Syllabus

Course Information

Instructor Course
John Paul Helveston Thursdays
Science & Engineering Hall, 2830 Corcoran Hall 207
+1 (202) 994-7173 Jan. 19 - May 11, 2023
jph@gwu.edu 12:45PM - 3:15PM EST
@JohnHelveston Slack

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.

Prerequisites

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

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.

Pep Talk!

Working in and learning a programming language can be as challenging as learning a new spoken language. Hadley Wickham (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, and try it again later.

I promise, you can do this!

Required Texts & Materials

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

Software

Go to the Course Software page for detailed instructions on how to install the software we’ll be using. Here’s a quick list:

Texts

The main textbook we will use for this course is “Programming for Analytics in R”, by Professor Helveston. The book is available freely on the web at https://p4a.jhelvy.com/.

Assignments

Participation & Attendance

Attendance will be taken and will be part of your participation grade.

In-person attendance is critical as we will spend a lot of time working on problems and writing code during class. You should plan to attend class in person each week.

There will be no option for remote attendance (e.g. zoom), and class sessions will not be recorded. If you have any questions or need special accommodations, send me a message on slack and we can discuss. If you must miss class for an excused absence (e.g. medical issue), please message Professor Helveston and get notes from your fellows classmates.

Multiple 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 poor score for attendance and participation.

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.

Readings

In addition to weekly homework assignments, students are expected to read through the assigned weekly reading to prepare for the next class. Check the schedule for the reading assignments.

Quizzes

There will be 7 quizzes given about once every other week immediately at the beginning of class. Make up quizzes will not be available if you miss it (except for excused absences). Please show up on time to class each week to ensure that you do not miss a quiz. Quizzes will cover material presented in previous classes and assignments during the weeks since the most-recent quiz.

Quizzes are short (10 minutes) and are designed 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 a small portion of your final grade. If you do poorly on one or two, use that as feedback on where you need additional 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.

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.

Grading

Standard Grading

Final grades will be calculated as follows:

Component Weight Notes
Participation / Attendance 7% (Yes, I take attendance)
Homeworks & Readings (13x) 48% Lowest 1 dropped
Quizzes (7x) 15% Lowest 2 dropped
Midterm Exam 10%
Final Exam 20%

Here’s a visual breakdown by category:

AMG Grading

An 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%

Grading Scale

Grade Range Grade Range
A 94 - 100% C 74 - 76.99%
A- 90 - 93.99% C- 70 - 73.99%
B+ 87 - 89.99% D+ 67 - 69.99%
B 84 - 86.99% D 64 - 66.99%
B- 80 - 83.99% D- 60 - 63.99%
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.

Rounding

I do not round final grades. Rather, I grade generously throughout the semester. For example, if you give a quiz your best shot and completely fail it, you will likely get a 50% instead of a 0%. The 50% is for trying (if you simply don’t take it, you’ll get a 0%). As a result, I will not modify or round your final score, even if you’re very close to a different letter grade (e.g., a 93.98 is an “A-” and will not be rounded up to an “A”).

Getting Help

This class can be challenging - do not suffer in silence! We have lots of ways to get help.

Slack

All course communication will be managed through Slack. A link to sign up for the course slack page can be found on the one (and only) announcement on Blackboard.

You can use Slack to:

  • Ask general questions.
  • Ask for help with an assignment.
  • Send direct, private messages to each other or the instructors (just like email…but better!)

Asking for help on Slack: You can post questions on slack and receive quick responses. This also enables other students to see answers to common questions. Be specific - if your code has an error you don’t understand, include the code and the error message in your question.

AMA Hours

I will hold regular “Ask Me Anything” hours each week. The specific hours will be posted on the course slack. During this period, you can come to my office (SEH 2830) and ask me anything. And I mean anything. Most of the time you may just have a question related to class, but I am also happy to discuss anything else with you regarding life, important decisions, career, art, music, dance, etc.

If the AMA hours don’t work with your schedule, you can always schedule a zoom call with me using this link. I’m available most days of the week.

Tutoring hours

Your class tutors will each hold a dedicated period of time each week for zoom tutoring hours. Please don’t make your tutors sit and do emails for two hours - come by and ask for help!

Library Services

While the University Library is not a stand in for TAs, you can schedule a consultation for general help with Coding, Programming, Data, Statistical, and GIS. See more at https://academiccommons.gwu.edu/writing-research-help

Course Policies

tl;dr

  • BE NICE
  • BE ON TIME
  • BE HONEST
  • DON’T CHEAT
  • SERIOUSLY: DON’T CHEAT

Late Policy

Each students is allowed 3 late homework submission days - use them however you want, no questions asked. These are meant to cover illness, family emergencies, and religious holidays.

Beyond those, assignments are due by 11:59pm on the assigned due date unless specified otherwise. Assignments submitted up to 24 hours past this deadline will be graded to a max of 50% of the available points. Assignments submitted beyond 24 hours past the deadline will not be graded and will be assigned a 0.

If you need a special accommodation such as due to an illness, family emergency, or religious holidays, contact the instructor ahead of time.

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.

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.

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.

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. 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 someone or something 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 or 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.

Use of chatGPT and other AI tools

Large language models (LLMs) are capable of producing exact solutions to some of the coding problems assigned in this class. So we all basically have a solutions manual available for free.

With this in mind, the use of AI tools is permitted as follows:

  • Assignments 1-7: Not permitted. Copying solutions from these engines and submitting them as your own for these assignments will be considered cheating. In this section of the course, we are building foundational skills in computational thinking and problem solving. If you don’t master these skills, you won’t be able to interpret what chatGPT is giving you, and you won’t be able to test if it is wrong. Master these skills first - it is worth the effort.
  • Assignments 8-13: Permitted, with caveats. If you do use AI tools on these assignments, you must include the prompt(s) you used as well as changed to made to them to get better results to receive full credit.

Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

In the follow-on course to this one (EMSE 4572: Exploratory Data Analysis), the use of AI engines will be allowed to support your projects.

Penalties

Violations will be reported to the Office of Student Rights & Responsibilities. 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 full letter grade deduction in the course.
  • Automatically failing the course.

Depending on the student’s prior academic history, violations 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.

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!

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!

Children in class

I applaud all of you who attend school with children! It is difficult to balance academic, work, and family commitments, and I want you to succeed. Here are my policies regarding children in class:

  1. All breastfeeding babies are welcome in class as often as necessary.
  2. Non-nursing babies and older children are welcome whenever alternate arrangements cannot be made. As a parent of young children, I understand that babysitters fall through, partners have conflicting schedules, children get sick, and other issues arise that leave parents with few other options.
  3. In cases where children come to class, I invite parents/caregivers to sit close to the door so as to more easily excuse yourself to attend to your child’s needs. Non-parents in the class: please reserve seats near the door for your parenting classmates.
  4. All students are expected to join with me in creating a welcoming environment that is respectful of your classmates who bring children to class.

I understand that sleep deprivation and exhaustion are among the most difficult aspects of parenting young children. The struggle of balancing school, work, childcare, and graduate school is tiring, and I will do my best to accommodate any such issues while maintaining the same high expectations for all students enrolled in the class. Please do not hesitate to contact me with any questions or concerns.

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. We must all take action to ensure that this never happens again.

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.

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 is a violation of the student code of conduct and it is also a violation of the class cheating policy.

Recording in Class

You are free to record class periods (audio and / or video) for your own personal study use and only for your personal study use. If you do record a class period, you are prohibited from posting or sharing the content online or with others.

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.

University Policies

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.

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

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.

Cute Animals

Once you have read this entire syllabus and viewed the course schedule, please send me a picture of 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 cheating policy is modified from the course 15-112 at Carnegie Mellon University