SD 212 Spring 2024 / Admin

Course Policy Statement

Official PDF version

1 Instructors

  • Prof. Daniel S. Roche, 438 Hopper Hall, x36814, (Coordinator).

  • Prof. Luke McDowell, 439 Hopper Hall, x36801, .

2 MGSP Leaders

  • 2/C Grace Blote, 5th company,

  • 2/C Ben Keenan, 9th company,

3 Grading

The work of the class consists of:

  • Homeworks (2-3 per week)
  • Labs (every 2 weeks)
  • Midterm exams (6-week and 12-week)
  • Final exam

Any student that completes every homework assignment to a satisfactory level will have their two lowest homework grades dropped at the end of the semester. The definition of “satisfactory level” is based on effort and is at the sole discretion of the instructor. Work submitted late may count for this requirement, even if it is late and gets zero credit.

Plus/minus grades will be assigned based on the following numerical cutoffs:

- +
A 90–92 93–100
B 80–82 83–86 87–89
C 70–72 73–76 77–79
D 60–66 67–69
F 0–59

Here is a breakdown of percentages by grading period.

6 weeks 12 weeks 16 weeks Final
Homeworks 20% 20% 20% 15%
Labs 40% 40% 40% 40%
Midterms 40% 40% 40% 20%
Final 25%

4 Collaboration

The guidance in the Honor Concept of the Brigade of Midshipmen and the Computer Science Department Honor Policy must be followed at all times. See https://www.usna.edu/CS/resources/honor.php. Specific instructions for this course:

  • Collaboration or assistance from any human other than the instructors, MGSP leaders, and those enrolled in SD212 this semester is not permitted. This includes any written or electronic materials from previous semesters.

  • Homework: Students may collaborate on homework with others in the same class, but must cite this collaboration clearly. Every student must actually complete their own assignment and understand anything they turn in.

  • Labs: Each lab presents a significant challenge and opportunity to develop and demonstrate mastery. The goal is not to simply complete the assignment and get the right answer, but to actually engage in the process to develop that answer through the data science skills we are learning in SD212.

    Unless otherwise specified, for labs:

    • Discussion of general strategies, tools, and tips is allowed (and encouraged) between current SD212 students. Examples: “How do I get pandas to read in dates correctly?” or “What web page did you use to figure out how to make that graph?”

    • Sharing specific solutions (such as source code) is not permitted. Example: “How did you do part 3?” or “Here is the for loop I used to convert the data”.

    • Looking at a fellow student’s code to help them debug it is allowed after you have already solved that part yourself. Example: Lucy has finished part 1 of the lab. Steve has tried to write a solution for part 1 but it’s giving an error or not working. Lucy looks at Steve’s code and offers some suggestions on how to fix it.

    • Looking at someone else’s code when you have not yet completed that part is not allowed. Example: Lucy has finished the lab and Steve is still working on part 1. Lucy lets Steve look at her code for part 2 to see how she solved it.

    • When in doubt, ask your instructor. We are all on the same team and trying to become better data scientists. Your instructor wants to help you succeed and is not trying to trap or trick you. We also know that you are just learning and struggling and don’t expect you do do everything perfectly the first time.

  • Exams: No collaboration is allowed. Any group study guides should be shared with the instructor.

All collaboration and outside sources should always be cited. The same rules apply for giving and receiving assistance. If you are unsure whether a certain kind of assistance or collaboration is permitted, you should assume it is not, work individually, and seek clarification from your instructor.

5 Use of Generative AI

The use of generative AI tools such as ChatGPT to help complete assignments is treated the same as collaboration or assistance with a human (see above) and is therefore prohibited under most circumstances. Please talk with your instructor if you believe there are ways to use generative AI tools without hindering the course learning objectives.

6 Absences

Students are responsible for all class material. Notes will be posted for each lecture, along with recommended readings. However, this material is not exhaustive and students missing class should arrange to copy notes from a classmate.

7 Late Policy

Homework solutions will generally be discussed immediately, and so no late submissions of homeworks will be accepted for credit. The same deadline applies even in the case of excused absences; students who will miss class should ensure that their work is still submitted on time (typically, electronically).

Labs: Each student has up to 3 grace days they may use at their discretion at any point during the semester for lab deadlines (either milestone or final deadline). An email to the instructor must be sent before the deadline in order to use a grace day. Each grace day extends 1 deadline for 1 student by 24 hours.

8 Classroom Conduct

Everyone in the classroom will show appropriate respect to each other at all times.

This class relies on active engagement and frequent interaction. Use of electronic devices during class time outside of note-taking apps is not permitted.

The section leader is responsible for recording attendance, bringing the class to attention, notifying the CS department office if the instructor is more than 5 minutes late, and directing the class in useful work in the instructor’s absence.

Drinks are permitted, but they must be in closable containers. Food, alcohol, and tobacco (of all kinds) are prohibited. Electronic devices must be silent during class and should never serve as a distraction to other students.

9 Textbooks

10 Course Website

Official website: https://usna.edu/Users/cs/SD212/

Mirrors:

11 Extra Instruction

Extra instruction (EI) is strongly encouraged and should be scheduled by email. (For Dr. Roche, first go here to check available times.) EI is not a substitute lecture; students should come prepared with specific questions or problems.

12 Course Description

This course builds on the programming skills developed in the prerequisite course and moves the focus towards a wider software ecosystem in order to solve more complex data science tasks. Students will learn and apply foundational principles of program organization including classes and objects, interfaces, inheritance, abstraction, and decoupling. In addition, important command-line skills will be developed for data gathering and cleaning, as well as library and software acquisition and use. These principles will be utilized through high-level programming in Python to analyze and manipulate real-world data sets.

13 Credits

3-2-4

14 Prerequisites

SD211 Intro to Data Science and Programming

15 Learning Objectives

  1. Observe the structure of new datasets and perform basic data cleaning and manipulation using command-line tools. (supports outcome 2)

  2. Understand how regular expressions can be used to describe tokens and their use in programs to manipulate plain-text inputs. (supports outcome 1)

  3. Write programs to analyze real datasets using popular data science libraries. (supports outcome DS-6)

  4. Utilize basic object-oriented principles such as inheritance and operator overloading to develop and structure complex programs. (supports outcome 1)

  5. Understand how libraries are packaged, distributed, downloaded, and installed using standard tools.

  6. Examine how data science has been and can be used to impact society at large. (supports outcome 4)

16 Student Outcomes

Graduates of the program will have an ability to:

1. Analysis.
Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
2. Implementation.
Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3. Communication.
Communicate effectively in a variety of professional contexts.
4. Ethics.
Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5. Teamwork.
Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
DS-6. Data.
Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs

17 Syllabus

  • Unit 1: Welcome back (Classes 1–3)
    Course overview, Data science pipeline, Python review
  • Unit 2: Command line (Classes 4–7)
    Files and directories, bash commands, Piping and redirection
  • Unit 3: Regular expressions (Classes 8–11)
    Regex syntax, Python re, Command-line tools
  • Unit 4: Error handling (Classes 12–14)
    try/except, return codes in bash
  • Unit 5: Versions and packaging (Classes 15–16)
    git, pip, mamba
  • Unit 6: Data cleaning (Classes 17–19)
    Missing data, Outliers, Preprocessing
  • Unit 7: Hardware and OS (Classes 20–22)
    CPU, Memory hierarchy, Filesystems, Role of the operating system
  • Unit 8: Concurrency (Classes 23–26)
    Multithreading, Python GIL, Multiprocessing, pickle, shell job control
  • Unit 9: Data Ethics (Classes 27–28)
    Principles, Case studies
  • Unit 10: OOP in Python (Classes 29–32)
    Operator overloading, Inheritance, Naming conventions, Generators
  • Unit 11: Typing (Classes 33–34)
    Type hints, Linters, Static vs run-time checks
  • Unit 12: Machine learning with sklearn (Classes 35–38)
    Statistical data types, Reading documentation, Classification, Regression

18 Updates to the course policy

In case this course policy needs to be changed during the semester, students will be notified by email and verbally during class. The current version will always be posted on the course website.