605.631.81 - Statistical Methods for Computer Science

Computer Science
Spring 2024


Statistical methods are the foundation for data science, artificial intelligence, and much of the field of computer science. Topics include probability, random variables, regression, gradient search, Bayesian methods, graphical methods, and exponential random graph models. Student will have the foundation to excel in future courses in machine learning, data science, algorithms, and more. Practice exercises will develop proficiency in the R programming language.


Profile photo of Tony Johnson.

Tony Johnson


Profile photo of Ian McCulloh.

Ian McCulloh


Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

By the end of this course, you will be able to:
• Summarize the principles of counting and combinatorial analysis.
• Explain the axioms of probability.
• Calculate conditional probabilities for multivariate problems.
• Construct probability trees for statistical reasoning.
• Compare and contrast various families of discrete and continuous random variable.
• Explain relationship between distribution and density functions.
• Apply linearity of expectation to calculate expected value.
• Evaluate the appropriateness of conclusions drawn from problems involving statistical paradoxes.
• Simulate random variables.
• Calculate statistics for regression analysis and anomaly detection.
• Apply exponential random graph models to relational data.
• Construct probabilistic graphical models for complex systems.
• Demonstrate proficiency in R.

Course Goals

To equip students with the basic fundamentals of statistical reasoning, probability, random variables, and statistical estimation necessary to excel in the field of computer science. Apply these skills to common computer science challenges and develop statistical literacy to understand academic papers in the field.


Ross, S. (2010). A First Course in Probability, 8th Ed. Upper Saddle River, NJ: Pearson

Required Software

The course will use R, however, students will be provided with easy to follow tutorials. Students should install the free open source version of RStudio on their computer during the first week of class. This software can be located at: https://posit.co/products/open-source/rstudio/

This course will use Jupyter Notebooks which can be installed here: https://jupyter.org/  

You will also be required to sign up for a GitHub account at https://github.com/

Student Coursework Requirements

A grade of “A” indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week. A grade of “B” indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Final grades will be determined by the following weighting:

• 70% Assignments
• 30% Data Challenge Exam

Each week, you will apply lesson concepts to practice exercises from out textbook. You will complete a graded quiz on similar problems the following week after questions on lesson material have been addressed. Students will also be expected to develop and R Jupyter Notebook and add to it each week with relevant functions and visualizations. The final few modules will involve a capstone data challenge where you will demonstrate your skills acquired throughout the course.

Assignments (70% of Final Grade Calculation)

Each week instructors will demonstrate select lesson concepts in the R programming language using a pre-selected dataset. Students will be required to create their own similar R Jupyter Notebooks using a different dataset. Notebooks that are equivalent in function and complexity to those demonstrated will receive a B, while those that exceed the demonstrated function and complexity will receive an A.
Notebooks that do not work will receive an F. There are no C’s and D’s.

Final Data Challenge (30% of Final Grade Calculation)

The final data challenge exam will involve students working on customized problems applying course concepts to solve a real-world problem requiring statistical methods. Students will work independently to solve challenging problems documenting their process and results. Then summarize findings and recommendations with classmates in a discussion forum in Module 14.

Grading Policy

Assignments are due according to the dates posted in the Canvas course site. Students may check these due dates in the Course Calendar or the Assignments in the corresponding modules. Grades will generally post no later than one week after assignment due dates. A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week. A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

The following grades are used for this course: A+, A, A– (excellent), B+, B, B– (good), C (unsatisfactory), F (failure), I (incomplete). A grade of F indicates the student’s failure to complete or comprehend the course work.

A course for which an unsatisfactory grade (C or F) has been received may be retaken. The original grade is replaced with an R. If the failed course includes laboratory, both the lecture and laboratory work must be retaken unless the instructor indicates otherwise. A grade of W is issued to those who have dropped the course after the refund period but before the drop deadline. The transcript is part of the student’s permanent record at the university. No grade may be changed except to correct an error, to replace an incomplete with a grade, or to replace a grade with an R.

The Whiting School assumes that students possess acceptable written command of the English language. It is proper for faculty to consider writing quality when assigning grades.

For incomplete grades, please see the Graduate Programs catalogue for the Whiting School of Engineering.

The course grading scale uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range Letter Grade
100-98 = A+
97-94 = A
93-90 = A−
89-87 = B+
86-83 = B
82-80 = B−
79-77 = C+
76-73 = C
72-70 = C−
69-67 = D+
66-63 = D
<63 = F

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.