Course format: The course will be focused on the introduction of popular computer algorithms and statistical models for important problems in computational biology. The format of the course will include lectures by the instructor, computing labs, class discussion, directed reading, and student presentation or project. The exact format will depend on the size of enrolment and student background. We emphasize statistical models, algorithms, and data structures instead of biological implications and applications, although some relevant biological background and motivations will be discussed.

Prerequisite: No background in biology is required but is a plus. We mainly use R (http://www.r-project.org/) for computational analysis. R programming will be taught in this course.

Attendance: You are supposed to attend all the classes. Participation is highly encouraged to make the class more interactive. Class attendance and participation are taken into consideration by the instructor for the evaluation of the students. In general, students who attend class regularly perform much better than those who come only occasionally. If you miss one class be sure to consult one of your classmates about the content of the lecture and visit the course web page to get notes, exercises, assignments, deadlines and announcements.

Textbooks:

• “Using R for Introductory Statistics”, by John Verzani, Chapman & Hall/CRC, 2004, ISBN 1584884509
• “Statistical Methods in Bioinformatics: An Introduction” by Warren J. Ewens and Gregory Grant , the Springer Press, 2nd edition (September 30, 2005) ISBN-10: 0387400826 

Grading
The requirements of this course will consist of participating in lectures, homework, in class computing lab assignments, a midterm and a project. The grading breakdown is the following:

• Homework & computing lab exercise (10%)
• Quiz (25%)
• Term Project/Paper (10%)
• Midterm (25%)
• Final (30%)

Tentative grading scale A: 90% or above; B+: 85% or above; B: 75% or above; C+: 66 % or above; C: 60% or above.
 

Late assignment policy
You have 72 hours in total grace period for late assignments. The number of late assignments should not exceed 3.

Collaboration and Honor Code Students may discuss problems together but must write up their own solutions. When writing up the solutions, students should write the names of people, if any, with whom they discussed the assignment. Note in particular that copying homework or programming assignments, in full or in part is forbidden. Students found cheating or plagiarizing will be immediately referred to the Dean of Students and the NJIT Committee on Professional Conduct and subject to Disciplinary Probation, a permanent marking on the record, possible dismissal, and an "F" grade in the course. All submitted assignments will be checked for similarities, and plagiarism and guilty students identified.