Preliminaries
CS 331: Data Structures and Algorithms
Michael Lee <lee@iit.edu>
Michael Lee
- lee@iit.edu
- http://moss.cs.iit.edu
- Of ce hours by appointment (Tue/Thu 11AM-1PM)
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Agenda
- Course overview & Administrivia
- Prerequisites
- Topics & Resources
- Grading
- Dev environment & Class procedures
Data Structures
- How do we store, organize, and retrieve data on a computer?
& Algorithms
- How can we ef ciently (in space/time) carry out some typical data
processing operations?
- How do we analyze and describe their performance?
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Prerequisites
- I assume you are …
- uent in some programming language
- familiar with procedural & OO paradigms
- comfortable with development processes:
- compilation, debugging, testing
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Python
- We’ll use the Python programming language to explore data
structures & algorithms
- Easy-to-learn, clean (“one obvious way to do” things), and
popular language
- Ton of useful, powerful libraries
Topics
- Python crash course
- Algorithmic analysis
- Linear data structures (Lists, Stacks, Queues)
- Mapping structures (Hashtables and Trees)
- Recursion
Class Resources
1. Course website: moss.cs.iit.edu/cs331
- static information
- lecture calendar, slides, links to
external resources, etc.
Class Resources
2. Google Colaboratory
- interactive lecture notebooks
Class Resources
3. Blackboard
- Final gradebook
Class Resources
4. GitHub
- Assignment distribution & submission
Supplements
- The Python Tutorial (docs.python.org/3/)
- Problem Solving with Algorithms and
Data Structures Using Python
Grading
- 40% Programming Assignments
- 60% Exams (3 total: 2 midterms + nal)
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Programming Assignments
- ~10 assignments
- All assignments are retrieved and submitted via GitHub
- Provided codebase typically covered in preceding lectures
- Late policy: elastic over summer
- Hard due date for everything: June 24
On Exams
- Tentative dates are on course website (June 3, June 10)
- All exams are cumulative (but will focus on recent material)
- Will be administered online, to be taken on your own
computer (or in lab)
- Later exam scores, if higher, will improve earlier ones
>> scores = [60, 80, 75]
>> [max(scores[i:]) for i in range(3)]
[80, 80, 75]
>> scores = [75, 80, 100]
>> [max(scores[i:]) for i in range(3)]
[100, 100, 100]
Jupyter Notebook
- In-browser Python development platform
- “Cells” can contain plain text, code, output (and more)
- All lecture notes will be distributed as notebook les
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Jupyter Notebook
- We strongly recommend installing Microsoft Visual Studio
Code (VSCode) as an IDE for both lectures & labs
- Install the Jupyter and Python extensions
Interactive Lectures
- Lecture notebooks available in course repository
- Open on Google Colab or VSCode
- Class is usually one long interactive demo
- Starter and completed lecture notebooks are available in the
course GitHub repository
