Instructor: Manu Office: Starcher Hall
Rm 209
Email: manu.manu@und.edu Phone:
777-4671
Class times: Tu,Th 12:30–1:45 PM
Location: Starcher 121
Review session/office hours: Monday 9–10 AM
and Thursday 1–2 PM
Textbooks:
R for Data Science, 2nd edition, by Wickham et al., ISBN-13: 9781492097402, O’Reilly Media, Inc. Electronic version is available for free at https://r4ds.hadley.nz.
ggplot2: Elegant Graphics for Data Analysis, 3 edition, by Wickam et al.. Available for free at https://ggplot2-book.org.
Advanced R, 2nd edition, by Hadley Wickham, ISBN-13: 978-0815384571, Chapman & Hall/CRC. Electronic version is available for free at https://adv-r.hadley.nz.
Required equipment:
Course overview:
What is Biological Data Science? Modern biology and medicine are increasingly data driven. This means that discoveries in diverse fields from biomedicine to ecology are made by analyzing quantitative datasets, which are often of massive size. The practice of medicine is also increasingly based on the analysis of large datasets. The influence of data analysis in medicine is expected to move from recommendations that researchers make to personalized medicine—physicians consulting large datasets and patients’ genomic information to make day-to-day diagnostic and therapeutic decisions.
Who should take this course? Anyone curious about how to understand data, extract meaningful insights from data by manipulating and visualizing it, and how to best present those insights. Biol 302/502 is an essential advanced requirement for the Molecular and Integrative Biology major and an optional advanced requirement for the Pre-Health Sciences emphasis. The course is also suitable for students seeking a major or minor in Biology in conjunction with a major in Computer Science, Mathematics, Physics, or Scientific Computing.
Do I need to know programming and/or R to take this course? No! All the essential computer skills for biological data science will be taught during the semester.
Course details
What will be covered? Data visualization—how to make informative, easy to understand, and attractive graphs and plots. Data transformation—how to ingest data, clean them up, and compute informative quantities. Exploratory data analysis—how to understand relationships between biological variables by visualization and other methods. The R computing environment—how to use R and the Rstudio IDE effectively as well as git version control. Automation—how to automate data analysis tasks using elementary programming techniques. All the methods will be taught using hands-on analysis of real biological datasets.
Learning goals:
By the end of the semester, you should be able to:
Visualize datasets with suitable and attractive graphs and charts using R.
Import datasets into R and manipulate them for analysis.
Perform exploratory data analysis to understand relationships between biological variables at a qualitative level.
Automate data analysis workflows using elementary R programming
Use RStudio and git version control with ease.
Assigned reading, assigned videos, and study guides: In order to aid the students’ mastery of the course material, the instructor will post study guides and video lectures for each module of the course. The study guides list what to read, which lecture videos to watch, and include some primary material to be read. The study guides also include activities that the students will be performing in and outside of class. The students are expected to 1) read and comprehend the reading material, 2) watch the video lectures, and 3) carry out some activities and fill out the study guide before class, and 4) submit the study guides on Blackboard before the posted deadlines. Students must bring their computers to class as in-class work would involve completing the activities on the study guide and discussing them. The submitted study guides will be graded and have 20% weight in the final grade (see Grading policy).
Study guides and group-work policy: Students will be assigned to groups. The students are encouraged to work with the members of their group to complete the readings, videos, and study guides together. However, each student will submit their study guide individually and will be graded on their own work. Blackboard discussion forums and Collaborate Ultra will be enabled for the convenience of the groups.
Homework assignments: Homework will assess students’ comprehension of and ability to apply concepts from the 8 course modules and provide additional practice. The homework assignments will consist of multiple-choice and short-answer questions as well as R activities. Students will be required to complete the homework assignments individually by the posted deadline. It is important for you to work through these problems on your own or with classmates to fully understand concepts and questions that will be on the exams.
Exams: There will be 8 mini exams that test students’ mastery of the material in each module. With the exception of the first fortnight of classes, which will not have an exam, the exam will be held on the 1st Tuesday of each module (see Tentative class schedule). The mini exam in each module will test students on the material from the previous module. The final exam will not be cumulative and will test students on material from module 8.
Final exam: TBD, Starcher 121.
Graduate credit: Students taking Biol 502 for graduate credit must carry out a project involving the analysis of a dataset from their own research or a dataset assigned by the instructor. This additional project must be discussed with the instructor during the first two weeks of the semester.
Grading Policy: Final grades will be calculated from the total score on study guides, homework assignments, and the mini exams. The two lowest scoring study guides will be dropped. The lowest scoring homework assignment and mini exam will be dropped. Grades for all exams, study guides, and homeworks will be posted on Blackboard. The total score will be determined according to the following weights:
| Assessment | Percent of final grade (Biol 302) | Percent of final grade (Biol 502) |
|---|---|---|
| Mini exams (best 7 out of 8) | 50% | 50% |
| Study guides (best 14 out of 16) | 30% | 20% |
| Homeworks (best 7 out of 8) | 20% | 10% |
| Project | 20% | |
| Total | 100% | 100% |
Final grades will be assigned on the following scale:
A: 90–100% B: 80–89.99% C: 70–79.99% D: 60–69.99% F: <60%
The instructor reserves the right to alter the grading scale to compensate for unforeseen discrepancies. Changes could raise your grade, but IT WILL NOT LOWER IT.
Students with disabilities: If a student has a disability that qualifies under the Americans with Disabilities Act and requests an accommodation, he/she should contact the instructor and Disability Support Services (190 McCannel Hall, 777-3425).
Late assignment and missed test policy: In the event that the student is unable to write a mini exam as scheduled because of a university-sponsored activity, illness or other emergency, the instructor must be notified prior to the exam, which must then be rescheduled within the next five school days. Make-up exams will be allowed only for university-approved absences and are entirely at the instructor’s discretion. The format of the make-up exam is also at the discretion of the instructor. Make-up assessments will not be allowed for study guides or homework assignments. However, the occasional absence will not affect the grade since the lowest two of study guide and homework assignment scores will be dropped.
Cheating, plagiarism, and collusion: The highest level of professional and academic integrity will be expected. It is expected that students present for evaluation their own work that represents their own thinking or study in an area. Cheating, plagiarism, and any other acts of dishonesty will be dealt with in accordance with UND policy as described in the Code of Student Life. Scholastic dishonesty may result in failure of the course. When reference material (work which includes unique ideas, findings, etc.) of others is used, students should be careful to give appropriate credit to the author. Failure to properly credit the work of others (i.e., inaccurate referencing, copying large amounts of material) may result in a grade of zero for the assignment and a charge of academic misconduct (See University of North Dakota, Code of Student Life).
Specific examples provided in the Code of Student Life include the following:
Cheating on a test includes, but is not limited to:
Copying from another student’s test.
Possessing or using material during a test not authorized by the person giving the test.
Circumventing safeguards against cheating, such as lockdown browsers.
Attempting a proctored in-person test remotely without permission of the instrunctor.
Collaborating with or seeking aid from another student during a test.
Knowingly using, buying, selling, stealing, transporting, or soliciting in whole or in part the contents of an unadministered test.
Substituting for another student or permitting another student to substitute for oneself to take a test.
Bribing another person to obtain an unadministered test or information about an unadministered test.
Using generative AI or other similar tools to answer the questions on an exam.
Plagiarism means the appropriation, buying, receiving as a gift, or obtaining by any means another person’s work and the unacknowledged submission or incorporation of it in one’s own work.
Collusion means the unauthorized collaboration with another person in preparing any academic work offered for credit.
Notice of non-discrimination: As part of its commitment to providing an educational environment free from discrimination, UND complies with Title IX of the Education Amendments, which prohibits discrimination and harassment based upon sex in an institution’s education programs and activities. Title IX prohibits sexual harassment, including sexual violence, of students at UND-sponsored activities and programs whether occurring on-campus or off-campus. Title IX also protects third-parties, such as visiting student athletes, from sexual harassment or violence in UND’s programs and activities and protects employees from sexual harassment and discrimination. Prohibited harassment includes acts of verbal, nonverbal or physical aggression, intimidation or hostility based on sex, even if those acts do not involve conduct of a sexual nature; sex-based harassment by those of the same sex; and discriminatory sex stereotyping. UND will take prompt action to investigate and resolve reports of sexual harassment or sexual violence in accordance with Title IX. UND’s Title IX coordinator is Donna Smith, Director of Equal Employment Opportunity/Affirmative Action, 401 Twamley Hall, 264 Centennial Drive Stop 7097, Grand Forks, ND 58202-7097, 701-777-4171, donna.smith@und.edu. Retaliation against any person who initiates an inquiry or complaint or participates in the investigation of a complaint is prohibited. Such conduct will be further cause for disciplinary action.
Tentative Class Schedule
| Date | Module | Topics | Chapters | Mini exam | |||||
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Module 1 | RStudio, git, R projects, R notebooks and markdown, introduction to data visualization | Ch 1.2 | ||||||
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Module 2 | Basic computer architecture, variables in R | Ch 2 |
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Module 3 | Data types, data structures, data visualization continued | Ch 1.3–1.7, Ch 2 |
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Module 4 | Relational and Boolean operators, logical indexing and subsetting | Ch 12.3 |
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Module 5 | Data transformation, row, column, and group verbs | Ch 3 |
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Module 6 | Functions | Ch 25 |
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Module 7 | Flow control | Ch 5 of Advanced R |
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| 28-Nov | No class - Thanksgiving break | ||||||||
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Module 8 | Exploratory data analysis |
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Final Exam | Starcher 121, 1–3 PM |
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