This course will be an introductory course to data science and is required for MPH students with concentration in Public Health Data Science. The goals of the course are to,
- achieve proficiency in the R programming language, with particular emphasis on the Tidyverse;
- gain familiarity with a suite of data science tools;
- master the practices of good data science; and,
- learn how to apply the methods and tools to public health datasets.
This course is required for MPH students with concentration in Public Health Data Science and is appropriate for master's students in quantitative fields. PhD students in computer science, business, psychology, social science, and science may also find this course useful. There are no pre-requisites beyond those required for entry into the MPH program.
Chuck Pepe-Ranney, PhD
Visiting Lecturer
Department of Biostatistics
3101A McGavran-Greenberg Hall
Phone: 575-313-0993
Email: chuckpr@ad.unc.edu
TBD
TBD
https://github.com/chuckpr/BIOS512
Tuesday and Thursday from 11am until 12:15pm.
This course will teach you the skills to build and communicate end-to-end data narratives.
We will specifically be focusing on the following techology:
The course format will include two lectures each week. The lectures will be supplemented with in-class exercises, case studies, and examples of data science applications in public health settings.
The instructor reserves the right to make changes to the syllabus, including topics, readings, assignments, and due dates. Any changes will be announced as early as possible. For session-by-session course schedule details, please see the course website.
This course will include the following graded assignments that contribute to your final grade in the course. For assignment descriptions and assignment grading rubrics, please see below.
| Assignments | Points/Percentages |
|---|---|
| (approximately) Weekly exercises | 75 |
| Final project | 20 |
| Participation | 5 |
Below you will see the competency you will develop in this course, the learning objectives that comprise the competency, and the assignment in which you will practice demonstrating this competency.
Select and use data visualization methods to interpret and communicate research results, with the overall objective of conducting reproducible research, both individually and in project teams.
- Achieve proficiency in the R programming language.
- Gain familiarity with a suite of data science tools, including data wrangling, data quality control, data cleaning/editing, exploratory data analysis, and data visualization.
- Understand the principles of good data science, particularly reproducible research.
- Become familiar with several software tools for best practices in data science including Git, Docker, Jupyter, and Make.
- Apply the data science tools and principles of best practice to real data problems in public health settings.
Will require use of R and data science tools in solving real data science problems. Projects will also require demonstration of the elements of reproducible research.
Will contain questions about the capabilities of different software tools and questions about the principles of good data science.
Final course grades will be determined using the following UNC Graduate School grading scale. The relative weight of each course component is shown in the Graded Assignments table.
| Grade | Description | Numeric Value |
|---|---|---|
| H | High Pass: Clear excellence | 93-100 |
| P | Pass: Entirely satisfactory graduate work | 80-92 |
| L | Low Pass: Inadequate graduate work | 0-79 |
| F | Fail | 0-69 |
Final course grades will be determined using the following UNC Undergraduate School grading system.
| Grade | Description | Numeric Value |
|---|---|---|
| A | Mastery of course content at the highest level of attainment that can reasonably be expected of students at a given stage of development. The A grade states clearly that the students have shown such outstanding promise in the aspect of the discipline under study that he/she may be strongly encouraged to continue. | 90-100 |
| B | Strong performance demonstrating a high level of attainment for a student at a given stage of development. The B grade states that the student has shown solid promise in the aspect of the discipline under study. | 80-89 |
| C | A totally acceptable performance demonstrating an adequate level of attainment for a student at a given stage of development. The C grade states that, while not yet showing unusual promise, the student may continue to study in the discipline with reasonable hope of intellectual development. | 70-79 |
| D | A marginal performance in the required exercises demonstrating a minimal passing level of attainment. A student has given no evidence of prospective growth in the discipline; an accumulation of D grades should be taken to mean that the student would be well advised not to continue in the academic field. | 60-69 |
| F | For whatever reason, an unacceptable performance. The F grade indicates that the student’s performance in the required exercises has revealed almost no understanding of the course content. A grade of F should warrant an advisor’s questioning whether the student may suitably register for further study in the discipline before remedial work is undertaken. | 50-59 |
The instructor will be an active reader and will occasionally post throughout the semester. The group discussion forum will be moderated by the group members unless an issue is brought to the instructor’s attention by a fellow group member. The course discussion forum will be hosted on Slack.
The preferred way to contact the instructor is on the course Slack channel. The instructor will typically respond to a message within 24 hours or less if sent Monday through Friday. The instructor may respond to weekend message, but it is not required of them. If you receive an out of office reply when emailing, it may take longer to receive a reply. The instructor will provide advance notice, if possible, when they will be out of the office.
All graded assignments will receive written feedback that coincides with the assessment rubric. Feedback is meant to be constructive and help the student continue to build upon their skills. The types of feedback you may receive are descriptive feedback, evaluative feedback, and motivational feedback. Feedback is a tool that you as a learner can use to understand the areas that you are succeeding in and what you can do to improve in other areas.
Assignments will be graded no more than four weeks after the due date. Assignments that build on the next assignment will be graded within one week of the final due date. Early submissions will not be graded before the final due date.
The instructor reserves the right to make changes to the syllabus, including topics, readings, assignments, and due dates. Any changes will be announced as early as possible. For session-by-session course schedule details, please see below.
The materials used in this class, including, but not limited to, syllabus, exams, quizzes, and assignments are copyright protected works. Any unauthorized copying of the class materials is a violation of federal law and may result in disciplinary actions being taken against the student. Additionally, the sharing of class materials without the specific, express approval of the instructor may be a violation of the University's Student Honor Code and an act of academic dishonesty, which could result in further disciplinary action. This includes, among other things, uploading class materials to websites for the purpose of sharing those materials with other current or future students.
Submit all assignments via GitHub or GitHub Classroom. Emailing assignments is not acceptable unless prior arrangements have been made.
Your attendance and active participation are an integral part of your learning experience in this course. If you are unavoidably absent, please notify the course instructor (and Teaching Assistant if one is assigned).
You are expected to follow common courtesy in all communication to include email, discussion forums, and face-to-face. All electronic communications sent should follow proper English grammar rules to include complete sentences. This is a professional course, and you are expected to communicate as a professional.
You are expected to offer individual contributions in class and on individual assignments, and collaborate with fellow students on assignments for which students may work together, such as group assignments.
To ensure effective functioning of the Honor System at UNC, students are expected to:
- Conduct all academic work within the letter and spirit of the Honor Code, which prohibits the giving or receiving of unauthorized aid in all academic processes.
- Learn the recognized techniques of proper attribution of sources used in written work; and to identify allowable resource materials or aids to be used during completion of any graded work.
- Students may use materials they wrote for other courses, but only if the student themselves produced the work.
- For homework, students may verbally discuss approaches to the problems but each student should independently write up the answer and verify solutions.
See Additional Resources and Policies for additional information.
In this class, we practice the Gillings School’s commitment to inclusion, diversity, and equity in the following ways. See Additional Resources and Policies for additional information.
- Treat all members of the Gillings community (students, faculty, and staff) as human persons of equal worth who deserve dignity and respect, even in moments of conflict and disagreement.
- Contribute to creating a welcoming and inclusive classroom environment, where all are able to learn and grow from one another.
- Acknowledge and respect the diversity of experiences that others bring to the classroom and the ways in which this richness enhances everyone’s learning
- Strive to maintain a spirit of curiosity and generosity, particularly in the face of new and/or seemingly contradictory information and perspectives Encourage and solicit feedback from students to continually improve inclusive practices.
Assignment due dates will not be changed because of exams or assignments in other courses or because of conflicting vacation travel plans. Late submissions will receive a 10% point reduction for every day that they are late. After seven days, late submissions will receive no points. Corrected submissions will not be accepted unless stated otherwise. You must inform the instructor on the first week of class if you cannot attend an exam or presentation due to extenuating circumstances, such as medical procedures or professional travel. Attendance on the day of the presentation and exams is otherwise required to receive points for those activities.
Readings for a particular class should be completed before the class session and before completing associated activities.
The best way to help prevent technical issues from causing problems for assignments and quizzes is to submit them at least 24-36 hours before the due date and time. Your instructor cannot resolve technical issues, but it’s important to notify them if you are experiencing issues. If you have problems submitting an assignment or taking a quiz in Sakai, immediately do the following:
- Contact he UNC Information Technology Services (ITS) department with the time you attempted to do your course action and what the course action was.
- Send a Slack message to your instructor with the information you sent to ITS and what time you sent the information.
The ITS department provides technical support 24-hours per day, seven days per week. If you need computer help, please contact the ITS Help Desk by phone at +1-919-962-HELP (919- 962-4357), or by online help request at http://help.unc.edu/help/olhr, or by UNC Live Chat at http://help.unc.edu/chat.