BC Library: http://www.brooklyn.cuny.edu/web/academics/library.php

The top priority for this class, as across the entire sociology curriculum, is to help you understand what you want your education to be. We can begin with a general question about areas of knowledge and skills and discuss the connections between what we do in this class and what you want to do during and after your time at BC. (We sometimes talk about this at CUNY as “career competencies” but we can frame it more broadly than that.) To achieve this priority, we will follow an ethic of care: I will help you figure out a way to fit this course into your other commitments (other classes and other responsibilities in life) and I ask that you commit to helping your classmates achieve their learning goals. We’re in this together.

We will consider together the logic of data analysis in pursuit of a sociological question and that will give us an opportunity to examine several different kinds of quantitative analytical techniques. Together, we will define a sociological question for the semester and then undertake a series of tasks, similar to what a professional researcher would do, to find data, prepare the data, plan an analysis, describe findings, and discuss the sociological insight of the analysis. For example, we might ask, “What factors explain prejudice?” We could then evaluate different sources of survey data that measure prejudice which would allow us to build and test a model to explain the causes of prejudice.

Having found a data source, we will learn some techniques for preparing the dataset for analysis. In this course, we’ll use an open source statistical programming language, Python, to do data management and analysis. Python is free and you can install it on your own computer (Windows, Mac, Linux) if you wish. In class, we’ll use Python online using Google’s Colaboratory. I will provide a collection of Colab notebooks that you can use as a reference for the different data management and analysis tasks. You will submit your lab assignments to me in Colab as well, and we’ll use the notebooks together in class as we work on the tasks associated with our shared research question.

As we examine a specific research task, we’ll work on it together in class. We’ll discuss some of the factors that should inform our decisions related to each task. We’ll look at examples of the task, including Python code to carry out the task. In class meetings we’ll work together on activities related to these tasks. Then, you have the opportunity to demonstrate your learning in lab assignments connected to these tasks.

When we get to data analysis, we’ll plan and carry out an analysis together. I will ask each of you to identify a follow-up question to ask based on our shared analysis. This follow-up question will be the basis for your individual project in the course, which will serve as the final exam. The project will incorporate a discussion of some relevant literature related to your follow-up question. You can choose to prepare the project as a brief research paper (about 5,000 words including abstract and references) or a conference poster presentation.

To aid us in this endeavor, we’ll use a variety of no-cost materials. As a general resource, we’ll use the following textbook: Allen B. Downey, Think Bayes 2. The author of the textbook has made the Python notebooks for the class available for us to use, along with notebooks that I have written for us. We’ll also use resources to learn Python that are available for free on the web.

In thinking about the learning objectives for this course, we want to consider the skills that will help you after your time at the college. That path might lead to graduate or professional school, or it might lead to the labor market. But in either case, the work we do is directly connected to what admissions committees and employers say that they value. So, for example, when we work on Python code to compute a percentage table, you will be learning material related to an assignment in class, but more than that, you will be practicing career competencies, including critical thinking, technology use, and teamwork.

Learning objectives (career competencies)
By the end of the semester, you will be able to:
* Identify a meaningful sociological research question (critical thinking);
* Explain the relationship between different kinds of sociological questions and data analysis practices (critical thinking, technology, effective communication);
* Write code for introductory data analysis using Python (quantitative reasoning, technology, teamwork);
* Compute various descriptive and inferential statistics relating to the linear model, and interpret the results (quantitative reasoning, technology);
* Assess sociological arguments in terms of research results (critical thinking); and,
* Interpret empirical evidence from sociological research articles (critical thinking, effective communication).

Organization of the course:
* Class meetings will be a mixture of lecture presentation and hands-on activities.
* Be prepared to participate in discussions. You should complete the assigned readings prior to the class meeting in which they will be discussed.
* Be prepared to participate in group activities. A good way to learn is to teach, and we will often engage in exercises that require you to explain material to each other.
* Out of respect for your classmates, please practice appropriate classroom decorum. We all have different learning styles and habits, but our behavior in class should not interfere with the learning of others.
* Regular attendance is expected. You will be evaluated on the basis of the work you turn in, and learning the material is much more difficult if you miss classes and assignments. If you are unable to attend class, please let me know.

Evaluation of your work
Grading will consist of:
* Ten reflection writing exercises (10 points);
* Ten class activities (10 points);
* Ten lab activities (30 points);
* A research project in which you pose a follow-up question to our shared question, perform a relevant kind of analysis, and discuss the results; this will serve as the final exam for this class (50 points).

You’ll receive credit for the reflections and class activities if you participate (submitting the reflections prior to their scheduled class and attending class for the activities). Lab activities will be graded as satisfactory/unsatisfactory. You will have an opportunity to revise unsatisfactory assignments–that is, to get the code to run correctly and interpret the results. The research project will be evaluated on a specific rubric, which we will discuss in class. I will ask you to apply the rubric to your own project and then discuss your self-evaluation with me. All work for evaluation must be completed by the end of the semester. If circumstances prevent you from being able to complete the work by the end of the semester, we can reach an agreement for a specific plan for an incomplete (INC).

Grades will be assigned according to the following scale: 100-95 = A, 94-90 = A-, 89-86 = B+, 85-82 = B, 81-80 = B-, 79-76 = C+, 75-72 = C, 71-70 = C-, 69-66 = D+, 66-62 = D, 61-60 = D-. Scores below 60 are failing.

A grade of B- or higher is required to take SOCY 5201W, the capstone seminar in the major. I will work with you throughout the semester to find a path to exceed the minimum grade requirement. If you are committed to doing the work, I will commit to making sure that you succeed.

Academic integrity
The faculty and administration of Brooklyn College support an environment free from cheating and plagiarism. Each student is responsible for being aware of what constitutes cheating and plagiarism and for avoiding both. The complete text of the CUNY Academic Integrity Policy and the Brooklyn College procedure for policy implementation can be found at www.brooklyn.cuny.edu/bc/policies. If a faculty member suspects a violation of academic integrity and, upon investigation, confirms that violation, or if the student admits the violation, the faculty member MUST report the violation. Students should be aware that faculty may use plagiarism detection software.

Student Disability Services
The Center for Student Disability Services (CSDS) is committed to ensuring students with disabilities enjoy an equal opportunity to participate at Brooklyn College. In order to receive disability-related academic accommodations, students must first be registered with CSDS. Students who have a documented disability or suspect they may have a disability are invited to schedule an interview by calling (718) 951-5538 or emailing [email protected] If you have already registered with CSDS, email [email protected] or [email protected] to ensure accommodation emails are sent to your professor.

Please consult, if needed, the college’s bereavement policy.

Sexual and Gender-based Harassment, Discrimination, and Title IX
Brooklyn College is committed to fostering a safe, equitable and productive learning environment. Students experiencing any form of prohibited discrimination or harassment on or off campus can find information about the reporting process, their rights, specific details about confidentiality, and reporting obligations of Brooklyn College employees on the Office of Diversity and Equity Programs website. All reports of sexual misconduct or discrimination should be made to Ivana Bologna, Title IX Coordinator (718.951.5000, ext. 3689), and may also be made to Public Safety (719.951.5511), the New York City Police Department (911 or a local NYPD precinct), or Michelle Vargas, Assistant Director of Judicial
Affairs, Division of Student Affairs (718.951.5352) as appropriate. The CUNY Equal Opportunity and Non-discrimination Policy and Complaint Procedures includes additional information regarding reporting discrimination and/or retaliation.

Consideration of religious observance
If you are unable to attend class or turn in an assignment because of religious holidays and related religious observances, let me know and we will arrange for alternatives.