CptS 475: Data Science
Syllabus, Fall 2024
Course Information
Credit Hours: 3Semester: Fall 2024
Meeting Times: MWF, 9:10–10:00 am
Meeting Location: Bryan Hall 305
Learning Management System: Canvas will be used for the management of this course, including for posting of lecture materials, assignments, announcements, and messages. It will also beused for handling student submissions and instructor and Teaching Assistant feedbacks.

Instructor Information
Instructor: Assefaw Gebremedhin
Office: EME B43
Email: assefaw DOT gebremedhin AT wsu DOT edu
Webpage: https://eecs.wsu.edu/~assefaw/
Instructor Office Hours: Wednesdays 12–1:30 pm
Office hours will be conducted in-person, but I will also make a zoom option available in case youcan’t make it in person. Zoom meeting information is posted on the Canvas site of the course.
Teaching Assistant: Funso Oje
Email: olufunso DOT oje AT wsu DOT edu
Office: Dana 115
TA Office Hours: Tuesdays and Thursdays 1–2:30 pm
TA Office hours will be conducted in person in Dana 115, but a zoom option is also made available
in case you can’t make it in person. Zoom meeting information is posted on the Canvas site of thecourse.

Course Description
Data Science is the study of the generalizable extraction of knowledge from data. Being a data
scientist requires an integrated skill set spanning computer science, mathematics, statistics, anddomain expertise along with a good understanding of the art of problem formulation to engineer
effective solutions. The purpose of this course is to introduce students to this rapidly growing fieldand equip them with some of its fundamental principles and tools as well as its general mindset.
The course will use the programming languages R (primarily) and Python.
Topics to be covered include: the data science process, exploratory data analysis, data wrangling, data visualization, linear regression, classification, clustering, principal component analysis,time-series data mining, deep learning, and data and ethics.
The focus in the treatment of these topics is on breadth, rather than depth, and emphasis is placedon integration and synthesis of concepts and their application to solving problems. Necessary
theoretical abstractions (mathematical and algorithmic) are introduced as and when needed.

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Audience
The course is suitable for upper-level under-graduate students in computer science, engineering,applied mathematics, the sciences, business, and related analytic fields. The course is offeredconjoint with a 500-level graduate course (CptS 575).

Prerequisites
Students are expected to: (i) have taken an introductory course in statistics and probability, (ii)have fundamental knowledge of algorithms and good programming experience (equivalent to completing a data structures course such as CptS 223), and (iii) have fundamental knowledge of linear
algebra (e.g. eigenvalue/vector computation).

Coursework
The course consists of several elements: lectures (three times a week, 50 min each); a set ofassignments; a substantial semester project; one mid-term exam and no final exam. Below is how
the coursework and assessment is broken down.
• Assignments (35%). There will be a total of 5 assignments spread through the semester.
Each assignment will have one major topic of emphasis. Assignments are to be completed andsubmitted individually. Each assignment will carry equal weight. Together all assignments
account for 35% of final grade.
• Semester Project (40%). Students, working individually or in a team of two, will complete
a semester project. A project could take one of several forms: analyzing an interesting dataset
using existing methods and software tools; developing a new method; building your own dataproduct; or creating a visualization of a complex dataset.
Students will be given an opportunity to choose from a list of projects the instructor provides
or propose their own project. Guidelines for what constitutes a project will be provided bythe instructor. A project will culminate in a written report and a short presentation in class.
General guidelines for how to prepare a report will be provided by the instructor. Students
are expected to follow the guidelines. Similarly, guidelines for how to prepare and deliver
good presentations will be provided by the instructor, and students are expected to follow
the guidelines.
• Exam (23%). There will be one mid-term exam designed to complement the assignments
and the semester project. The exam is tentatively scheduled to take place in the week of Nov.
4. Final date will be decided later after consulting with the class.
• Class Participation (2%). Active class participation—in discussions during lectures, surveys, and other online discussions, including responding to Participation Question of The
Day—is required. Class Participation will count towards 2% of the final grade.

Expectations for Student Effort
For each hour of lecture equivalent, students should expect to have a minimum of two hours ofwork outside class.

2GradingLetter grades will be given according to the following ranges:
A (93–100%), A- (90–92.99%), B+ (87–89.99%), B (83–86.99%), B- (80–82.99%), C+ (77–79.99%),
C (70–76.99%), C- (67–69.99%), D (60–66.99%), F (less than 60%).

Learning Outcomes and Assessment
Student Learning Outcomes.

Course Topics/Dates.

By the end of the course, students
should be able to:

The following topics/dates willaddress this outcome:• Describe what Data Science is andthe skill sets needed

What is Data Science? (week 1);• Describe the Data Science Process• Use R (or Python) to carry out
statistical modeling and analysis• Carry out effective exploratory
data analysis• Use effective data wrangling approaches to manipulate data
• Create effective visualization ofdata (to communicate or persuade)• Apply machine learning algorithms for predictive modeling• Apply effective methods to assess
model performance• Apply learning methods to discover patterns, trends and anomalies in data
• Reason around ethical and privacy
issues in data science conduct, andapply ethical practices• Work effectively in teams on data
science projects• Apply knowledge gained in thecourse to carry out a project andwrite a technical report

EDA and the Data Science Process (week 3 )Intro to R (week 2); Most subsequent topics throughout thesemester
EDA (week 3)
Data Wrangling (week 4, week5)
Data Visualization (week 5,week 6)
Linear Regression (week 7);
Classification (weeks 8 and 9);
Deep Learning (week 13, week15)

Evaluation.
This outcome will
be evaluated primarily by:
Assignments;
Exam
Assignments;
Exam; ProjectAssignments;
ProjectAssignments;
Project
Assignments;
ProjectAssignments;
ProjectAssignments;
Project; Exam

Cross-validation (week 10)

Exam; Project

Unsupervised Learning (week11); Time Series Data Mining(week 12)Assignments;
Project; Exam

Data and Ethics (week 15)

In-class exerciseProjectProject3

Detailed Topics and Course Outline
1. Introduction: What is Data Science?
- Big Data and Data Science; Landscape of perspectives; Skill sets needed
2. Intro to R
- R basics; R graphics; R Markdown
3. Exploratory Data Analysis and the Data Science Process- Basic tools of EDA; Philosophy of EDA; The Data Science Process
4. Data Wrangling- Data transformation and manipulation (dplyr); Relational data; Data “tidying” (tidyr)
5. Data Visualization- Principles of data visualization; Use of color and design considerations; Web mapping;data visualization and story-telling
6. Overview of Machine Learning- Supervised Learning (canonical examples and real world applications);
Unsupervised Learning (canonical examples and real world applications)
Reinforcement Learning
7. Linear Regression- Simple linear regression; Multiple linear regression; Extensions of the linear model
8. Classification- Overview of classification; Logistic regression; Linear Discriminate Analysis; Naive Bayesclassifier; K-Nearest Neighbors (KNN); Decision Trees and Random Forest
9. Resampling Methods- Cross-validation; The Bootstrap
10. Unsupervised Learning- Principal Component Analysis (PCA); K-means clustering; Hierarchical clustering
11. Time Series Data Mining Overview- Examples of areas where time series data arise; Distance measures; Algorithms (motifdiscovery, anomaly detection, segmentation, classification, clustering).
12. Intro to Deep Learning- What is deep learning? The perceptron; Activation functions; Building neural networks;
Training neural networks; Regularization; Software packages for DL; Convolutional neural networks
13. Data Science and Ethical Issues- Discussions on privacy, security, ethics; A look back at Data Science4

Weekly Schedule
See Table 1 for a weekly schedule of topics and assignments.
Week
01 (Aug 19)
02 (Aug 26)
03 (Sep 02)
04 (Sep 09)
05 (Sep 16)
06 (Sep 23)
07 (Sep 30)
08 (Oct 07)
09 (Oct 14)
10 (Oct 21)
11 (Oct 28)
12 (Nov 04)
13 (Nov 11)
14 (Nov 18)
15 (Nov 25)
16 (Dec 02)
17 (Dec 09)

TopicsWhat is Data Science?
Intro to R/Python
Exploratory Data Analysis
Data Wrangling I
Data Wrangling II, Data Visualization I
Data Visualization II
Semester project set-up, Overview of ML
Linear Regression
Classification I
Classification II, Resampling methods
Unsupervised Learning
Time Series Data Mining
Deep Learning (DL)DL II, Ethics, Course wrap-upThanksgiving break
Project presentations

Assignments/Notes
Assign. 1 out
Assign. 1 due, Assign. 2 out
No class 9/2; Assign. 2 due
Assign. 3 out
Assign. 3 due
Assign. 4 out
Assign. 4 due, Project proposal out
Project proposal due, Assign. 5 out
Assign. 5 due
Project progress report due
Mid-term exam
No class 11/11 (Veterans Day)
In-class exercise

Final project report due on Dec 10

Table 1: Tentative week-by-week schedule of topics and assignments. The date shown in parenthesis
is just the Monday of that week.

Books
There is no required “textbook” for this course. Select chapters from the followings references will
be used as starting points for discussions, and they will be supplemented with instructor-developed
lecture notes and reading assignments from other sources.
• Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction toStatistical Learning. The book comes in two versions, one version in R (ISLR) and the otherin Python (ISLP). The second edition of ISLR was published in 2021 and ISLP was publishedin 2023. The book is freely available online at: https://www.statlearning.com/.
• Hadley Wickham, Mine Centinkaya-Rundel and Garett Grolemund. R for Data Science,
Second Edition. Freely available on-line at: https://r4ds.hadley.nz/• Kevin P. Murphy. Probablistic Machine Learning: An Introduction. MIT Press, March 2022.
Various info including draft PDF files of chapters available at:https://probml.github.io/pml-book/book1.html.
• Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets. Third
Edition, Cambridge University Press. 2021.
The book is freely available online at: http://www.mmds.org/.
• Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. ISBN
9780262035613. The book is freely available online at: http://www.deeplearningbook.org
5Policies
Conduct
Students are expected to maintain a professional and respectful classroom environment. In particular, this includes:• silencing personal electronics (non-disruptive devices may be used during class)• arriving on time and remaining throughout the class.
Use of Generative AI Tools and Technologies
The use of generative AI tools and technologies, such as ChatGPT, to create content is permitted,
but it must be fully disclosed as follows:• If the tool is used to generate code, the generated code should be first saved in some way(e.g. in a separate file or commited to a repository) before being adapted with the following
statement in the comments of the code: “Generative AI was utilized to generate this code”
followed by a description of the functionality that the autogenerated code is fulfilling.
• Next, the code can be adapted and saved again (or commited)—the comments of the codeshould be updated to provide a summary of the adaptation.
• If the tool is used to generate text, at the beginning of the submitted document, include astatement that says “Generative AI was utilized to generate a draft of this text”, and thenindicate in what way you edited the draft to produce the submitted text.
If you find yourself in a situation that is not listed above and are worried about whether or
not it might be considered cheating or if you are uncertain about the need to disclose the use ofa particular tool, err on the side of caution and email me (via Canvas). I’ll give you a definitive
answer.
Correspondence
All class related correspondence with the instructor will be made via Canvas.
Attendance
Regular attendance is expected. While students may miss class for urgent reasons, excessive absences that are not cleared with the instructor will factor into the Class Participation portion of
the semester grade.
Missing or Late Work
Submissions will be handled via Canvas. Students are expected to submit assignments by thespecified due date and time. Assignments turned in up to 48 hours late will be accepted with a
10% grade penalty per 24 hours late. Except by prior arrangement, missing or work late by morethan 48 hours will be counted as a zero.

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Missed Exam
There will be only one exam in the course, tentatively scheduled to take place in the week of Nov
4. Final date of the exam will be decided at least two weeks prior to the exam date. Date will
be picked in consultation with the class to accomodate as much as possible students’ other examschedules and commitmments. Make-up exam is not allowed if the exam is missed unless there isan extra-ordinary circumstance.
Academic Integrity
Academic integrity is the cornerstone of higher education. You are responsible for reading WSU’s
Academic Integrity Policy, which is based on Washington State law. If you cheat in your work inthis class you will:• Fail the assignment or exam in which the cheating happened.
• Be reported to the Center for Community Standards.
• Have the right to appeal my decision.
• Not be able to drop the course or withdraw from the course until the appeals process isfinished.
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at theCenter for Community Standards website. You must submit this request within 21 calendar daysof the decision.

Lauren’s Promise:
WSU’s Commitment to Address Discrimination and Harassment
On October 22, 2018, Lauren McCluskey, 21 years old, was murdered by a man she briefly dated
on the University of Utah campus, where she was a student. Lauren was raised in Pullman,Washington. Together with her parents, who are professors at WSU, this university community
stands firmly behind Lauren’s Promise: WSU will listen and facilitate support and reporting
options if someone is threatening you.
WSU prohibits discrimination and harassment. This includes discriminatory harassment, hatecrimes, sexual discrimination, sex-based harassment, stalking, dating violence, domestic violence,
sexual assault, and all types of sexual violence.
If you are in immediate danger, call 911.
If you have experienced or have witnessed discriminatory behavior, you can contact the WSU
Compliance and Civil Rights (CCR) and/or the WSU Title IX Coordinator. CCR can provide
information on reporting options, including confidential resources available to you, and facilitate
supportive measures. To contact CCR:
Online: Online Reporting Form
Email: ccr@wsu.edu
Phone: 509-335-8288
For more information, see the WSU Policy Prohibiting Discrimination and Harassment (Executive Policy 15), WSU Standards of Conduct for Students (Chapter 504-26 WAC), and the WSU
Notice of Nondiscrimination.

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Reasonable Accomodation
Students with disabilities or chronic medical or psychological conditions can request reasonableaccommodations. If you need reasonable accommodations to fully participate in your courses,
please go to your campus’ Access Center/Services website (see links below). Follow the proceduresto request accommodations. You may also contact your campus office to schedule an appointment
with an Access Advisor.
The Access Center/Services will notify your instructors of your requested accommodations, but
you may need to communicate with your instructors about how some of your accommodations will
work (by email, Zoom, or in person).
Contact an Access Advisor on your campus:• Pullman, WSU Global Campus, Everett, Bremerton, and Puyallup:509-335-3417, http://accesscenter.wsu.edu, or email access.center@wsu.edu• Spokane: 509-358-7816, https://spokane.wsu.edu/studentaffairs/access-resources/,or email spokane.access@wsu.edu• Tri-Cities: 509-372-7352, http://www.tricity.wsu.edu/disability/,or email tricities.AccessServices@wsu.edu• Vancouver: 360-546-9238,
https://studentaffairs.vancouver.wsu.edu/access-center,or email van.access.center@wsu.edu

Emergencies on Campus
To receive emergency alerts on your phone or by email, click on the link to the page of yourcampus that you can access via this university syllabus website: https://syllabus.wsu.edu/university-syllabus/. These alerts may include information about active shooter situations andsevere weather.
In case of an active shooter, follow these ideas: “Run, Hide, Fight”.
In any emergency, remain ALERT by observing and paying attention to WSU emergency alerts.
ASSESS your specific situation, and ACT to ensure your own safety and the safety of others if you
are able.

Other Policies in the University Syllabus
Students are responsible for reading and understanding all university-wide policies and resources
pertaining to all courses (for instance: accommodations, care resources, policies on discriminationor harassment), which can be found in the university syllabus website:
https://syllabus.wsu.edu/university-syllabus/.

Academic Dates and Deadlines
Students are encouraged to refer to the academic calendar often to be aware of critical deadlines
throughout the semester. The academic calendar can be found at
http://registrar.wsu.edu/academic-calendar.

ChangesThis syllabus is subject to change. Updates will be posted on the course website.
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