This course is part of the MS in Data Science program at the University of San Francisco.
The goal is to give students a deeper and more general view of data structures and algorithms. While students have examined a number of data structures, such as binary trees, already, this course provides a much more in-depth study. This depth will benefit them greatly in the advanced machine learning course. This course also tends to address many of the difficult algorithm questions students get during job interviews. The critical data structures covered in this class are: lists, linked lists, trees, graphs, hash tables, and tries. The course also covers a variety of common and useful recursive and non-recursive algorithms, such as searching and sorting.
At the end of the last module, we studied feature importance and briefly touched on clustering. There was not time to study these topics in more detail through projects, but they are important and students report getting related interview questions extremely frequently. Consequently, while seemingly a bit out of place, the faculty has decided to include free-form reports on these machine-learning related topics in this data structures and algorithms class. Here are two quick comments I received from the last cohort concerning these projects:
By the end of this course, students will be able to:
- Describe object-oriented programming and use objects to construct linked lists, trees, and graph data structures
- Identify the essential logic and algorithms in the code of others
- Implement critical algorithms, such as sorting, searching, and list manipulations
- Create Trie data structures
- Design recursive algorithms
- Analyze and compare algorithmic complexity using “big-O” notation
INSTRUCTOR. Mustafa Hajij.
SPATIAL COORDINATES:
Class is held at 101 Howard 527
Lectures are Thursday
10:00 am - 11:50 am (PT) for section 1
1:00 pm - 2:50 pm (PT) for section 2
INSTRUCTION FORMAT. Class runs for 1:50 hours 1 day/week. Instructor-student interaction during lecture is encouraged and we'll mix in mini-exercises / labs during class. All programming will be done in the Python 3 programming language, unless otherwise specified.
TARDINESS. Please be on time for class. It is a big distraction if you come in late.
| Artifact | Grade Weight | Due date |
|---|---|---|
| OO hashtable | 8% | Fri, Feb 3 11:59pm |
| Clustering | 17% | Sat, Feb 25 11:59pm |
| Feature selection and importance | 20% | Mar 11, 2023 11:59pm |
| Exam 1 | 25% | Thur Feb 17 --- |
| Exam 2 | 30% | Fri Mar 10 --- |
Note: In order to pass this course, students are expected to receive at least 50% of each category. E.g., receiving less than 29/60 on your quiz might not be a satisfying grade.
The oohtable project will be graded with the specific input or tests given in the project description, so you understand precisely what is expected of your program. (There are a few hidden tests though.) Consequently, projects will be graded in binary fashion: The tests either work or they do not. The only exception is when your program does not run on the grader's or my machine because of some cross-platform issue. This is typically because a student has hardcoded some file name or directory into their program. In that case, we will take off a minimum of 10% instead of giving you a 0, depending on the severity of the mistake. Please go to github and verify that the website has the proper files for your solution. That is what I will download for testing.
Each project has a hard deadline and only those projects working correctly before the deadline get credit. My grading script pulls from github at the deadline. All projects are due at the start of class on the day indicated, unless otherwise specified.
Grading standards. I consider an A grade to be above and beyond what most students have achieved. A B grade is an average grade for a student or what you could call "competence" in a business setting. A C grade means that you either did not or could not put forth the effort to achieve competence. Below C implies you did very little work or had great difficulty with the class compared to other students.
- Review OO notebook and Operator overloading notebook
Here is a great free book on algorithms by Jeff Erickson.
A great book that is not free is here Introduction to Algorithms
A very useful set of programming-concepts-for-data-science and data science coding questions by former USF MSDS student Shikhar Gupta.
10 Steps to Solving a Programming Problem
- Welcome! (Day 1)
- Hashtable
- Review hash table construction from MSDS692 project
- self review hash table videos
- discuss OO htable project
- How to read code (Day 1)
- Problem-solving (Day 1)
- Core data structures (Day 2)
- Introduction to algorithm complexity (“Big-O” notation) (Day 2,3)
- Getting a grip on recursion (Day 3)
- Walking data structures (Day 4)
- Sorting (Day 4)
- Searching (Day 5)
- Searching notebook
- Instead of LeetCode, implement the TRIE search found mentioned in slides
- Graphs (Day 5, 6)
- Graphs notebook
- Graph Algorithms Video
- Graph Algorithms Lecture
- DL Application of Topological Sort
- Problem-solving exercise: LeetCode backtracking (Walking graphs often involves backtracking)
- Graph algorithms in ML
The use of recursive algorithms will be emphasized frequently and wherever appropriate to reinforce this critical mechanism. Discussion of formal algorithm complexity will also be emphasized throughout the lectures.
ACADEMIC HONESTY. You must abide by the copyright laws of the United States and academic honesty policies of USF. You may not copy code from other current or previous students. All suspicious activity will be investigated and, if warranted, passed to the Dean of Sciences for action. Copying answers or code from other students or sources during a quiz, exam, or for a project is a violation of the university’s honor code and will be treated as such. Plagiarism consists of copying material from any source and passing off that material as your own original work. Plagiarism is plagiarism: it does not matter if the source being copied is on the Internet, from a book or textbook, or from quizzes or problem sets written up by other students. Giving code or showing code to another student is also considered a violation.
The golden rule: You must never represent another person’s work as your own.
If you ever have questions about what constitutes plagiarism, cheating, or academic dishonesty in my course, please feel free to ask me.
Note: Leaving your laptop unattended is a common means for another student to take your work. It is your responsibility to guard your work. Do not leave your printouts laying around or in the trash. All persons with common code are likely to be considered at fault.
USF policies and legal declarations
Students with Disabilities
If you are a student with a disability or disabling condition, or if you think you may have a disability, please contact USF Student Disability Services (SDS) for information about accommodations.
Behavioral Expectations
All students are expected to behave in accordance with the Student Conduct Code and other University policies.
Academic Integrity
USF upholds the standards of honesty and integrity from all members of the academic community. All students are expected to know and adhere to the University's Honor Code.
Counseling and Psychological Services (CAPS)
CAPS provides confidential, free counseling to student members of our community.
Confidentiality, Mandatory Reporting, and Sexual Assault
For information and resources regarding sexual misconduct or assault visit the Title IX coordinator or USFs Callisto website.