Syllabus

Authors
Affiliation

About the Course

Instructor

NoteAbout Me

Student hours:

  • Mondays from 2:00 pm – 3:30 pm ET in McConnell 213
  • Thursdays from 10:00 am – 11:30 am ET in McConnell 213
  • by appointment:
    • either in McConnell 213
    • or via Zoom

Description

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data.

Note

Restrictions: Students do not normally earn credit for more than one course on this list: ECO 220, GOV 203, MTH 220, PSY 201, SDS 201, SDS 210, SDS 220 or SOC 204. Enrollment limited to 40.

Corequisite

  • SDS 100 (Students who have completed SDS 100 in a previous semester need not repeat it.)

Learning goals

By the end of this course, you will be able to:

  • translate a research problem into a set of questions that can be answered with data
  • distinguish between observational studies and randomized experiments, and identify the strengths and limitations of each study design
  • distinguish between the population, sample, parameter, and statistic for a given study, and critically analyze whether the reported results reasonably follow from the study and analyses conducted
  • explore data using graphical displays and numerical summaries, and interpret what those graphs and summaries do and do not reveal
  • determine, for a given research question, whether it is best addressed by a descriptive analysis, a linear regression model, a hypothesis test, or a confidence interval and appropriately conduct the selected analysis
  • understand and interpret/contextualize the meaning of confidence intervals and p-values
  • consider the ethical implications of study design and analysis decisions
  • effectively communicate statistical ideas and results, both verbally and in writing

Textbooks

Required

Supplemental resources

  • Posit Workbench is an interactive web application framework for R. Available for free via our Posit Connect Server.
  • IMS Tutorials: The course textbook is accompanied by a series of online tutorials that provide hands-on practice and review of the course content. You may complete these tutorials outside of class.

SDS 100

SDS 100 is an important support mechanism for all introductory statistics and data sciences courses at Smith. It provides an introduction to the computational tools that you will need to be successful in SDS 210 and other SDS courses: R, RStudio, and Quarto. You will learn how to navigate modern data science workflows; utilize open-source software; author scientific reports that integrate data, text, graphics, and other media; and reason about ethical practices in statistics and data science. If SDS 210 is your first course within SDS, please make sure that you are also concurrently enrolled in SDS 100!

Classes

Classes meets on Mondays, Wednesdays, and Fridays from 10:50–12:05 in Seelye 301.

Evaluation

Expectations

This is a 4 credit course, meaning that by federal guidelines, it should consume about 12 hours per week of your time. We meet for 3.75 hours per week. That means you should be spending about 8.25 hours per week, or more than 1 hour per day, on this course outside of class.

Warning

You should be spending about 8.25 hours per week on this course outside of class.

Figure 1 illustrates what this level of engagement might look like:

Time Sun Mon Tue Wed Thu Fri Sat
Morning Attend class for 75 minutes Attend class for 75 minutes Office hours for 15 minutes Attend class for 75 minutes Rest
Afternoon Read/study for 1 hour Office hours for 15 minutes Homework for 1 hour Homework for 1 hour Rest
Evening Read/study for 1 hour Read/study for 45 minutes Read/study for 1 hour Read/study for 45 minutes Homework for 2 hours 🎉 🎊
Total (minutes) 120 135 120 120 135 90 0
Figure 1: Sample weekly schedule for 12 hours of academically engaged time.

Grading & Assignments

Your achievement of the course learning goals will be evaluated through the following:

  • Knowledge Checks (10%): On Fridays, we will have a short, one question, written knowledge check during the middle of class. These problems are designed to take just a couple of minutes, and will help to prepare you for the written exams. The only grades available on the Knowledge Checks are 100, 85, and 50, and the lowest three grades will be dropped. There will be no make-up knowledge checks!
  • Homework (15%): Homework will be assigned on a weekly basis and will generally be due on Fridays at 11:59 p.m. All assignments should be completed using Quarto and submitted online via Moodle. These assignments will be graded for completeness and correctness, so make sure to make an effort on every problem. Your lowest homework grade will be dropped.
  • Exams (45%): There will be three self-scheduled, closed-book exams. For each, you will be allowed to bring one single-sided sheet of handwritten notes as well as a scientific calculator. You may not use your phone or laptop.
  • Project (20%): You will work in groups of three to conduct a statistical study on a topic of your choice. This project will require you to (a) write a study proposal, (b) appropriately visualize and analyze data in order to answer your study question, (c) present your results orally to the instructor, and (d) submit a written report describing your study and its findings. Detailed instructions for this project will be given out in class, and project checkpoints will occur throughout the semester.
  • Engagement (10%): Attending class meetings, being active on the course Slack channel, participating in class discussions, and offering ideas and questions in class or on Slack are all ways of demonstrating engagement. Your engagement grade will be determined by me in consultation with periodic self-assessments completed by you.

Extensions

I value your ability to meet deadlines and manage your own workload. I am also a reasonable person who understands that life happens and this is not always possible. Extensions up to 48 hours will typically be granted when requested at least 48 hours in advance without requiring a reason or an explanation. Longer extensions, or those requested within 48 hours of a deadline, will typically not be granted. Please plan accordingly. Please note that because many of the assignments in this class are collaborative, individual extensions for group assignments will be problematic. All extended deadlines will appear on Moodle.

Academic Integrity

I expect that you will maintain your academic integrity in this class. Please read Smith’s Academic Integrity Statement. Please pay particular attention to this sentence in the “Examples of Violations” section:

If you are finding ways to avoid the “thinking” component of your coursework, you should stop to ask yourself whether you are compromising your academic integrity.

Policies

Inclusion

I am committed to fostering a classroom environment where all students thrive. I am committed to affirming the identities, realities and voices of all students, especially those from historically marginalized or underrepresented backgrounds. I am dedicated to creating a space where everyone in the class is respected, is free from discrimination based on race, ethnicity, sexual orientation, religion, gender identity, disability status, and other identities, and feel welcome and ready to learn at your highest potential.

If you have any concerns or suggestions for how to make this class more inclusive, please reach out to me. I am here to support your learning and growth as data scientists and people!

Accommodations

Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Accessibility Resource Center (ARC) at the beginning of the semester. To contact ARC, please email .

Attendance

You choose whether you will attend class. If you choose to attend, I expect your full attention. If you choose not to attend, you accept responsibility for any lost educational value.

Warning

We hope it goes without saying that during class, you should not use your computer or cell phone for personal email, web browsing, chatting with AI, social media, or any activity that’s not related to the class.

Collaboration

Much of this course will operate on a collaborative basis, and you are expected and encouraged to work together with a partner or in small groups to study, complete assignments, and work on projects. However, all work that you submit for credit must be your own.

Important

Copying and pasting sentences, paragraphs, or blocks of code from another student or from online sources is not acceptable and will receive no credit.

No interaction with anyone but the instructors is allowed on any exams or quizzes. Violations will be reported to the Academic Integrity Board.

Generative AI

Generative AI and Academic Integrity

Please read the Smith Academic Integrity Board’s statement on Generative Artificial Intelligence & Your Academic Integrity

I draw your attention to the following excerpt:

Tip

Any time you are using AI in a way that is substituting for the “thinking work” that you should be doing for a course, you should stop.

My perspective on AI and your learning

My goal is to help you achieve the learning goals for this course using only the mental model you have built of the material we have covered, and without the aid of generative AI. While I accept the ubiquity of generative AI, I believe that helping you build your mental model of this material is where I can best contribute to your education. To that end, much of our time in class and many of our assessments will take place in AI-free environments. Other learning will take place outside of class, wherein you are free to use AI in whatever fashion you want (unless otherwise noted), provided that it is in compliance with Smith’s Academic Integrity policies.

Please understand that while AI may be helpful to you in building your mental model of the material, it will not be available to you during many of our assessments, and I am comparatively less interested in your ability to complete tasks while using AI than I am in your ability to demonstrate knowledge using only your brain (and body).

This perspective applies to all course content, including mathematical equations and R code.

Usage

Important
  • Use of generative AI is expressly prohibited on exams and oral presentations.
  • Use of generative AI is generally prohibited inside the classroom, although there may be exceptions.
  • Unless otherwise noted (such as the above), generative AI can be used whenever you are outside of class.

Please read my perspective on learning and generative AI. Please understand that careless and excessive use of generative AI will likely impede your ability to achieve the course learning goals.

Warning

Remember that generative AI is not intelligent, doesn’t think, and has no idea what is true or false. You are solely responsible for the veracity of anything (e.g., code or text) you submit.

Code of Conduct

As the instructor and assistants for this course, we are committed to making participation in this course a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. Examples of unacceptable behavior by participants in this course include the use of sexual language or imagery, derogatory comments or personal attacks, deliberate misgendering or use of “dead” names, trolling, public or private harassment, insults, or other unprofessional conduct.

As the instructor and assistants we have the right and responsibility to point out and stop behavior that is not aligned to this Code of Conduct. Participants who do not follow the Code of Conduct may be reprimanded for such behavior. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the instructor.

Important

All students, the instructor, the lab instructor, and all assistants are expected to adhere to this Code of Conduct in all settings for this course: lectures, labs, office hours, tutoring hours, and over Slack.

This Code of Conduct is adapted from the Contributor Covenant, version 1.0.0, available here.

Resources

Moodle and course website

The course website and Moodle will be updated regularly with lecture handouts, project information, assignments, and other course resources. Homework and projected grades will be submitted to Moodle. Please check both regularly.

Computing

The use of the R statistical computing environment with the RStudio interface is thoroughly integrated into the course. You have two options for using RStudio:

  • The server version of Posit Workbench on the web. The advantage of using the server version is that all of your work will be stored in the cloud, where it is automatically saved and backed up. This means that you can access your work from any computer with a web browser (Firefox is recommended) and an Internet connection.
  • A desktop version of RStudio IDE installed on your machine. The downside to this approach is that your work is only stored locally, and you will have to manage your own installation.

Note that you do not have to choose one or the other – you may use both. However, it is important that you understand the distinction so that you can keep track of your work. Both R and RStudio are free and open-source, and are installed on most computer labs on campus.

Unless otherwise noted, you should assume that it will be helpful to bring a laptop to class. It is not required, but since there are no workstations in the classroom, we will need a critical mass (i.e. at least 12) computers in the classroom pretty much everyday.

Communication

  • Slack is the primary forum for course-related discussions of all kinds. Please do not email me with course-related questions! Instead, post those #questions on Slack. If discretion is absolutely necessary, private message me on Slack.
Important

It is very important that all project-related communication take place in Slack channels that all group members can see! Private texts and side conversations will very quickly lead to other group members feeling excluded.

Writing

Your ability to communicate results—which may be technical in nature—to your audience – which is likely to be non-technical—is critical to your success as a data scientist. The assignments in this class will place an emphasis on the clarity of your writing.

This course is part of Smith College’s Writing Enriched Curriculum. As such, the course supports the Writing Plan of the Program in Statistical & Data Sciences.

Please read the SDS Writing Plan for more information.

The Spinelli Center

The Spinelli Center (Seelye 207) supports students doing quantitative work across the curriculum. In particular, they employ:

  • SDS Tutors available from 7:00–9:00pm on Sunday–Thursday evenings in Sabin-Reed 301. These students are trained to help you with your statistics and R questions.
  • A Data Research and Statistics Counselor (Cameron) who keeps both drop-in hours and appointments. Students are welcome to email to make an appointment.

Your fellow students are also an excellent source for explanations, tips, etc.

Tentative Schedule

The following outline gives a basic description of the course. Please see the detailed schedule for more specific information about readings and assignments.

Week Topic Reading
1 Introduction to data Ch. 1–3
2 Exploratory data analysis Ch. 4–6
3 Simple linear regression Ch. 7
4 Multiple regression Ch. 8.1–8.4
5 Review, exam, Project (Phase I)
6 Probability ?
7 Hypothesis testing and confidence intervals Ch. 11–12
8 Mathematical inference and decision errors Ch. 13–14
9 Review, exam, Project (Phase II)
10 Inference for proportions Ch. 16–18
11 Inference for means Ch. 19–22
12 Inference for regression Ch. 24–25
13 Project (Phase III)