Introduction to Data Science: CptS 483-06 -- Syllabus

Links:   Syllabus in PDF   Schedule and Lecture Material  

Course information

Credit hours: 3
Semester: Fall 2015
Meeting times and location: MWF 12:10–13:00, Sloan 163
Course website:
Relevant course material, including this syllabus, and course related resources will be made available at the course website. Additionally, the online portal OSBLE ( will be used for posting lecture material, assignments, announcements, etc and for handling submissions.

Instructor information

Assefaw Gebremedhin
Office: EME 59
Email: assefaw AT eecs DOT wsu DOT edu

Office hours

Tentative office hours: Tuesdays 2:00-3:00pm, or by appointment.

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 mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions. This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used.

Learning outcomes

At the conclusion of the course, students should be able to:


The course is suitable for upper-level undergraduate (or graduate) students in computer science, computer engineering, electrical engineering, applied mathematics, business, computational sciences, and related analytic fields.


Students are expected to have basic knowledge of algorithms and reasonable programming experience (equivalent to completing a data structures course such as CptS 223), and some familiarity with basic linear algebra (e.g. solution of linear systems and eigenvalue/vector computation) and basic probability and statistics. If you are interested in taking the course, but are not sure if you have the right background, talk to the instructor. You may still be allowed to take the course if you are willing to put in the extra effort to fill in any gaps.

Course work

The course consists of lectures (three times a week, 50 min each), and involves a set of assignments (about 3 or 4) and a project. A project could take one of several forms: analyzing an interesting dataset using existing methods and software tools; building your own data product; or creating a visualization of a complex dataset. Students are encouraged to work in teams of two or three for a project. Assignments, on the other hand, are to be completed and submitted individually. Besides the assignments and a project, there will be frequent opportunities for in-class exercises and ``thought experiments".


Your final grade will be determined based on your performance on each of the following items; the percentages in parenthesis show the weight each item carries to the final grade. Letter grades: 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%). Grading scale may be adjusted depending on class average.

Topics and Course Outline

  1. Introduction: What is Data Science?
  2. Statistical Inference
  3. Exploratory Data Analysis and the Data Science Process
  4. Three Basic Machine Learning Algorithms
  5. One More Machine Learning Algorithm and Usage in Applications
  6. Feature Generation and Feature Selection (Extracting Meaning From Data)
  7. Recommendation Systems: Building a User-Facing Data Product
  8. Mining Social-Network Graphs
  9. Data Visualization
  10. Data Science and Ethical Issues


The following book will be used as a textbook and primary resource to guide the discussions, but will be heavily supplemented with lecture notes and reading assignments from other sources. The lecture notes and reading material will be posted on the course's website or the associated OSBLE page as the course proceeds.

Additional references and books related to the course:


Missing or late work

Submissions will be handled via the OSBLE page of the course. Students are expected to submit assignments by the specified 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 more than 48 hours will be counted as a zero.

Academic Integrity

Academic integrity will be strongly enforced in this course. Any student who violates the University's standard of conduct relating to academic integrity will receice an F as a final grade in this course, will not have the option to withdraw from the course and will be reported to the Office of Student Standards and Accountability. Cheating is defined in the Standards for Student Conduct WAC 504-26-010 (3). You can learn more about Academic Integrity on the WSU campus at Please also read this link carefully: EECS Academic Integrity Policy. Use these resources to ensure that you do not inadvertently violate WSU's standard of conduct.

Safety on Campus

Washington State University is committed to enhancing the safety of the students, faculty, staff, and visitors. It is highly recommended that you review the Campus Safety Plan ( and visit the Office of Emergency Management web site ( for a comprehensive listing of university policies, procedures, statistics, and information related to campus safety, emergency management, and the health and welfare of the campus community.

Students with Disabilities

Reasonable accommodations are available for students with a documented disability. If you have a disability and need accommodations to fully participate in this class, please either visit or call the Access Center (Washington Building 217; 509-335-3417) to schedule an appointment with an Access Advisor. All accommodations MUST be approved through the Access Center. For more information, consult the webpage or email at

Important 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

Weather Policy

For emergency weather closure policy, consult:


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