• Credit hours: 3.0 credit hours
  • Prerequisites: None, but a background in algebra is assumed
  • Mason Core: Natural science (+lab when taken with CDS 102)

Learning outcomes

By the end of the course, students will be able to:

  • Use Github for collaborating on a reproducible research document
  • Obtain, clean, transform, and visualize a dataset
  • Interpret dataset trends using basic statistical measures and models
  • Perform hypothesis tests on datasets using statistical inference



Category Weight
Participation 15%
Reading responses 15%
Assignments 20%
Midterm project 20%
Final project 30%

Final grade

Based on the final total score, your final grade will be determined as follows:

Score Range Final Grade
97–100 A+
93–96 A
90–92 A-
87–89 B+
83–86 B
80–82 B-
77–79 C+
73–76 C
70–72 C-
65–69 D
<65 F


During the course, we will use the following free and open-source software to manipulate data, perform computations, and write documents:

RStudio sits on top of R, Git, and LaTeX and provides a user-friendly interface to using these programs. You can think of R, Git, and LaTeX as plugins that activate RStudio’s main features. In practice, RStudio is the software application that we will learn to use during the course.

The course will be administered through the following online platforms:

Github will be the central “hub” for the course that manages our day-to-day activities. This includes the distribution of course materials, collecting assignments and projects, as a place to save our work, and for out-of-class discussions and collaborations. Blackboard will be used for posting course announcements and grades.

Class activities

Classes will typically consist of one or more of the following activities:

  • Short and medium-length lectures
  • Step-by-step example exercises
  • Code exploration and follow-up questions based on example exercises
  • Discussion of student questions
  • Peer code reviews
  • Groups: formulate-conference-consensus
  • Throwback exercises

The above list is not meant to be exhaustive and additional activities may be introduced during the course.



Students are expected to attend every class and are responsible for obtaining and understanding the material that they miss, including any examples that derive from resources not available online. Any missed work during an unexcused absence cannot be made up. Frequent absences, tardiness, or leaving class early are grounds for reducing your participation grade.

Absences due to religious holidays, scheduled varsity sports trips, and short-term illnesses should be brought to the instructors attention as early as possible. Any make-up work is to be completed within 24 hours. Exemptions may be granted at the instructor’s discretion.

In-class activities

Computational and data science is a diverse topic that draws on material from multiple disciplines, including statistics, computer science, and knowledge domains such as medicine, social science, and the natural sciences. As such, some of these topics may be new to you. In addition, many of our activities will require some amount of programming, which is an activity that’s best learned by doing. In-class activities and exercises are designed to re-enforce these concepts. Certain activities will be completed in pairs or in groups. A combination of completion and quality of work will be factored into your participation grade.

Prep-work assignments (as indicated)

From time to time you may be asked to complete a short exercise or tutorial to prepare you for the next class. You may be asked to submit evidence of completion. Successful completion and being prepared to work with or discuss these in class will be factored into your participation grade.


Reading assignments will be regularly scheduled during the semester. They will be posted on the course schedule with a reminder sent via Blackboard. Students are to complete the reading assignments by the specified date and are expected to engage with the material by submitting a minimum of 20 posts during the semester on the specified discussion threads on Github. The critieria for meeting the 20 post requirement is as follows:

  • During the semester, 10 posts will be submitted between August 28th and October 16 and 10 posts will be submitted between October 18 and December 6.
  • Each set of 10 posts will consist of at least 5 question posts and 5 answer posts.
  • A question post contains 2 (or more) questions about the reading assignment.
  • An answer post provides an answer to another student’s question about the reading assignment
  • You can earn, at maximum, 1 question post credit and 1 answer post credit per reading assignment.
  • Question posts must be submitted no more than 1 day after the reading assignment completion date to count for credit. Answer posts must be submitted no more than 7 days after the reading assignment completion date.
  • You can receive an answer post credit if you correct a mistake in the thread, provided that you can state what the mistake is and explain what the correct response should be in a civil and respectful manner.
  • You are encouraged to contribute follow-up posts to answers already provided, although you should refrain from answering new questions until the 7 day response period ends.
  • If, for some reason, all questions have been answered or you cannot figure out the answer to any of the remaining questions, contact the instructor as soon as possible.


There will be 5 assignments to complete during the semester, consisting of four homeworks and an extended in-class activity that will be completed over several class periods. Assignments will be submitted to Github as a new pull request with a title that begins with the word Submission and a description that tags both the instructor and the grader.

Assignments submissions are documents consisting of interwoven segments of writing and code blocks. It is expected that you will write in full sentences and use proper grammar and punctuation. You will be expected to explain what you are doing with each chunk of code and to interpret the meaning of what you calculate. The document’s style and formatting will also be taken into account during grading and should follow the course’s style guides for R and RMarkdown.

Students will be permitted to resubmit 1 of their assignments during the semester within a week of receiving their grade on the original assignment. The resubmissions are eligible to receive full credit and replace the lower grade. The updated assignment is to be submitted as a new pull request with a title that begins with the word Resubmission.

Students may discuss homework assignments outside of class, but the final submission must be in your own words. See the academic integrity section for specifics.

Midterm project

Students will complete a midterm project in assigned groups during the first half of the semester. For this project, you will perform an exploratory data analysis on a dataset, focusing on “wrangling” the dataset so that you can produce meaningful visualizations and interpret basic trends. More detailed information about the project will be provided in the coming weeks.

Final project

In lieu of a traditional final exam, students will be building a portfolio containing both comprehensive notes on the major R functions used during the course and an overview of accomplishments demonstrating a student’s learning and growth over the semester. Models for the format and content of the notes portion of the portfolio will be provided in the first few weeks and will be gradually built throughout the semester. Students will then present their portfolio to the instructor in a 5-10 minute interview during the scheduled final exam period. More information on both the portfolio and the final interview will be provided in the coming weeks.


Late work

Unless otherwise noted, assignments are to be submitted by 11:59pm on the due date. Up to two submissions that miss the deadline by 10 minutes or less will be accepted for full credit over the course of the semester.

The following penalties apply for most assignments (please note that weekends count as days):

  • First day late, by 11:59pm: -10%
  • Second day late, by 11:59pm: -20%
  • Third day or later: no credit

The writeup for the midterm project and your final course portfolio are exceptions and must be submitted by the due date. Late submissions will not be accepted.

Presentations, such as for the midterm project or the final interview, must be given on the scheduled date and cannot be made up.

Extensions or exemptions may be granted in the case of illness or a family emergency at the instructor’s discretion, and it is the student’s responsibility to inform the instructor about these kinds of circumstances as soon as possible.

Regrading appeals

Regrade appeals need to be in writing, printed out, and hand delivered to the instructor within 72 hours of receiving back an assignment. Email submissions will not be accepted, no exceptions. Appeals are only to be used for correct answers being marked as incorrect, misapplication of the grading rubric, or incorrectly tallied points. Submissions need to clearly state what you want regraded and to justify the request by citing evidence1. The number of points a question, exercise, or rubric category is worth or that were deducted for an incorrect answer or mistake cannot be appealed and are not up for debate or negotiation.

If the instructor is not available when delivering a request, please see Natalie Lapidot-Croitoru (231 Research Hall) or Karen Underwood (373 Research Hall). Ask them to initial the request, write down the date and time of receipt, and to hold it for instructor pickup.

Extra credit and grading curves

Individual requests for extra credit or a grading curve will not be granted, no exceptions. Any opportunities to earn extra points will be offered to the entire class. Grading curves are handled on a per-assignment basis and are applied to all students equally.


Students are expected to be civil in their classroom conduct and respectful of their fellow classmates and the instructor for the duration of the course. Examples of expected behavior include, but are not limited to:

  • Showing up to class on time
  • Not interrupting your classmates or the instructor
  • Silencing your cell phone
  • Refraining from texting/messaging
  • Refraining from using devices for anything other than coursework2
  • Removing ear-buds/headphones and sunglasses when class begins

The expectations of civil and respectful behavior still apply for all online discussions, including emails, peer code review, group collaborations, posting about reading assignments, etc. Students are still expected to follow proper grammar and punctuation in online posts and to refrain from using internet slang, abbreviations, and sarcasm.

The instructor will address violations of classroom decorum on a case-by-case basis. The instructor reserves the right to enact grade-based penalties for highly disruptive or repeat violations. Penalties for decorum violations cannot be negotiated or appealed.

Academic integrity

Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work.“3

Students may discuss non-group work outside of class, however in all instances it is required that your submitted responses to assignments are written in your own words. Do not duplicate or paraphrase another person’s material or ideas and represent them as your own. Content that comes from a resource or another student should be properly cited.

A note on sharing or reusing code found on other Github repos or on websites like Wikipedia or Stack Overflow. I am aware that there are solution sets, sample snippets of code, etc. that can be of use while working on your assignments, projects and exercises during the course. It’s common knowledge that researchers in both industry and academia will use search engines while writing code. Being able to search for existing solutions so that you don’t “reinvent the wheel” is a useful skill. Therefore, unless I specify otherwise, you are permitted to use these resources as long as you provide a citation.

Exceptions to this rule are:

  • For individual assignments, you cannot reuse another student’s code
  • For group assignments, you cannot reuse another group’s code
  • You are not permitted to use solution sets for the two main textbooks we’re using during the course

Any material that is taken in whole or in part from another source and not properly cited will be treated as a violation of Mason’s Academic Honor Code.

Other violations of Mason’s Honor Code will be treated similarly. Suspected violations will be reported to the Office of Academic Integrity. Please see the Honor Code page for details.

Support services

The Math Tutoring Center is in 344 Johnson Center; The Math Department also maintains a list of persons that have identified themselves as math tutors:

Mason’s Writing Center is in A114 Robinson Hall; (703) 993-1200;

George Mason provides Counseling and Psychological Services (CAPS) for students. Contact them at (703) 993-2380 or


Students with disabilities who need academic accommodations, please see the instructor and contact the Office of Disability Services (ODS) at (703) 993-2474. All academic accommodations must be arranged through the ODS:

Mason diversity statement

George Mason University promotes a living and learning environment for outstanding growth and productivity among its students, faculty and staff. An emphasis upon diversity and inclusion throughout the campus community is essential to achieve these goals. Diversity is broadly defined to include such characteristics as, but not limited to, race, ethnicity, gender, religion, age, disability, and sexual orientation. Diversity also entails different viewpoints, philosophies, and perspectives. Attention to these aspects of diversity will help promote a culture of inclusion and belonging, and an environment where diverse opinions, backgrounds and practices have the opportunity to be voiced, heard and respected.


The instructor reserves the right to modify this syllabus at any time during the course to improve the learning experience and classroom environment. These changes will be announced on Blackboard and the digital version of the syllabus will be updated to reflect the changes. The pacing of the course and the list of covered topics may also be altered in response to student progress.

The learning outcomes reflect what a student is expected to understand by the end of the course after putting in the necessary time and effort both inside and outside the classroom and completing all assignments. These outcomes are not a guarantee, and students will get more out of the course the more they put into it. Any acquired skills and knowledge can fade over time if not reviewed or practiced after the course concludes.

  1. Acceptable evidence includes in-class notes (provide date of class), a reading passage (provide full citation), or another valid source (textbooks, official publications, etc).

  2. The term “devices” is meant to be broad and includes classroom computers, laptops, cell phones, tablets and e-readers, smart watches, etc. Exceptions can be made in cases of family or personal emergencies, please speak to me before class.

  3. Office for Academic Integrity. 2017-2018 Honor Code and Honor System. Web. 27 Aug. 2017.