |
+| Class 10 | [lecture16-binary-models](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture16-binary-models) | In some cases this material is covered as a seminar from the course that discusses Part II. This provides an opportunity to fill any gaps or make class 12 not so dense, by jumping to the next class's material. |
 | Class 11 | [lecture17-dates-n-times](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture17-dates-n-times), [lecture18-timeseries-regression](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture18-timeseries-regression) | If short in time, skip [lecture17-dates-n-times](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture17-dates-n-times) |
 | Class 12 | [lecture19-advaced-rmarkdown](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture19-advaced-rmarkdown), [lecture20-basic-spatial-vizz](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture20-basic-spatial-vizz) | Two paths: discuss [lecture19-advaced-rmarkdown](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture19-advaced-rmarkdown) in detail with the whys as well, but then there is no time for [lecture20-basic-spatial-vizz](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture20-basic-spatial-vizz). Or stick with the technical details in both lectures, which allows higher probability to finish. |
-| Class * | [lecture20-basic-spatial-vizz](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture20-basic-spatial-vizz) | This lecture seldomly fits into the timeframe of the class, especially if this coding class runs along with Data Analysis 1 and 2 (DA1 and DA2) and serves as a supplement both in coding and understanding the material. However, if there is a mismatch, this class can be flexibly used as a substitute (e.g. DA1 or DA2 are lagging behind) |
+| Class * | [lecture20-basic-spatial-vizz](https://github.com/gabors-data-analysis/da-coding-rstats/tree/main/lecture20-basic-spatial-vizz) | This lecture seldomly fits into the timeframe of the class, especially if this coding class runs along with theory classes for Part I and II and serves as a supplement both in coding and understanding the material. However, if there is a mismatch, this class can be flexibly used as a substitute (e.g. theory class is lagging behind) |
 
 
 ## Our thanks
 
-Thanks to all folks who contributed to the codebase for the course, especially Gábor Kézdi, co-author of the book. But also thanks to [Zsuzsa Holler](https://www.linkedin.com/in/zsuzsa-holler-70bba031/), [Kinga Ritter](https://www.linkedin.com/in/kinga-ritter/?originalSubdomain=es), [Ádám Víg](https://github.com/adamvig96), [Jenő Pál](https://github.com/paljenczy/), [János Divényi](https://divenyijanos.github.io/pages/about-me.html), Gábors' and Ágoston's many students. Big thanks to [Laurent Bergé](https://sites.google.com/site/laurentrberge/software?authuser=0), [Grant McDermott](https://grantmcdermott.com/software/) and [Vincent Arel-Bundock](https://arelbundock.com/#code) for awesome packages and all the help on coding over several years. Finally, for the financial support of the Department of Economics and Business at CEU.
+Thanks to all folks who contributed to the codebase for the course, especially Gábor Kézdi, co-author of the book. But also thanks to [Zsuzsa Holler](https://www.linkedin.com/in/zsuzsa-holler-70bba031/), [Kinga Ritter](https://www.linkedin.com/in/kinga-ritter/?originalSubdomain=es), [Ádám Víg](https://github.com/adamvig96), [Jenő Pál](https://github.com/paljenczy/), [János Divényi](https://divenyijanos.github.io/pages/about-me.html), [Marc Kaufmann](https://trichotomy.xyz/), Gábors' and Ágoston's many students. Big thanks to [Laurent Bergé](https://sites.google.com/site/laurentrberge/software?authuser=0), [Grant McDermott](https://grantmcdermott.com/software/) and [Vincent Arel-Bundock](https://arelbundock.com/#code) for awesome packages and all the help on coding over several years.
 
 
 ## Found an error or have a suggestion?