You will need two additional, new Python libraries for this week:
conda install matplotlib
conda install seaborn
You can download Homework 1 using the same commands as we do in CS 225, except doing so within your CS 296 git directory:
git fetch release
git merge release/hw3 -m "Merging initial files"
Complete the hw3.ipynb found within the hw3 directory:
jupyter notebook
To submit Homework 3:
git add -u
git commit -m "<your message>"
git push origin master
You can download Homework 1 using the same commands as we do in CS 225, except doing so within your CS 296 git directory:
git fetch release
git merge release/hw2 -m "Merging initial files"
Complete the hw2.ipynb found within the hw2 directory:
jupyter notebook
To submit Homework 1:
git add -u
git commit -m "<your message>"
git push origin master
This process will be very similar to the one you used for CS 225.
Create your CS 296 repository by visiting:https://edu.cs.illinois.edu/create-ghe-repo/cs296-25-sp19
Clone your newly created repository:
git clone (YOUR REPO URL)
cd NETID
git remote add release https://github-dev.cs.illinois.edu/cs296-25-sp19/_release.git
You can download Homework 1 using the same commands as we do in CS 225:
git fetch release
git merge release/hw1 -m "Merging initial files"
If you have never used Jupyter, you may need to install Jupyter and Python with the following commands:
conda install jupyter
conda install pandas
Once Jupyter is installed, launch Jupyter notebooks with the following:
jupyter notebook
Finally, you can go into hw1 and complete the hw1.ipynb notebook.
To submit Homework 1:
git add -u
git commit -m "<your message>"
git push origin master
Each semester, CS 225 offers a one credit hour honors section that covers an advanced topic in CS related to data structures (offered as CS 296, Section 25). This is the honors component to receive James Scholar or HCLA credit for CS 225. However, it is not necessary to be part of an honors program to participate in CS 296 – anyone can join (see “Prerequisites” below)!
As an honors course, CS 296 will be much less structured than CS 225, require significant independent work and learning, and we expect you to go above and beyond what you would normally do as part of a regular course. Students in the past have learned a lot, had a lot of fun, and created amazing projects.
This semester, CS 296 will focus on data science with a focus on creating meaningful and impactful visualizations. Data analysis will be done primarily in Python while data visualization will be done using d3.js.
As part of the course, you will complete multiple projects (at least one group project and at least one solo project). The following links shows project submissions from a similar course:
CS 296 meets every Thursday, starting February 14 at 5:00pm.
We expect that you will be taking CS 225 at the same time, but that is not usually strictly necessary. If you are not enrolled in CS 225, you must have credit for CS 225.
If the honors section fills up, priority will be given to current CS 225 students.
Since CS 296’s first meeting is not until several weeks into the semester, you may not have registered for it during normal registration. We are more than happy to approve any late-adds for you to add the course up until the second meeting of the class.