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+# First Assignment
+
+## Class Expectations
+
+
+I assume this just is supposed to be some kind of lorem ipsum placeholder text.
+However, in case it is not here are a few words on my expectations of this
+class: After finishing my Master's thesis in political science last summer,
+I felt that the approach I pursued within this thesis, which can be characterized as
+computer-driven text analysis which was used to determine political preferences of political
+actors on the basis of speeches they delivered and press statements they
+released, had some potential if applied the right way. Packages for r like
+*wordfish* enabled me to do so on my own. However as an autodidact (and way more
+an political theorist than a computer scientist) both the preparation of the
+text corpora and the analysis had some flaws which made things like text mining,
+modelling as well as the interpretation and modification of the resulting tables
+and graphs more cumbersome than probably necessary. Coming from this starting
+point, I hope this course will make it possible for me to use the things I
+already know in a more structured, informed and faster way. Of
+particular interest for me are data visualization principles. After producing
+long essays and theses for a while now, I certainly appreciate a well made graphical
+representation of complex data.
+
+
+### Autodidactic Basis
+
+Websites I use as a self-taught r-user interested in text analysis:
+
+* quantitative text mining on [textasdata.com](http://www.textasdata.com/)
+
+* Some fancy applications of r, both descriptive statistics and more advanced
+    stuff on [r-bloggers](https://www.r-bloggers.com/)
+
+## Top Expectations
+
+* No more hand-cleaning of 2000 parliamentary speeches
+
+* Being able to have a look under the surface of r-packages. How does topical clustering work from a computer-scientist's view and how do I avoid weird outcomes and bad graphical representation?
+
+* Better Visualization! Maps in r! What does aes do?