Course format: The course will be focused on the introduction of principles, state-of-the-art analytics solutions, and challenges in high throughput genomic data analysis. The format of the course will include lectures by the instructor, computing labs, class discussion, directed reading, and projects. The exact format will depend on the size of enrolment and student background. We emphasize practical analytical skills for genomic data analysis.
Prerequisite: BNFO601 Foundations of Bioinformatics or BNFO615 Data Analysis in Bioinformatics. Talk to the instructor if you don’t take any of them.
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.
(Optional) Textbooks:
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:
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.