edits

README.rst

@@ -25,27 +25,13 @@
 
     -- Richard Hamming.
 
-The sound analysis of computational economic models requires expertise in economics, statistics,
-numerical methods, and software engineering. We first provide an overview of basic numerical methods
-for optimization, numerical integration, approximation methods, and uncertainty quantification.
-We then deepen our understanding of each of these topics in the context of a dynamic model of human
-capital accumulation using `respy <https://respy.readthedocs.io>`_. We conclude by showcasing basic
-software engineering practices such as the design of a collaborative and reproducible development
-workflow, automated testing, and high-performance computing.
-
-Students learn how to use `Python <https://www.python.org>`_ for advanced scientific computing.
-They acquire a toolkit of numerical methods frequently needed for the analysis of computational
-economic models, obtain an overview of basic software engineering tools such as
-`GitHub <https://www.github.com>`_ and `pytest <https://docs.pytest.org>`_, and are exposed to
-high-performance computing using
-`multiprocessing <https://docs.python.org/3/library/multiprocessing.html>`_
-and `mpi4py <https://mpi4py.readthedocs.io>`_.
-
-We build the course on the `Nuvolos.cloud <https://nuvolos.cloud>`_ as an integrated research and
-teaching platform. The platform provides a simple, browser-based environment that allows for
-complete control over students’ computational environment and simplifies the dissemination of
-teaching material. It enables students to seamlessly scale up their course projects from a
-prototype to a high-performance computing environment.
+The sound analysis of computational economic models requires expertise in economics, statistics, numerical methods, and software engineering. We first provide an overview of basic numerical methods for optimization, numerical integration, approximation methods, and uncertainty quantification. We then deepen our understanding of each of these topics in the context of a dynamic model of human capital accumulation using `respy <https://respy.readthedocs.io>`_. We conclude by showcasing basic software engineering practices such as the design of a collaborative and reproducible development workflow, automated testing, and high-performance computing.
+
+Students learn how to use `Python <https://www.python.org>`_ for advanced scientific computing. They acquire a toolkit of numerical methods frequently needed for the analysis of computational economic models, obtain an overview of basic software engineering tools such as `GitHub <https://www.github.com>`_ and `pytest <https://docs.pytest.org>`_, and are exposed to high-performance computing using  `multiprocessing <https://docs.python.org/3/library/multiprocessing.html>`_ and `mpi4py <https://mpi4py.readthedocs.io>`_.
+
+Guest lectures organized by institutions from the public and private sector are an integral part of my teaching curriculum. These events connect students directly with employment opportunities that match their interests and skill set and provide students with insights into scientific computing applications in a variety of settings.
+
+We build the course on the `Nuvolos.cloud <https://nuvolos.cloud>`_ as an integrated research and teaching platform. The platform provides a simple, browser-based environment that allows for  complete control over students’ computational environment and simplifies the dissemination of teaching material. It enables students to seamlessly scale up their course projects from a prototype to a high-performance computing environment.
 
 
 Textbooks

index.rst

@@ -13,7 +13,7 @@
    lectures/index
    resources/index
    references/references
-   iteration/lecture_plan_2020
+   iteration/lecture_plan_2021
 
 .. include:: README.rst
    :start-line: 43

iteration/lecture_plan_2020.rst → iteration/lecture_plan_2021.rst

@@ -2,11 +2,11 @@
 Organization
 ###############
 
-We start on October 27th, 2020, and meet on Tuesdays (2:00-3:30 PM) and Wednesdays (8:30-10:00 AM).
+We start on October 12th, 2021, and meet on Tuesdays (2:00-3:30 PM) and Wednesdays (8:30-10:00 AM).
 
 **Lecturer:** `Philipp Eisenhauer <https://peisenha.github.io>`_
 
-**Teaching Assistant:** `Annica Gehlen <https://www.iame.uni-bonn.de/people/annica-gehlen>`_
+**Teaching Assistants:** `Emily Schwab <https://github.com/s6emschw>`_ and `Carolina Alvarez <https://github.com/carolinalvarez>`_
 
 We will conduct all course communications using the bonn-econ-teaching `Zulip <https://zulip.com>`_ chat, so please be sure to join us there. To join the Zulip organization, please click on the button below.
 
@@ -87,7 +87,7 @@ We then reconvene at the beginning of the new year to discuss some selected issu
 +------------+---------------------------------------------------------------------------------------------------------------+
 | 20/01/2021 | Macroeconomics using `pydsge <https://pydsge.readthedocs.io/>`__  by Gregor Boehl (OSE, University of Bonn)   |
 +------------+---------------------------------------------------------------------------------------------------------------+
-| 26/01/2021 | Guest lecture by Limes-Institute  (University of Bonn)                                                        |                                     
+| 26/01/2021 | Guest lecture by Limes-Institute  (University of Bonn)                                                        |
 +------------+---------------------------------------------------------------------------------------------------------------+
 | 27/01/2021 | Bayesian estimation using `pyabc  <https://pyabc.readthedocs.io/>`__ (Hasenauer Lab, University of Bonn)      |
 +------------+---------------------------------------------------------------------------------------------------------------+