DA 101 - Introduction to Data Analytics

Spring 2021

Your Professor:

Matt Lavin

My Email:


My Office:

Burton D. Morgan Center 411

Office Hours

MW 3:30-5:00 p.m.; Th 1:00-3:00 p.m.; and F 1-2 p.m.

Our Classroom:

Burton D. Morgan Center 315

When We Meet:

MWF 11:30 a.m.-12:20 p.m.

When the Lab Meets:

Tuesdays, 1:50-4:40 p.m.

Course Description

Many of the most pressing problems in the world can be addressed with data. We are awash in data, and modern citizenship demands that we become literate in how to interpret data, what assumptions and processes are necessary to analyze data, as well as how we might participate in generating our own analyses and presentations of data. Consequently, data analytics is an emerging field with skills applicable to a wide variety of disciplines. This course introduces analysis, computation, and presentation concerns through the investigation of data driven puzzles in wide array of fields – political, economic, historical, social, biological, and others. No previous experience is required.

By the end of the course, you should be able to:

  • Identify, describe, and use different formats of data and data sources in class discussion and during lab projects
  • Collect, clean, store, and extract data needed for an analysis during lab projects
  • Write basic computer programs using RStudio for a reproducible data analysis workflow
  • Create data visualizations and extract and interpret meaning from the visual information
  • Perform statistical analysis on a dataset, and interpret the results
  • Reflect and evaluate on ethical, social, and legal issues in data collection, analysis, and security in discussion and in class projects using real datasets
  • Communicate and interpret all aspects of data analysis (data, cleaning, analysis, results) to a diverse, technical or non-technical audience, in oral, visual, and written format
  • Synthesize the above skills to create & present a new, independent data analysis project

Office Hours

Note that I am not planning to do "in-person" office hours this semester. I am using Google Calendar for virtual office hours, by appointment. If you go to my appointment page, you will see a real-time account of when I am available. My standard appointment slots are MW 3:30-5:00 p.m.; Th 1:00-3:00 p.m.; and F 1-2 p.m.. Note that these appointment slots will disappear once I've been booked. If I ever need to cancel office hours on a given day (say, for example, if I'm ill), I will update the calendar and email anyone with an appointment. If I find that I have more requests for appointments than I have availability, I may convert some of these slots to virtual hangouts, where anyone would be welcome to drop in, but for the moment I will hold these as opportunities for one-on-one discussion. 

If my office hours by appointment do not work for your schedule, you can also email me to request an appointment at another time. When sending me such an email (or really any email), please follow some basic conventions of formality and politeness. There's no need to construct the equivalent of a business letter, but please don't begin your message with "hey," and please take an extra moment to make sure you spelled my name correctly. I promise to show you the same courtesy. I will do my best to reply within 48 hours, barring any emergency circumstances.

Additional Norms and Policies

Here you will find information on required readings, import university policies, and course-specific policies like attendance and cell phone use.

Required Texts

R for Data Science (Wickham and Grolemund), ISBN-13: 978-1491910399
Free online (http://r4ds.had.co.nz) or order print edition online by matching the ISBN
Additional selected readings will be made available as html or pdf, and linked to the course website or shared via Notebowl


All projects in this course will be scripted and analyzed using R, an open source data analysis language and environment. No previous experience with R, statistical software packages, or computer programming is required. Specifically, we will be using RStudio as our programming environment. Instructions for installing R and R Studio will be posted to the calendar below.

Grading and Feedback

Since there are multiple sections of DA 101 every semester, the various instructors work hard to make sure there is approximate parity in terms of content, workload, and expectations. However, it is also true that each professor has their strengths, areas of interest, and priorities, and I'm sure I'm no exception. We'll have to spend some time together for you to get a real sense of what I value, how I grade, etc., but I look forward to that process, and to getting to know you all better more generally. One of the big advantages of a school like Denison is that, if you want to work with me again, you'll probably be able to, whether in another data analytics course, a summer research fellowship, or some other capacity. 

As a general rule, the expectations in this course are high, and I'm confident you can all do great work. The feedback I provide on assignments is designed to help you get there. My goal is to provide specific, relevant, and honest feedback when I grade your work. This will include constructive criticism, strategies for improvement, and guidance on how students can achieve success. I will not do "compliment sandwiches" just to begin and end on a positive remark, but this means that, when I praise your work, it's an honest (and I think more meaningful) act of praise. 

Regarding the major assignment rubric, it is adapted from the standards that the data analytics program uses for all its majors. I don't expect your work to meet the same standards as a graduating senior, but I think using the same categories on our rubric will give you a better idea of what it might mean to major in data analytics, as many of you are hoping to do. If you don't want to be a data analytics major, these criteria are still highly relevant to almost any program of study. 

Major Assignment Rubric

Item Description
Assignment Process: All materials are turned in on time and in the right place. Assignment directions are followed. Required components are all present and submitted on time.
Attention to Detail: The project is well organized, flows logically, and follows the all formatting guidelines, including attention to proofreading, proper citations, and language that is appropriate to a well-informed, non-technical reader.
Research Question and Research Design: The project has a focused and well defined research question that can be addressed with computational, data-driven analysis. The focal data set and method(s) are appropriate for the research question.
Data, Visuals, and Code: The data are fully described, properly sourced, and presented in appropriate ways. Visuals (tables, charts, graphs) are used effectively to describe multiple aspects of the research project (data, methods, or results). The paper provides sufficient details and/or points to supplementary materials that make the research reproducible by a technical reader (i.e, detailed footnotes, appendices, GitHub, code, etc.)
Data Analysis Methods: The method(s) used to test the research question is justified, validated, and applied appropriately; the student appropriately describes the strengths and weaknesses of the methods used; outside sources are used to justify how the methods are used and interpreted.
Reporting and Interpretation of Results: The results are interpreted correctly and clearly address the research question; the project discusses its limitations, the extent to which it can be generalized, and expansion to further research.
Ethical Considerations: The writing thoughtfully engages any ethical considerations of using the data, methods, and implications of communicating the findings.

Grade Breakdown

Item Percentage Comments
Oral Presentation 5 Individual assignment
Quizzes 10 Individual assignments
Data and Code Ledger 15 Team-based assignments
Lab Work and Lab Reports 40 Team-based assignments
Final Project 30 Individual assignment. Four separate components (see assignment description)

The Quantitative GE Requirement

The goal of the quantitative reasoning requirement is to develop the skills of all students in the descriptive, analytical, and predictive aspects of quantitative reasoning. A course fulfilling this requirement must utilize numerical quantities and employ, as an integral and sustained part of the course, at least one of the following forms of quantitative reasoning.

  1. the application of mathematical models to describe or predict the behavior of systems, and the design, construction, and interpretation of graphical representations of mathematical models.
  2. the utilization, numerical analysis, and interpretation of the significance and limitations of data to answer questions, test hypotheses, or solve problems, and the design, construction, and interpretation of graphical representations of numerical data.

Extensions Policy

Retroactive and last-minute extensions will not be granted. At the same time, life happens. Sometimes something just isn’t going to get done. If you speak to me at least a week ahead of time and I approve an extension, I will consider assigning a new due date and hold you to it. The trade off is that work turned in this way is probably not going end up in my hand when I grade everything else, so it’s going to get less feedback. If you miss a deadline entirely without getting an extension, you will automatically receive a 0 for your grade.


Cell phones should be off and put away. Laptops are okay for notes and such, but you should not be messaging, using Facebook, etc. I’ll check screens regularly give you a verbal warning on your first offense. After that, I reserve the right to ask you to leave class and mark you absent if you are creating a distraction.

Being Prepared for Class

Coming to class prepared means that you have the day's reading in hand (printed or digital) and have come to class with a way to take notes (printed or digital). If you are not prepared for class, I reserve the right to grade as if you were absent for that day. Anything due on a given day is due at the start of class. Any digital submission of material is due by the time class starts on the day the hard copy is due. These policies apply for in-person and remote participation. 

Transitional and Remote Learning Policy

This class has a transitional learning policy, which means that we will conduct class over Zoom for the first several weeks (up to four weeks but not more than that) and then convert to an in-person course for all those willing and able to attend in person. Our section of DA 101 will have student participants in various locations and time zones. We can also expect that one or more students may need to miss class because of illness or quarantine protocol. As a result, there will be a no-permission-needed policy of allowing students to participate remotely. This section of DA 101 and its lab, however, will require synchronous participation. That is, all students, regardless of time zone, will be expected to log in live for class and lab. If this expectation will not work with your schedule, there are two additional sections of DA 101 that may be a better option. I also ask that you keep me informed and meet with me and/or our TA, as needed, in order to keep apace with the course work. Note, however, that Denison's university-wide attendance policy still applies. This means, among other things, that if a class is missed, for any reason, the student is responsible for determining what occurred in the missed class. Additionally, absence from a class will not be accepted as an excuse for not knowing class material.

If you need to participate in any particular class remotely but synchronously, you can do so by joining the remote feed for our course, which will be password protected but online for every class period. (Our lab will be completely remote and conducted in the same way.) If you need to participate in a particular class remotely and asynchronously, you will be able to access a video recording of the day's Zoom broadcast via Notebowl. Watching videos rather than attending class should be considered an option of last resort, and active engagement and participation are expected. You should also look at the daily calendar and complete any readings, quizzes, homework, lab reports, etc. If you are missing a team-based assignment, you should coordinate your participation with your teammates. To get access to any lectures, or to make up a peer review, you should email me about whether to make a virtual appointment with me or our TA.

If all classes, at some point in the term, are forced to switch entirely to remote learning, I will provide detailed instructions on how to complete all the remaining assignments.

Disability Resources

If you are a student who feels you may need an accommodation based on the impact of a disability, you should contact me privately as soon as possible to discuss your specific needs. I rely on the Academic Resource Center in 020 Higley Hall to verify the need for reasonable accommodations based on documentation on file in that office.

Academic Integrity

Proposed and developed by Denison students, passed unanimously by DCGA and Denison’s faculty, the Code of Academic Integrity requires that instructors notify the Associate Provost of cases of academic dishonesty. Cases are typically heard by the Academic Integrity Board, which determines whether a violation has occurred, and, if so, its severity and the sanctions. In some circumstances the case may be handled through an Administrative Resolution Procedure. Further, the code makes students responsible for promoting a culture of integrity on campus and acting in instances in which integrity is violated.

Academic honesty, the cornerstone of teaching and learning, lays the foundation for lifelong integrity. Academic dishonesty is intellectual theft. It includes, but is not limited to, providing or receiving assistance in a manner not authorized by the instructor in the creation of work to be submitted for evaluation. This standard applies to all work ranging from daily homework assignments to major exams. Students must clearly cite any sources consulted--not merely for quoted phrases, but also for ideas and information that are not common knowledge. Neither ignorance nor carelessness is an acceptable defense incases of plagiarism. It is the student’s responsibility to follow the appropriate format for citations. Students should ask their instructors for assistance in determining what sorts of materials and assistance are appropriate for assignments and for guidance in citing such materials clearly.

Our Commitment to Liberal Arts Education

Denison's mission statement articulates an explicit commitment to liberal arts education. It emphasizes active learning, which defines students as active participants in the leaning process, not passive recipients. Denison seeks to foster self-determination and to demonstrate the transformative power of education. A crucial aspect of this approach is what Denison's mission statement refers to as "a concern for the whole person," which is why the university provides a "living-learning environment" based on individual needs and an overriding concern for community. This community is based on "a firm belief in human dignity and compassion unlimited by cultural, racial, sexual, religious or economic barriers, and directed toward an engagement with the central issues of our time."

In this class, we will discuss inequality directly. In many cases, you will asked to apply quantitative reasoning skills to these subject, which can be difficult because there is always the potential for the available data to complicate or contradict something you may feel very passionate about. In these cases, you should aspire to adopt an attitude of critical skepticism, i.e. wary of claims that are not supported by evidence but potentially willing to be persuaded by evidence if you find it compelling, and willing to give that evidence a fair hearing.

How we treat one another will be a cornerstone of these conversations. Denison's "Guiding Principles" speak of "a community in which individuals respect one another and their environment." Further, "each member of the community possesses a full range of rights and responsibilities. Foremost among these is a commitment to treat each other and the environment with mutual respect, tolerance, and civility." It's easy to treat someone this way when you like them and agree with their ideas, but the real challenge is treating those who differ from us with the same compassion and respect. However, I consider disruptive, deceitful, or hateful behavior to be breaches of these responsibilities. Bullying, trolling, hate speech, and harassment of any kind will not be tolerated.

Teaching Assistants

Our TA for this section of DA 101 will be posted shortly. The TA will be available by appointment to help answer questions about the course or particular assignments. His/her TA hours are also TBA. You can connect on Zoom on either day, and in person on Thursdays (in Burton D. Morgan 405). The TA's office hours will also be also posted on NoteBowl with a Zoom link and a Password. Office hours for all other DA 101 TAs will follow as well. While each TA is scheduled to work with a particular faculty instructor and attend their lab sections, our program encourages students to visit any TA during scheduled office hours, especially if their availability better fits your schedule.


Oral Presentation (5% of grade)

Each of you will be responsible for giving a PechaKucha presentation this semester. A PechaKucha is type of lightning talk where a presenter shows 20 slides for 20 seconds of commentary each (6 minutes and 40 seconds total). (More about the PechaKucha Presentation on NoteBowl.)

Quizzes (10% of grade)

This course has intermittent quizzes on material from readings and lectures. Quizzes are designed to measure how well you are integrating the material. They will generally consist of 10-20 multiple choice, fill-in-the-blank, and short answer style questions. There are four quizzes for this course, one for each quarter. Normally, I would do these in class, but in the interests of reducing hand-offs of pieces of paper, all quizzes will be online and open book, to be completed outside of class. There are also four take-home assignments, one for each quarter, and each of these counts as a quiz grade. Quizzes and take-home assignments will be conducted through Notebowl or Github Classroom. 

Data and Code Ledger (15% of grade)

The Data and Code Ledger is a living document that you and your lab teammates will assemble over the course of the semester. You will turn it in periodically in lieu of a lab report for that week. (More about the Data and Code Ledger on NoteBowl.)

Lab Work and Lab Reports (40% of grade)

This portion of your grade consists of participating in the lab and completing all lab assignments. Assignment descriptions, datasets, and starter code will be made available through Github and Github Classroom. Links to individual lab assignments can be found on the Lab Schedule below. 

Final Project (30% of course grade)

The final project is divided into four components, each due during a different quarter of the semester. Each assignment is worth 25% of your assignment grade. (More about Final Project Assignment on NoteBowl.)

Lab Schedule

Note: The lab component of this course will be conducted remotely using Zoom. Most labs require online submission of a team-based lab report, which is always due by the start of the following lab (one week later). Where noted, individual submissions are required but also due by the next lab. The Zoom link and password for labs are posted to NoteBowl. 

Lab 1: Tuesday, February 2, 2021

Introduction to R, RStudio, Executing .r files, The File System, and File Paths

Due Next Week: Individual Submission

Lab 2: Tuesday, February 9, 2021

Invasive Species Part 1

Due Next Week: Individual Submission

Lab 3: Tuesday, February 16, 2021

Collecting and Representing Data

Due Next Week: Team Submission

Lab 4: Tuesday, February 23, 2021

Book Reviews as Data

Due Next Week: Turn in Data and Code Ledgers (Individual Submission)

Lab 5: Tuesday, March 2, 2021

Invasive Species Part 2

Due Next Week: Team Lab Report

Lab 6: Tuesday, March 9, 2021

Substance Use

Due Next Week: No homework

Lab 7: Tuesday, March 16, 2021

No Lab This Week. Enjoy Your Day Off!

Due Next Week: Turn in Data and Code Ledgers (Individual Submission)

Lab 8: Tuesday, March 23, 2021

Audio Books

Due Next Week: Team Lab Report

Lab 9: Tuesday, March 30, 2021

Political Polarization Part 1

Due Next Week: No Homework

Lab 10: Tuesday, April 6, 2021

Political Polarization Part 2

Due Next Week: Team Lab Report

Lab 11: Tuesday, April 13, 2021

Examples of Reproducible Code

Due Next Week: Turn in Data and Code Ledgers

Lab 12: Tuesday, April 20, 2021

Authorship Attribution

Due Next Week: Team Lab Report

Lab 13: Tuesday, April 27, 2021

Oral Presentations for Final Projects

Due Next Week: Complete IRB Training

Lab 14: Tuesday, May 4, 2021

Oral Presentations for Final Projects

Summary of Due Dates

Due Date Assignment
Monday, February 01, 2021 Sign up for Github, make Google Drive Folder, Complete Course Survey
Tuesday, February 9, 2021 Individual Lab Report
Wednesday, February 10, 2021 Take-home assignment 1
Monday, February 15, 2021 Quiz 1
Tuesday, February 16, 2021 Individual Lab Report
Tuesday, February 23, 2021 Team Lab Report
Monday, March 1, 2021 Bring Draft of Final Project Topic Exploration Assignment to Class
Tuesday, March 2, 2021 Turn in Data and Code Ledgers (Individual Submission)
Friday, March 5, 2021 Turn in Revised Final Project Topic Exploration Assignment
Monday, March 8, 2021 Quiz 2
Tuesday, March 9, 2021 Team Lab Report
Wednesday, March 10, 2021 Take-home assignment 2
Monday, March 15, 2021 Mid-term Evaluation and Progress Report
Monday, March 22, 2021 Bring Draft of Final Project Project Plan Assignment to Class
Tuesday, March 23, 2021 Turn in Data and Code Ledgers (Individual Submission)
Monday, March 29, 2021 Turn in Revised Final Project Project Plan Assignment
Wednesday, March 31, 2021 Take-Home Assignment 3
Thursday, April 1, 2021 Team Lab Report
Wednesday, April 7, 2021 Bring Draft of Final Project Visualization Component to Class
Monday, April 11, 2021 Turn in Revised Final Project Visualization Component
Thursday, April 15, 2021 Team Lab Report
Thursday, April 22, 2021 Turn in Data and Code Ledgers (Individual Submission)
Wednesday, April 28, 2021 Take-home assignment
Thursday, April 29, 2021 Team Lab Report
Monday, May 3, 2021 Bring First Draft of Final Project Written Analysis to Class
Wednesday, May 5, 2021 Class time set aside for course evaluations
Thursday, May 6, 2021 Complete IRB Training and Submit Certificate of Completion on Notebowl
Monday, May 17, 2021 Complete and Submit Final Version of Written Analysis and Reflection via Github

Weekly Calendar

Quarter 1: Introduction to Data Analytics

Week 1: Data Literacy

Monday, February 01, 2021

In Class: Introductions

Homework: Sign up for Github, make Google Drive Folder, Complete Course Survey

Wednesday, February 03, 2021

In Class: Discuss survey results

Homework: Watch Three TED Talks ... Stacy Smith, The Data Behind Hollywood's Sexism (15:36); JP Rangaswami, Information is Food (7:48); John Wilbanks, Let's Pool Our Medical Data (16:11)

Friday, February 05, 2021

In Class: The Promise of Data

Homework: Read R for Data Science "1. Introduction" (to the book) and "2. Introduction" (to the "Explore" section)

Week 2: Data, Metadata and Quantification

Monday, February 08, 2021

In Class: Lecture on Data and Data Analysis

Homework: Take-home assignment (counts as quiz grade)

Wednesday, February 10, 2021

In Class: Data vs. Metadata; The Data Lifecycle

Homework: Read "Chapter 4: Field of Ignorance" from Moneyball (pdf on Notebowl)

Friday, February 12, 2021

In Class: The Problems of Quantification

Homework: complete Quiz 1: Data Literacy, Metadata, and the Data Lifecycle (on Notebowl)

Week 3: Working with Data

Monday, February 15, 2021

In Class: Review of R, RStudio, and concepts so far

Homework: Read R for Data Science "5. Data transformation"

Wednesday, February 17, 2021

In Class: Transforming Data

Homework: Read Richard Jean So, "All Models Are Wrong" (pdf on Notebowl)

Friday, February 19, 2021

In Class: All Models Are Wrong. Some Models Are Useful.

Homework: Read R for Data Science "3. Data visualization"

Week 4: Data Visualization

Monday, February 22, 2021

In Class: Review of data visualization in R (ggplot2), common visualization types

Homework: Watch Hans Rosling, TED Talk, "The Best Stats You've Ever Seen"; Watch David McCandless, "The Beauty of Data Visualization"

Wednesday, February 24, 2021

In Class: Storytelling with Data

Homework: Read Cairo, The Truthful Art, 41-65 (pdf on Notebowl)

Friday, February 26, 2021

In Class: The Five Qualities of Great Visualizations

Homework: Complete the Topic Exploration Assignment. Submit on Notebowl and Bring a Laptop to Next Class

Quarter 2: Data and Communication

Week 5: Descriptive Statistics 1

Monday, March 01, 2021

In Class: Peer Feedback on Topic Exploration Assignment (final version due Friday at 11:59 p.m.)

Homework: Read R for Data Science "7. Exploratory Data Analysis"

Wednesday, March 03, 2021

In Class: Using data to generate questions and pursue insight

Homework: Read "Introduction: the Hidden Side of Everything", from Freakonomics (pdf on Notebowl)

Friday, March 05, 2021

In Class: Norms, trends, individuals, and outliers

Homework: Complete Quiz 2: Working with Data, Data Visualization, Sampling, and Populations (on Notebowl)

Week 6: Descriptive Statistics 2

Monday, March 08, 2021

In Class: Distributions, sampling, central limit theorem

Homework: Take-home assignment (counts as quiz grade)

Wednesday, March 10, 2021

In Class: Descriptive Statistics Continued, Shapiro-Wilk Test

Homework: Cathy O'Neil, "Arms Race: Going to College" from Weapons of Math Destruction (pdf on Notebowl)

Friday, March 12, 2021

In Class: Overconfidence

Homework: Mid-term Evaluation and Progress Report

Week 7: Statistical Significance

Monday, March 15, 2021

In Class: Review of Key Concepts; Discuss Project Plan Assignment

Homework for Friday: Duncan Watts, from "The Dream of Prediction," Everything is Obvious (pdf on Notebowl)

Reminder: No Lab This Week, and No Class on Wednesday.

Wednesday, March 17, 2021

No Class Today. Enjoy Your Time Off!

Friday, March 19, 2021

In Class: What is Probability?

Homework: Complete the Project Plan Assignment. Submit via Google Drive folder and Bring a Laptop to Next Class

Quarter 3: Data Analysis

Week 8: Predictive Analytics 1

Monday, March 22, 2021

In Class: T-Tests, P-Values; Peer Review of Project Plan Assignment (final version due Friday at 11:59 p.m.)

Homework: Sign up for oral presentations (link to sign-up sheet on NoteBowl)

Wednesday, March 24, 2021

In Class: Confidence Intervals, Effect Size

Homework: Read Daniel Kahneman, "The Illusion of Understanding," "The Illusion of Validity," and "Intuitions vs. Formulas" Thinking Fast and Slow (pdf on Notebowl)

Friday, March 26, 2021

In Class: Problems of Intuition, How and When Quantification Fails

Homework: Submit final version of Project Plan Assignment by Monday at 11:30 a.m.

Week 9: Predictive Analytics 2

Monday, March 29, 2021

In Class: Logistic and Linear Regression

Homework: Complete take-home assignment 3 (counts as quiz grade)

Wednesday, March 31, 2021

In Class: Logistic and Linear Regression Continued

Homework: Read Gebru et. al., "Datasheets for Datasets" (online); Complete team lab report

Friday, April 02, 2021

In Class: Data Ethics and Machine Learning

Homework: Watch Alex Edmans, TEDx Talk, What to Trust in a Post-Truth World (17:47)

Week 10: Bayesian Analytics

Monday, April 05, 2021

In Class: Bayes Theorem and Bayesian Data Analysis

Homework: Complete Final Project Visualization Component. Submit via Google Drive folder and Bring a Laptop to Next Class. (Final version due Monday at 11:30 a.m.)

Wednesday, April 07, 2021

In Class: Peer Review of Final Project Visualization Component (Final version due Monday at 11:30 a.m.)

Homework: Read Nate Silver, "Less and Less Wrong," from The Signal and the Noise (Penguin, 2012) 221-247 (pdf on Notebowl)

Friday, April 09, 2021

In Class: How to Think Like a Bayesian

Homework: Read William Stafford Noble "A Quick Guide to Organizing Computational Biology Projects"; "The importance of local context in COVID-19 models"; "Data curation during a pandemic and lessons learned from COVID-19" (these are short but may be difficult to read, so plan accordingly)

Quarter 4: Case Studies and Student Projects

Week 11: Case Studies 1, Computational Biology

Monday, April 12, 2021

In Class: Organizing Computational Projects

Homework: None

Wednesday, April 14, 2021

In Class: Computational Biology, Data Curation and Modeling in Various Disciplines

Homework: Read D'Ignazio and Klein, "What Gets Counted Counts", Data Feminism, Cambridge: MIT Press, 2020: Open Access Edition Online. https://data-feminism.mitpress.mit.edu/pub/h1w0nbqp/release/2?readingCollection=0cd867ef

Friday, April 16, 2021

In Class: What is Data Feminism?

Homework for Tuesday: Read José Nilo G. Binongo "Who Wrote the 15th Book of Oz? An Application of Multivariate Analysis to Authorship Attribution" (pdf on Notebowl)

Reminder: No Class on Monday.

Week 12: Case Studies 2, Authorship Attribution

Monday, April 19, 2021

No Class Today. Enjoy Your Time Off!

Wednesday, April 21, 2021

In Class: In Class: Authorship attribution methods

Homework: Read Andrew Piper, "There Will Be Numbers"

Friday, April 23, 2021

In Class: Can culture be analyzed quantitatively?

Homework: Read Harris et. al., "Two Failures to Replicate High-Performance-Goal Priming Effects" (pdf on Notebowl)

Week 13: Case Studies 3, Priming Effects

Monday, April 26, 2021

In Class: Priming Effects and the Replication Crisis

Homework: Take-home assignment (counts as quiz grade)

Wednesday, April 28, 2021

In Class: Type I errors, p-hacking, the curse of dimensionality, the file drawer problem

Homework: Read Longino, "Values and Objectivity," from Science as Social Knowledge (pdf on Notebowl)

Friday, April 30, 2021

In Class: What is scientific objectivity?

Homework: Complete First Draft of Final Project Written Analysis. Submit via Google Drive folder and Bring a Laptop to Next Class

Week 14: Case Studies 4, Gender Prediction

Monday, May 03, 2021

In Class: Peer Feedback on Topic Exploration Assignment ... revise and turn in final drafts by May 12, 2021

Homework: Read Helena Mihaljevic et. al., "Reflections on Gender Analyses of Bibliographic Corpora" Frontiers in Big Data 2 (August 28, 2019): 29

Wednesday, May 05, 2021

In Class: Gender bias and questioning binarism

Homework: Complete Course Evaluations

Friday, May 07, 2021

In Class: Data Analytics at Denison; Moving Forward as a Student and a Human

Homework: Complete Final Projects

Week 15: Exam Week

Monday, May 17, 2021

Homework: Complete and Submit Final Version of Final Projects (Written Analysis and Reflection) via Github by 2 p.m. Eastern Time