dpploy / chen-3170 Goto Github PK

The raw.githubusercontent.com URL is returning a 404 error for some hosted images. The issue is found at the moment in notebooks: 03-arrays and labwork-03-801-802. However, the hosted image for 04-arrays-operations is working.

I think this notebook lays out the different kinds of variables very well. I think that there could be more in depth information on how to use structures such as dict() or list() since they are used substantially throughout the rest of the course. Having the big blocks of code broken up by the comments is good but there are still sections that get very complicated quickly such as the 'Systematic operation on all elements of periodic_table' cell.

The pre-made handwritten notes aren't a useful way for students to understand the math (in my opinion). It would be much better if you did this math in class along with the students so they can actively see the process instead of looking at it as you discuss the steps.

This notebook was very easy to understand based on the information in Notebook 10 and was not too difficult to put together for the weekly assignment. Once the matrix was assembled, the rest of the notebook guided students on the plotting of information as well as adding additional data points.

This notebook is full of useful information but I feel as though we did not spend a lot of time going over all of it. It kind of felt as though this section was very rushed when it is used a lot in future labs and in the midterm. Definitely spend more time going over line by line what each string of code means and what it does.

This notebook is full of useful information but it can be a bit overwhelming. I would suggest breaking it up into 2 notebooks with one notebook discussing matrices and the code implementation (filtering, 2D and 3D arrays) and the other discussing the remaining information (plots, how to set up matrices, etc.).

Not an issue within the notebook, but possibly increase the amount of assert statements that should be included in the labs (at least the earlier ones). Those can be useful in teaching why certain code doesn't work besides the program just giving an error. Also, I would teach while loops along with the rest of the information in this notebook, especially since we never got around to really using them and they can be very useful for looping code.

About getting the tools

From the same initial assumptions of 00-syllabus.ipynb, to avoid those famous dependency issues related to getting the suggested packages and software, It'd be a good idea to add the following package managers as suggestions for the not-so-die-hard-programmers:

On Windows:

• Chocolatey

On macOS:

• Homebrew

On Linux:

• Nixpkgs, for a sandbox approach.

About Anaconda

On line 74 of 00-syllabus.ipynb, it's suggested the students install Anaconda as a way of getting the Python-Jupyter resources.
I'd like to differ and recommend the smaller version of Anaconda, Miniconda, which is also readily available at https://conda.io.

Miniconda is considerably smaller than Anaconda and the user can add packages as they're need, where Anaconda has a much larger prepackaged toolchain which most of the time goes on unused.

On the matter of the resources available.

Google offers two very capable free options to the ones already mentioned in line 72 of 00-syllabus.ipynb.

• Colaboratory,
• Kaggle

Which have the following attributes.

• Production and execution of arbitrary python code through the browser,
• Both are especially well suited for:
  • machine learning,
  • data analysis and
  • education.

Technically, those are hosted Jupyter notebook services requiring no initial setup to use while providing free access to Google's computing resources including CPUs, TPUs and GPUs through the web browser.

Additionally one might run those notebooks locally through the jupyter_http_over_ws available at PyPl.

This notebook is a very god description of vectors and an intro to matrices. The only thing I would add to this notebook is in the matrix section, possibly explain the plot functions in more detail instead of just using them. We use the plot functions alot but I've noticed myself just copying the code from the notebooks instead of knowing how to use it myself.

This notebook definitely contained a lot of vital information that was used throughout the rest of the course. Definitely make sure that at this point students are comfortable with this information in lecture.
I feel that we did not discuss a lot about the sub-mechanisms in lecture and during the lab period and may be why some people struggled with this section in the midterm project. The extra-help class that you held really helped to clarify everything and put all of the pieces together as you were actively writing the code with us. I feel as though the importance of the plots at the end of this notebook were not explained in lecture.

There is a lot of complex math behind all of this code and I would recommend spending a little bit more time on it if you can to make sure students fully understand it. Once the first few blocks of code are described (based on the math), it does help clarify things and make the information easier to apply to a real example.

The lu_factorization, as you probably know, was the hardest code for people to write because of all the different steps it requires. I think going through this idea on paper more in class would help people understand how the code itself should work instead of the class needing you to give them the code. While knowing how the code works to solve the lu is useful, unless it is taught better during class people will not be able to accomplish the task of writing it.

Other then the lu, the rest of the notebook outlines the reaction rate vector information very well.

The actual math behind Newton's method is not complex at all, but I would definitely spend more time describing the code aspect of everything to students. In this notebook I feel as though there is a lot going on so it is kind of hard to keep-up.

Having us write our own function codes that already exist in Python was extremely difficult and note really needed. In my opinion it was kind of pointless to re-write function codes that already exist (forward solve, backward solve, and lu_factorization function codes). This was very challenging and students were left to figure out the complex code without enough practice to write it.
I would definitely recommend providing more guidance if you are going to have students write function codes like that early on, or move it near the end of the semester so that students will have a better understanding of how to write code.

Delete the use of Anaconda software, it was too much of a hassle to use this program. Not to mention if you used the Anaconda program you could not access any of the data files on the notebook.

The interactive visualization does not work because it is just a copy of the Fourier Notebook 12. Needs a complete update.