Computational statistics is a branch of mathematical sciences concerned with efficient methods for obtaining numerical solutions to statistically formulated problems. This course will introduce students to a variety of computationally intensive statistical techniques and the role of computation as a tool of discovery. Topics include numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.], random number generation, Monte Carlo methods, randomization methods, jackknife methods, bootstrap methods, tools for identification of structure in data, estimation of functions (orthogonal polynomials, splines, etc.), and graphical methods. Additional topics may vary. Coursework will include computer assignments.
Prerequisites: Multivariate calculus, familiarity with basic matrix algebra, graduate course in probability and statistics (such as 625.603 or an equivalent graduate course).
Instructor Contact information:
The course materials are divided into modules which can be accessed by clicking Course 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. All modules run for a period of seven (7) days. You should regularly check the Calendar and Announcements for assignment due dates.
To provide a background in the computationally intensive tools and methodologies relevant to statistical analysis and the visualization of complex data.
You will need to install R on your computer (free!), by going to the R Project website (http://www.rproject.org/) and following the instructions provided. There are also instructions provided in Module 1.
It is expected that each module will take approximately 6–10 hours per week to complete. Here is an approximate breakdown: listening to the audio annotated slide presentations (approximately 2–3 hours per week), reading the assigned sections of the texts and other readings (approximately 1-2 hours per week), and problem sets (approximately 3–5 hours per week).
Your final grade will be broken down as follows:
Assignments are due according to the dates posted in your Canvas course site. All assignments are released and due on Eastern Time. Each assignment, unless otherwise noted in the course module, should be submitted electronically via the assignment submission link within the module in which it is due. A comprehensive list of assignments and due dates are provided in the Course Outline. You may also check these due dates in the Calendar or the Assignments in the corresponding modules. I will post grades about a week after assignment due dates.
We generally do not directly grade spelling, grammar, or handwriting. However, egregious violations of the rules of the English language will be noted without comment. Also, if I can't read it, I can’t grade it. So please keep your work neat.
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.
A grade of C or F indicates that the student has failed to show a graduate level of understanding of the course material.
The following should be used as a general guideline on assignments to help determine your progress in the course:
100–98 = A+
97–94 = A
93–90 = A−
89–87 = B+
86–83 = B
82–80 = B−
79–70 = C
Final grades will be determined by the following weighting:
% of Grade
Module Problem Sets
Course Project (Proposal and Presentation)
15% (5% + 10%)
Exams (Midterm + Final)
40% (20% + 20%)
While the lecture videos, readings, and other provided materials contain all the information you need to solve any assigned problems, you are allowed to consult other references (e.g., other textbooks) to strengthen your understanding of general concepts if you wish (except during exam weeks). If you accidentally find a solution to an assigned problem in such a reference, you MUST NOT read it and should work out the solution on your own. Using solutions manuals (to any text) or otherwise searching for answers to problems (including accessing websites like Chegg, Course Hero, Stack Exchange, etc.) is never permitted. Not only does this violate the Academic Integrity policy, it hurts your own learning and understanding. If you need assistance, I am more than happy to provide it!Academic Integrity Course
You should have been enrolled in an academic integrity training course shortly after registering for your first class at Johns Hopkins Engineering for Professionals. This course covers the fundamental values of academic integrity, as well as information related to our academic misconduct policy and gives guidance on proper citation, and learn how to avoid mistakes like plagiarism and other violations of academic misconduct.
The academic integrity training course can be accessed through Canvas and will take approximately 30 minutes to complete. This is a pass/fail course and the grade will be posted to your transcript. All students are expected to complete the academic integrity course within their first term. For more information on our academic misconduct policy, please visit: http://ep.jhu.edu/faculty/prepare-to-teach/academic-misconduct.Plagiarism
Plagiarism is defined as taking the words, ideas or thoughts of another and representing them as one's own. If you use the ideas of another, provide a complete citation in the source work and present the words in the correct quotation notation (indentation or enclosed in quotation marks, as appropriate) and include a complete citation to the source. See the course text for examples.
I take academic integrity very seriously. Copying from any source is considered to be cheating as is searching the internet for solutions to the problems. The use of Chegg resources in this course will be considered cheating. If you are caught, you will be reported to the EP Academic Integrity Officer for Academic Misconduct.
While discussion of the homework with your classmates is allowed, the assignments are intended to be done individually or in groups as outlined in the Problem Set Guidelines. Additionally, you should indicate on your assignment anyone with whom you have discussed the assignment by placing their name on the top of the first page in a clear visible location. Copying or sharing of written work and/or computer code outside of your homework group is considered to be cheating as is searching the internet for solutions to the problems. The use of Chegg is strictly prohibited. These activities will result in a grade of zero on the assignment and possible an F in the course. Discussion of exams or use of sources outside of those listed on the exam instructions is strictly prohibited and will result in an F in the course. Contact me if you have any questions, no matter how slight, about this policy, or if you have questions about a particular assignment.
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 email@example.com.
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, firstname.lastname@example.org.
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/
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).
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.