Updated readme.

Author: jtkiley

readme.markdown

@@ -46,10 +46,10 @@ segment | topic
 
 ## Materials
 
-The materials for this course are available in a course Dropbox shared with participants, and they are also posted in a public Github repository.
+The materials for this course are available in a public Github repository.
 
-There is a notebook and slide deck for each segment.
-The slides are available in zip archives containing Keynote and Powerpoint versions in the releases section (on Github) and in a subfolder in the course Dropbox.
+There is both a notebook and a slide deck for most segments.
+The slides are available in zip archives containing Keynote and Powerpoint versions in the releases section (on Github).
 Please note that the Keynote slides are (usually) the ones actually presented.
 
 Also, there is an `environment.yml` file for setting up your Anaconda environment, using the instructions below.
@@ -63,11 +63,20 @@ If you encounter issues, get as far as you can, and we will work through them in
 **Please note:** It is best to install (and work with) this software on a physical computer (i.e. not virtualized) that is not locked down with IT permissions.
 
 
+### Download materials
+
+1. On the right (on Github), click "Releases".
+1. Find the release for your course (usually at the top), and click the link for "Source code (zip)" under the "Assets" heading to download a zip archive of the course materials. **Note:** this is also where the slides are located (usually uploaded the day before the course begins).
+1. Expand the zip archive, which will produce a folder (e.g., `carma_python-1.1.0`) with the course materials inside.
+1. Move this folder somewhere easy to find (e.g., your desktop) while the course is ongoing.
+
+
 ### Install software
 
 1. Install [Anaconda, Python 3.8 version](https://www.anaconda.com/distribution/).
-1. (optional, but encouraged) Install Microsoft Visual Studio Code. The Anaconda installer asks if you would like to install it.
-1. (experts-only alternative) Install miniconda instead of the GUI version. While there are direct download versions, you would typically use a package manager (e.g., brew on macOS, apt on Ubuntu). Similarly, you could install VS Code with your package manager as well.
+1. (experts-only alternative) Install miniconda instead of the GUI version. While there are direct download versions, you would typically use a package manager (e.g., brew on macOS, apt on Ubuntu).
+
+**Note:** If you have a new Apple Silicon Mac, the easiest thing to do is to follow the instructions above, which will install Anaconda using Rosetta 2 (see this [Anaconda blog post](https://www.anaconda.com/blog/apple-silicon-transition)). They're fantastic computers, though this is one area where the native software is taking a bit to catch up.
 
 
 ### Importing the Anaconda environment
@@ -79,25 +88,6 @@ If you encounter issues, get as far as you can, and we will work through them in
 1. Back in the import popup, the environment name should be filled in automatically from the file, `carmapy` in this case. Click Import.
 1. Wait for the packages for the environment to be downloaded and installed. This could take a few minutes.
 
-**Note:** there is also a file named `environment_full.yml`.
-This file is much more specific about particular software versions, and it is largely specific to both macOS and particular hardware.
-I include it for documentation reasons, but you should generally use the more general (i.e. compatible) `environment.yml`.
-
-
-### Install the Jupyter Lab Extension for Plot.ly
-
-1. Open a terminal (on Windows, use the prompt labeled either "Anaconda Prompt" or "Anaconda (64-bit)" in the start menu).
-1. Activate the `carmapy` environment using the command `conda activate carmapy`.
-1. Install the extensions using these commands:
-
-```
-jupyter labextension install [email protected]
-jupyter labextension install @jupyter-widgets/jupyterlab-manager [email protected]
-
-```
-
-**Note:** On my desktop, each command listed above takes between two and three minutes to complete, so give it time. For your reference, these instructions are adapted from the [plot.ly getting started](https://plot.ly/python/getting-started/) document.
-
 
 ### Install TextBlob text corpora
 
@@ -116,14 +106,14 @@ You can run the notebook that we will use in the first session as a test of whet
 1. Click the dropdown and select `carmapy` which the environment you installed in the instructions.
 1. After a few seconds, the main window will refresh. Then, find "Jupyter Lab" and click the "Launch" button.
 1. A browser window will pop up, and the Jupyter Lab interface will load. Note: if you appear to have a blank page, make sure you are using a modern browser like Safari, Chrome, or Firefox as your default. Older versions of Microsoft's browsers (even on Windows 10) lack modern features, though the newest Microsoft Edge browser should be fine.
-1. On the left side of the Jupyter Lab interface, use the file browser to navigate to the location where you saved the `0a_intro.ipynb` notebook and double click it. Note: if you are using the Dropbox version, the folder is read only, so you should copy the files somewhere convenient.
+1. On the left side of the Jupyter Lab interface, use the file browser to navigate to the location where you saved the `0a_intro.ipynb` notebook and double click it.
 1. Once it loads, click the "Kernel" menu in the menu bar (inside the Jupyter Lab interface), and then click "Restart Kernel and Run All Cells..."
 1. The notebook should run quickly, and you should not see errors. Note: the single most common issue with any import errors at the top is that you have not selected the environment in step 3 above. You need to do that before launching Jupyter Lab, and a subsequent change will not affect the already--running Jupyter Lab.
 
 
 ## About Jason
 
-Jason T. Kiley is an Assistant Professor and Spears Fellow at Oklahoma State University.
+Jason T. Kiley is an Assistant Professor at Clemson University.
 His research examines the interplay of audience perceptions of firms, impression management, and their associations with outcomes, including recent publications in the Academy of Management Journal and Strategic Management Journal.
 As part of his work, he works to advance the use of software to increase the range, efficiency, and rigor of conducting empirical research.
 He is a co-organizer of the annual AOM Content Analysis PDW, and his published and in-progress work often uses state-of-the-art content analysis techniques, including recent work with semantic text analysis and machine learning.