ARCHY 208 A: Introduction to Archaeological Data Science

Autumn 2022
Meeting:
TTh 8:30am - 10:20am / NAN 181
SLN:
23638
Section Type:
Lecture
Joint Sections:
ARCHY 560 A
THIS CLASS IS INTENDED FOR STUDENTS WITH NO PRIOR PROGRAMMING EXPERIENC THERE ARE NO PREREQUISITES. IF YOU'VE TAKEN ANY CSE CLASS AND PASSED, THEN YOU LIKELY FIND THE CLASS UNSATISFYING AND WE ASK THAT YOU FIND ANOTHER CLASS.
Syllabus Description (from Canvas):

COURSE DESCRIPTION

This course is an introduction to basic methods of archaeology and data science. We will learn some of the key data science tools used in day-to-day archaeological and cultural heritage work, which are also becoming increasingly valued and popular in a wide variety of other research areas and professions. We will use these data science tools to tackle fascinating archaeological questions with contemporary relevance, for example:  What is the purpose of Palaeolithic cave art? When were the Egyptian pyramids built (and were aliens involved)? Where did the stones in Stonehenge come from? Why did the Mayan civilisation collapse? Why are there giant stone heads on Easter Island? How does archaeological evidence change what we know about the Nazi Death Camps? Woven throughout our exploration of these questions is an examination of the ethics and social impacts of archaeology and data science. This class will give students hands-on experience with tools and methods to prepare them for 400-level ARCHY courses, as well as quantitative work in many other fields. This class has no prerequisites. This is an introductory, novice-friendly class for students with no prior experience with computers, programming, statistics and archaeology. Students with prior experience in these programming and statistics should consider ARCHY 495/496 instead.

All materials will be delivered in the class meetings and via the course canvas page. This is a lower-division class that is open to all majors and expects no prior knowledge of archaeology or data science. This is a five credit course, so you should plan to do 10 hours of homework per week. This course is part of the Minor in Data Science (I am the director of this program, feel free to ask any questions!) and will contribute to your NW (Natural World) requirement

STUDENT LEARNING OUTCOMES

  • Understand the different kinds of data commonly used by archaeologists.
  • Explain how to make good decisions about using data to answer archaeological questions.
  • Apply basic statistical methods to summarize data and identify relationships in archaeological data
  • Manage, summarise, analyse and visualise archaeological data analysis pipelines with the R programming language and Quarto notebooks.

REQUIRED COURSE MATERIALS

You don't need to buy any texts for this class. All of the core readings will be accessible as PDFs or webpages, which we will provide to you directly. Optional readings will be similarly available online, either on the web generally, via the UW library's online resources or other sources. For unrestricted off-campus access to UW library content, you should install the Husky OnNet VPN by following the instructions here

To complete the assignments it will be useful to have a computer with Internet access and the following:

  • A web browser. We recommend Chrome
  • Speakers, headset, or earbuds to view the videos
  • Reliable broadband Internet connection (DSL or cable) to stream videos.

As a UW student you can borrow laptop and tablet computers for the duration of the academic quarter. The STLP offers no contact appointments as well as shipping. Email stlp@uw.edu or call 206-685-6090.

You will need access to RStudio and R to do the weekly lab reports. These are free, and you can find more details on getting access here. I have listed a few optional general introductory readings for archaeology and data science on our Canvas page. If you have trouble finding or accessing any readings, let us know so we can help you. You should not have to pay to read anything to succeed in this class. 
Current UW policy for Fall 2022 requires courses to be delivered in person, with all instructors and students strongly recommended to wear masks (regardless of vaccination status) covering your nose and mouth when in UW buildings, for the first two weeks of the quarter. I recognise that some students have documented medical conditions that place them at higher risk for complications from COVID-19. Requests for accommodations related to COVID-19 must be submitted to the office of Disability Resources for Students. For more information about vaccinations, testing, and UW policies on COVID, see https://www.washington.edu/coronavirus/ For specific information about what precautions are required in classrooms and laboratories, see https://www.washington.edu/coronavirus/covid-prevention-in-learning-spaces. If you are sick with any illness, you must stay home, even if you are fully vaccinated. 

COURSE FORMAT/STRUCTURE

Our scheduled class meeting times are TTh 8:30-10:20 am PST. Our first class for this quarter is on a Thursday, so we'll start our weekly cycle on that day also:

  • Our Thursday class will be divided into mini-lectures that will be delivered at the scheduled class time, with participation activities and breaks in between. 
  • Our Tuesday class will be divided into mini-lab sessions, with breaks in between. I will demonstrate data science skills that will help you complete the lab assignments. Our TA will give a brief summary of feedback on your lab assignments.  

This course is scheduled to run in person, synchronously at our scheduled class time.  We are not recording class meetings. If you miss class you will be able to download the slides from the module page on Canvas, and you should make arrangements to contact a peer to catch up on what you missed. If another student asks you to share your notes to help them catch up, please share generously. 

Sharing your recordings of the class, and other class materials, outside of class that include personally identifiable student information without the written consent of those students is a violation of FERPA. State law requires consent from people to be recorded. Please note that you are not permitted to make your own recordings without consent from the instructor and everyone else involved. For more information about privacy concerns, review the UW Privacy Office policies, or contact Helen Garrett, the UW's FERPA Officer. 

STUDENT AND INSTRUCTOR EXPECTATIONS

Expectations for the instructor and TA: We will show our respect to you by arriving to class each day with extensive notes and teaching plans to help you consider how the readings connect to our class discussions and your interests as they emerge. We will be flexible and responsive to your needs as a learner so please always let us know if we can make changes to our assignments or classroom activities that better support your learning. We will also be timely with grading so you receive grades for submitted work within a week. We will continue to support your learning and future opportunities long after this class is over. We will make space for you to guide the class in ways that are important to you so you can take ownership for your education, and develop your leadership and research skills.

Expectations for you: We all have responsibility for our own learning, but also the learning of one another. I have four expectations for you that will guarantee that you will do well in the class, and also that you will contribute to the learning of others:

  1. Come to every class, and be on-time both to class and in submitting your work;
  2. Complete all the readings/viewing before class and have access to them (or your notes on the readings) during class, and;
  3. Stay engaged with your classmates for the full class.
  4. Behave according to our code of conduct, which applies to all our online spaces for this class. 

In terms of your own experience, if you meet those four criteria you will get the full benefit of the education that you have worked so hard to receive, and you will prepare yourself for graduate school and/or fulfilling employment in a satisfying career.

Shared expectations: To create a set of shared expectations about how to make the class a satisfying learning experience, I sought your feedback via Poll Everywhere in our first class on three questions. I've summarised your responses in the syllabus here so they are easy to find (the anonymized full text of the responses are here):

I learn best when: the keywords in your responses were professor, discussion, engaging, interactive, learning, activities, organized, examples, hands-on, environment   

I don’t learn when: There is lot of/too much…  noise, distraction, reading, talking, busywork, text on the slides. There is not enough… engagement, feedback, relevant material, enthusiasm from the Professor

My peers help me learn when: They are… prepared, motivated, ask questions, excited, open minded, speak up in class, join in discussions, respectful, empathetic, helpful 

These are the behaviours and qualities that we expect from each other in this class. By participating in this class you acknowledge these expectations and agree to fulfill them to the best of your ability. If your behaviour is not consistent with these expectations, you may be asked to leave the class. 

ASSIGNMENTS

There are four types of assignment, three are weekly and one is a capstone:

Participation: Weekly, during class time, via Poll Everywhere. The purpose of this is to reward effort that you put into sharing your learning experience, and your building of community as an important component of education.

Annotations: Weekly, outside of class time, via Persuall. The purpose of this is to bring you into the conversation, and enable collaborative mastery and exchange ideas with your peers.  

Lab Reports: Weekly, outside of class time, submit to Canvas. The purpose of this is to get hands-on practice applying data science skills to archaeological questions.

Final Project: Capstone, outside of class time, submit to Canvas. The purpose of this is to demonstrate the skills and knowledge you have developed in this course through a collaborative exploration of your interest in archaeological science.

Find complete assignment details and due dates on the Assignments page.  To ask a question about assignments anonymously, please use this link. We will respond to these in class so you can get a response without revealing your identity. 
Missing a small number of the weekly assignments (participation, annotations and lab reports) will not affect your grade. Take a look at the grading table below to see how many assignments you can miss with no penalty. 

There are no grade penalties for late submissions. You can consider the due dates you see on Canvas as 'best submitted before' dates. However, you will find this course more satisfying of you complete the assignments on schedule, especially those that require collaboration with peers. 

If you submit any work after the due date, you are agreeing to wait until the end of the quarter for us to grade your work and give you feedback. In our grading and feedback we will prioritise work that is submitted on time. Of course we are flexible and can accommodate exceptions, but our general rule is to delay grading late submissions to incentivise on-time submissions and manage our workload. This closely resembles how deadlines work in the real world, in my experience. 

GRADES

We are using an additive grading method, based on ideas in specifications grading, labor-based grading, and anti-racist writing pedagogies. (For example, see the work of Linda Nilson and Asao Inoue). The main idea is that all assignments are scored out of some fraction of 4.0, and then all assignment scores are summed to determine a your final grade out of 4.0. The goal is to make grading highly transparent so you can understand how your work directly contributes to their your grade.

We have four types of assignment. Three of these recur on a weekly cycle, and a fourth that is the course capstone project. The table below shows how each assignment type, and each individual assignment, contributes to your final grade out of 4.0. Each assignment will be graded with a simple rubric that is visible on Canvas.

A few important things to know about our grading method: 

  • You don't need to do every single assignment to get a 4.0.  This gives you a lot of flexibility in when you do assignments. You have a lot of agency and self-direction, choosing assignments you are most interested in.
  • If you don't want to do the final project at all, you can still finish the course with a passing grade (assuming you've done well in everything else). 
  • If you get partial credit for most of your work on one assignment type, you can do additional assignments of that type, beyond the minimum number, to increase your score up to the maximum for that assignment type. 
  • There is no extra credit. If you do more assignments than the minimum required, it is because you want to explore your interests and achieve mastery of the skills. They will not hurt your grade, so you need not feel stress or jeopardy doing any assignment. You can take risks and experiment with the topics and skills.
  • The table below shows a week-by-week breakdown of assignments and grades so you can see how much each activity is worth to your final grade. You can use this information to plan how you use your time for this course. To track the progress of your grade over the quarter, look on Canvas gradebook for 'Additive grade (40 ≡ 4.0)'. The number next to that label is your grade times 10. So if you divide that number by 10, you'll get your grade as it will appear on your transcript. We calculate this value outside of Canvas, and aim to update this every week. 

The table below comes from our Google Sheet and shows how each assignment type contributes to your final grade:


The table below shows a week-by-week breakdown of assignments and grades so you can see how much each activity is worth to your final grade. You can use this information to plan how you use your time for this course. To track the progress of your grade over the quarter, look on Canvas gradebook for 'Additive grade (40 ≡ 4.0)'. The number next to that label is your grade times 10. So if you divide that number by 10, you'll get your grade as it will appear on your transcript. We calculate this value outside of Canvas, and aim to update this every week. 

If you would like to request a regrade, please submit a written request within 48 hours of receiving the grade. Your request should include a detailed, well-thought-out argument that explains how your work meets the requirements of the assignment/rubric.

To be fair to all students in the course, extra credit is not offered on an individual basis. To ask a question about grades anonymously, please use this link.

RESOURCES

Housing & Food Insecurity

The Doorway Project offers resources specific to the U District, links to Emergency Food Resources Map and connections to Mutual Aid Solidarity Networks The ROOTS Young Adult Shelter provides overnight shelter to people age 18-25. Any Hungry Husky offers a UW food pantry, providing students, staff, and faculty non-perishable goods at no cost. If you know other resources, please share them with me and I’ll post them for everyone.

Emergency Aid

Emergency Aid at UW-Seattle is also there to support students for emergency needs. In addition to connecting students to resources, they offer short term loan funds and counseling

Physical and Mental Health

Your physical and mental health are of the utmost importance. A variety of available services are described on this Student Life web-page.  Physical and mental health services are available via Husky Health & Well-Being, as well as through the Counseling Center. There are a variety of programs to help promote your health, including alcohol and drug use  education, suicide intervention , and many others. Health Advocates offer confidential advocacy and support for students impacted by sexual assault and other related experiences. If you are experiencing physical or mental health issues that prevent your from completing your coursework, please reach out to your instructor to make the appropriate accommodations.

Washington Warm Line Offers emotional support and comfort by trained peer volunteers. Hours: M-F 5:00pm - 9:00pm; Sat & Sun 12:30pm – 9:00pm, 1-877-500-WARM or 1-877-500-9276

King County Crisis Line Available 24/7, immediate help for individuals, families, and friends of people in emotional crisis. Can connect to emergency mental health consultation/evaluation if needed. 1-866-4CRISIS or 1-866-427-4747 

Crisis Text Line Text support for those in emotional distress and/or struggling with a range of concerns including depression, anxiety, suicide, abuse, bullying, self-harm, loneliness, etc. Text HOME to 741741 

Legal support

Student legal services is available for confidential legal advice and representation to  students. The University has outlined a strong anti-discrimination policy for the campus, which governs the behavior of students, staff, and faculty members. For reporting instances of discrimination or harassment, you may contact the University Complaint Investigation and Resolution Office. Students have also noted that Ethnic Cultural Center (ECC) is a strong community resource for undocumented students.

ACADEMIC CONDUCT

This short statement by the Committee on Academic Conduct in the College of Arts and Sciences will help you avoid unintentional misconduct and clarify the consequences of cheating. The university’s policy on plagiarism and academic misconduct is a part of the Student Conduct Code, which cites the definition of academic misconduct in the WAC 478-121 (WAC is an abbreviation for the Washington Administrative Code, the set of state regulations for the university. The entire chapter of the WAC on the student conduct code is here) According to this section of the WAC, academic misconduct includes:

“Cheating”—such as “unauthorized assistance in taking quizzes”, “Falsification” “which is the intentional use or submission of falsified data, records, or other information including, but not limited to, records of internship or practicum experiences or attendance at any required event(s), or scholarly research”; and “Plagiarism” which includes “[t]he use, by paraphrase or direct quotation, of the published or unpublished work of another person without full and clear acknowledgment.”

The UW Libraries have a useful guide for students at http://www.lib.washington.edu/teaching/plagiarism

RELIGIOUS ACCOMMODATIONS

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

INCLUSIVITY

Among the core values of the university are inclusivity and diversity, regardless of race, gender, income, ability, beliefs, and other ways that people distinguish themselves and others. If any assignments and activities are not accessible to you, please contact me so we can make arrangements to include you by making an alternative assignment available.

Learning often involves the exchange of ideas. To include everyone in the learning process, we expect you will demonstrate respect, politeness, reasonableness, and willingness to listen to others at all times – even when passions run high. Behaviors must support learning, understanding, and scholarship. 

If you read, see or hear something in any class meeting or among any class materials that you found offensive or exclusionary, please make an anonymous report here so we can immediately remove it or otherwise manage it. You can also use that anonymous feedback to confidentially ask questions or share observations about the class. Other options for reporting your concerns include the Chair of the Anthropology Department, the University of Washington ombud office and the UW SafeCampus Office.

Catalog Description:
Pyramids, Stonehenge, Nazi Death Camps: Pseudo-archaeology makes radical claims about such sites, but what do the data reveal? Tackles false claims about the human past using archaeological data. Hands-on experience of data analysis and visualization using the software program R in computing laboratories.
GE Requirements Met:
Natural Sciences (NSc)
Credits:
5.0
Status:
Active
Last updated:
April 27, 2024 - 3:45 pm