COVID-19 Considerations

This semester is going to be weird.

1 Course Information

Instructor Course
John Paul Helveston Tuesdays
Science & Engineering Hall, Office 2830 Anywhere!
+1 (202) 994-7173 Aug. 31 - Dec. 12, 2020
jpg@gwu.edu 12:45PM - 03:15PM EST
@JohnHelveston Slack

Overview of online mode

  • All synchronous class periods will be held online during the designated class time: Tuesdays from 12:45PM - 03:15PM EST.
  • All class periods will be recorded and posted on blackboard.
  • Students are encouraged to stream or download the recordings for personal use only and are not permitted to post or share the recordings.
  • We will hold multiple online study sessions every week.
  • All quizzes and exams will be held online during the designated class periods.

2 Course Description

This course provides a foundation in programming for analytics using the R programming language with a comparison to Python. Topics covered include fundamentals of programming (operators, data types, objects, functions, conditionals, loops, strings, testing, and debugging) as well as techniques for working with data sets in R and Python (file input/output, data structures, data wrangling, data visualization, external packages, and reproducible reporting). Emphasis will be on producing clear, robust, and reasonably efficient code using top-down design, informal analysis, and effective testing and debugging. Students will primarily work on individual programming assignments to develop skills in computational problem solving, writing code, and working with data. Students will be assessed through quizzes, homework assignments, and exams. Teaching will involve interactive lectures with plenty of time spent live coding and working on practice problems in class. This course assumes no prior programming experience and is an ideal preparation for higher level courses in data analytics.

2.1 Prerequisites

There are no prerequisites. Students are assumed to have zero prior programming experience for this course.

3 Learning Objectives

Having successfully completed this course, students will be able to:

  • Develop simple programs to effectively solve medium-sized tasks by:
    • Employing modular, top-down design in program construction.
    • Pro-actively creating and writing test cases to test and debug code.
    • Applying computational problem-solving skills to new problems.
  • Write clear, robust, and reasonably efficient code for working with data using:
    • Sequential, conditional, and loop statements.
    • Numeric, string, and logical data types.
    • Data structures, including lists, vectors, and data frames.
  • Reproducibly import, export, manipulate, and visualize data.

4 Pep Talk!

Learning a programming language can be as challenging as learning a new spoken language. Hadley Wickham - the chief data scientist at RStudio, and author of many amazing R packages you’ll be using - made this wise observation:

It’s easy when you start out programming to get really frustrated and think, “Oh it’s me, I’m really stupid,” or, “I’m not made out to program.” But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later.

If you’re finding yourself taking way too long hitting your head against a wall and not understanding, take a break, talk to classmates, ask questions in Slack, e-mail me, etc.

I promise, you can do this.

5 Required Texts & Materials

All texts and software for this course is freely available on the web. This includes:

5.1 Software

5.2 Texts / Other Useful Resources

This course does not require any textbooks. All learning materials and resources are freely available on the web. See the course references page for a list of useful resources.

6 Assignments

6.1 Class Participation

Regular class attendance is essential to succeed in this class. Multiple unexcused absences, inappropriate or unprofessional behavior during class (such as monopolizing discussions or being rude or disruptive), not participating in classroom exercises, and not being prepared for class will result in a lower grade for the class participation component. As a rule of thumb, the participation grade will be assigned according to the following rubric:

Score Attendance Classroom
Low Frequently absent Rude; disruptive; distracting; monopolizes discussions
Moderate Attended most classes, but often arrived late or left early Takes notes; attentive; occasionally contributes in class discussion / exercises
High Attends on time and prepared Takes notes; attentive; regularly contributes in class discussion / exercises; does not dominate conversation; listens and responds thoughtfully to comments made by others

6.2 Homework Assignments

Homework assignments contain a mix of coding exercises and written exercises. They assess the material taught the week(s) they are assigned, and should take several hours to complete. Start homeworks early.

Read the Collaboration Policy about collaborating with fellow students on homeworks. While most problems must be worked on individually, some will be marked as “collaborative”; on these problems (and only these problems), you may work on code with other students.

Homeworks will be graded based on style (modularity, readability, commenting, etc.) and functionality (correctness on a series of tests). Your code should be properly annotated with comments that are well-placed, concise, and informative. Your assignments will be graded by an automated grader and the instructional team.

6.3 Quizzes

There will be several quizzes given about once every two weeks immediately at the beginning of class. Quizzes cover material presented in previous classes and assignments during the weeks since the most-recent quiz. Quizzes are designed to be time-intensive, to test for fluency, and to demonstrate where additional study is needed. Quizzes are low-stakes - your worst one is dropped, and the rest count for just 15% of your final grade. If you do poorly on one, use that as feedback on where you need to focus more of your energy for improvement.

Why quiz at all? Research shows that giving small quizzes throughout a class can dramatically help with retention. It’s a phenomenon known as the “retrieval effect” - basically, you have to practice remembering things, otherwise your brain won’t remember them. The phenomenon and research on it is explained in detail in the book “Make It Stick: The Science of Successful Learning”, by Brown, Roediger, and McDaniel.

6.4 Exams

There will be one midterm exam covering the first 6 weeks of class, and a standard final exam during the final exam period at the end of the semester covering material from the entire semester. See the schedule for details.

7 Grading

7.1 Standard Grading

Final grades will be calculated as follows:

Course Component Weight Notes
Homeworks 48% 12 x 4% each
Quizzes 15% 5 x 3% each; lowest two dropped
Midterm Exam 12%
Final Exam 20%
Participation 5%

7.2 AMG Grading

This Alternative Minimum Grading (AMG) policy is available to everybody, but is designed specifically for students who struggle in the first part of the course, and then through sustained hard work and dedication manage to elevate their performance in the latter part of the course to a level that merits passing with a C (even if their Standard Grade might be lower than that).

Student cannot “sign up” for AMG grading. Every student will be considered both for Standard Grading and AMG, and the instructor can choose to assign the AMG grade if a student’s effort merits it. To qualify for AMG you must put forth sustained effort, which means meeting the following requirements:

  • You attend all class periods (with excused exceptions)
  • You complete all assignments
  • You do not violate the Collaboration Policy

To compute your AMG score, first use the following to compute your raw score. If the resulting score is higher than a C, set it back to a C.

Course Component Weight
Best 10 Homeworks 40%
Best 4 Quizzes 10%
Midterm Exam 10%
Final Exam 40%

7.3 Grading Scale

Grade Range Grade Range
A 93 - 100% C 73 - 76.99%
A- 90 - 92.99% C- 70 - 72.99%
B+ 87 - 89.99% D+ 67 - 69.99%
B 83 - 86.99% D 63 - 66.99%
B- 80 - 82.99% D- 60 - 62.99%
C+ 77 - 79.99% F < 60%

The course instructors may choose to change the scales at their discretion. You are guaranteed that your letter grade will never become worse as a result of changing scales.

8 Getting Help

This class can be challenging - don’t suffer in silence. Look at the “Getting Help” page for ways to get resources that can help you succeed.

9 Course Policies

9.1 tl;dr

  • BE NICE. BE HONEST. DON’T CHEAT.
  • Write your own code - don’t look at others’ code & don’t let others look at your code.
  • Homework due dates are “soft”; if you submit an assignment, it will be graded (up until Oct. 20 for homeworks 1-6, and Dec. 8 for homeworks 7-12).

9.2 Late Policy

Homework assignment due dates are “soft,” but the longer you wait to complete each assignment, the poorer you’ll likely do on quizzes and exams.

There are two hard deadlines for submitting homework assignments:

  • Oct. 20: Homeworks 1-6
  • Dec. 8: Homeworks 7-12

These deadlines are to ensure that you will be prepared for the Midterm (which covers weeks 1-6) and the Final Exam (which is cumulative).

9.3 Collaboration Policy

Learning how to program is like learning how to ride a bicycle - to get better, you must practice writing code yourself. Therefore, we have a set of strict rules regarding what kind of collaboration is allowed, what counts as over-collaboration, and what counts as cheating.

9.3.1 Good Collaboration

  • Discussing which general concepts might be useful in solving a problem (conditionals, loops, etc.).
  • Asking for debugging help with code.
  • Sketch out algorithms on a whiteboard together.
  • To avoid copying the code, you should write up the solution together, discuss it, then erase the solution, wait a few minutes, and write up solutions individually.
  • Help each other debug specific parts of assignment code.
  • General discussion of course concepts.
  • Detailed explanations of example code on the course website.
  • Collaboratively solving a practice problem, with any level of co-writing code and co-debugging.

9.3.2 Over-collaboration

Over-collaboration results in a warning on the first offense, and a penalty on later offenses. Examples include:

  • Explaining to a friend how to solve a problem in high-level terms by going through your own program line-by-line.
  • Helping a friend debug code by suggesting they use your own approach to the problem.
  • Collaborating with a student on an assignment and then not including their name as a collaborator in the assignment writeup.
  • In the case that you have taken this course before, copying your own code from the previous time you took the course.

9.3.3 Cheating

Cheating results in a penalty on the first offense, and failing the course on the second offense. Cheating on assignments can include:

  • Copying or stealing any amount of code from someone currently in the class or someone who has taken the class before.
  • NOTE: Copying is never okay, whether the code is provided electronically, visually, audibly, or on paper.
  • Providing code you have written for an assignment to anyone else in the class.
  • Finding code online and using it in the assignment. One exception: you may use code from the course website.
  • Putting code solutions from the course assignments online.
  • Receiving code-level assistance from any person not associated with the course.
  • Getting someone else to write the assignment code for you.
  • Asking questions about the assignments on any online services outside of the course office hours / Slack.

Cheating on quizzes, assignments, or the final project can include:

  • Referring to any external resources while completing the assignment (phones, notes, etc.).
  • Copying part of an answer off of another student’s paper, even if it is very small.
  • Using solutions provided by students who previously took the course.

9.3.4 Penalties

Penalties are decided by the course instructors, and can vary based on the severity of the offense. Possible penalties include:

  • Receiving a 0 on the assignment/quiz in question.
  • Receiving a -100 on the assignment/quiz in question.
  • Receiving a full letter grade deduction in the course.
  • Automatically failing the course.

Penalties may also be accompanied by a letter to the Dean of Student Affairs, again at the instructors’ discretion. This can lead to university-level penalties, such as being suspended or expelled.

9.3.5 Plagiarism Detection

Programs are naturally structured, which makes them very easy to compare for plagiarism. Automated plagiarism detection systems make this process even easier. Watch this video showing plagiarism detection software in action (this example is using Python code, but this also works for R code).

In short, if you copy code, we will know - don’t copy code!

9.3.6 Grace Period

College is a time when you do a lot of learning. Sometimes, you might make bad decisions or mistakes. The most important thing for you to do is to learn from your mistakes, to constantly grow, and become a better person.

Sometimes, students panic and copy code right before the deadline, then regret what they did afterwards. Therefore, you may rescind any homework submission for up to 24 hours after the deadline with no questions asked. Simply email the course instructors asking to delete the submission in question, and we will do so. Deleted submissions will not be considered during plagiarism detection, though of course they will also not be graded. However, it will always be better to get a 0 (or partial credit) on an assignment than to get a cheating violation!

9.4 Lauren’s Promise

I will listen and believe you if someone is threatening you.

Lauren McCluskey, a 21-year-old honors student athlete, was murdered on October 22, 2018 by a man she briefly dated on the University of Utah campus. If you are in immediate danger, call 911 or GWU police at 202-994-6111 (GWPD). If you are experiencing sexual assault, domestic violence, or stalking, if you report it to me I will listen and connect you to resources or call GWU’s Counseling and Psychological Services (202-994-5300).

Any form of sexual harassment or violence will not be excused or tolerated at GWU. GWU has instituted procedures to respond to violations of these laws and standards, programs aimed at the prevention of such conduct, and intervention on behalf of the victims. GWU Police officers will treat victims of sexual assault, domestic violence, and stalking with respect and dignity. Advocates on campus and in the community can help with victims’ physical and emotional health, reporting options, and academic concerns.

9.5 Use of Course Materials

All course materials available on the course website are developed open source - you are welcome to post and share them following the licensing guidelines listed in the license page.

However, all solutions to assignments and quizzes are proprietary. Don’t post them online or try to sell them - this would violate the GWU student code of conduct.

Students are encouraged to use electronic course materials, including recorded class sessions, for private personal use in connection with their academic program of study. Electronic course materials and recorded class sessions should not be shared or used for non-course related purposes unless express permission has been granted by the instructor. Students who impermissibly share any electronic course materials are subject to discipline under the Student Code of Conduct. Please contact the instructor if you have questions regarding what constitutes permissible or impermissible use of electronic course materials and/or recorded class sessions. Please contact Disability Support Services if you have questions or need assistance in accessing electronic course materials.

9.6 What To Do if the Instructor Does Not Arrive

Wait 20 minutes, after that you’re free to leave. One member of the class should be selected to notify the EMSE Department of the Instructor’s absence by calling the EMSE Department 202-994-4892 on next business day.

10 University Policies

10.1 University Policy on Religious Holidays

In accordance with University Policy, students should notify faculty during the first week of the semester of their intention to be absent from class on their day(s) of religious observance. Official university policy here: https://students.gwu.edu/accommodations-religious-holidays

  • Students should notify faculty during the first week of the semester of their intention to be absent from class on their day(s) of religious observance.
  • Faculty should extend to these students the courtesy of absence without penalty on such occasions, including permission to make up examinations.
  • Faculty who intend to observe a religious holiday should arrange at the beginning of the semester to reschedule missed classes or to make other provisions for their course-related activities.

10.2 Support for Students Outside the Classroom

Disability Support Services (DSS): Any student who may need an accommodation based on the potential impact of a disability should contact the Disability Support Services office at 202-994-8250 in the Rome Hall, Suite 102, to establish eligibility and to coordinate reasonable accommodations. For additional information please refer to: https://disabilitysupport.gwu.edu/

Mental Health Services (202-994-5300): The University’s Mental Health Services offers 24/7 assistance and referral to address students’ personal, social, career, and study skills problems. Services for students include: crisis and emergency mental health consultations confidential assessment, counseling services (individual and small group), and referrals. https://healthcenter.gwu.edu/counseling-and-psychological-services

10.3 Academic Integrity Code

Academic dishonesty is defined as cheating of any kind, including misrepresenting one’s own work, taking credit for the work of others without crediting them and without appropriate authorization, and the fabrication of information. For the remainder of the code, see: https://studentconduct.gwu.edu/code-academic-integrity

In addition to the formal code of academic integrity, the instructor expects that students will treat this course with the level of professionalism required in the workplace. Remember that real firms are sponsoring student projects throughout the semester; in a workplace setting, these firms would be paying clients for the analyses being conducted. This course prepares students to succeed in the workplace, and maintaining a high degree of professionalism is expected.

11 Cute Animals

Once you have read this entire syllabus and viewed the course schedule, please send me a cute picture of your favorite animal in a direct message on Slack.

For real.

Brownie points if it’s animated.

Page sources:

Some content on this page is inspired by and / or modified from other sources:

  • The “Pep Talk” and “Cute Animals” sections are inspired by Andrew Heiss’s courses.
  • The collaboration policy is mostly copied from the course 15-112 at Carnegie Mellon University
EMSE 4574: Programming for Analytics (Fall 2020) |
Tuesdays | 12:45 - 3:15 PM | Dr. John Paul Helveston | jph@gwu.edu
Content 2020 John Paul Helveston. See the licensing page for details.