Syllabus

An introduction to the conceptual foundations and practical skills needed to transform data into useful forms and apply predictive analytics to discover patterns and anticipate trends. Primary focus is on the core skills and concepts needed to pull data from a range of sources; to filter, transform, and combine data sets to prepare them for analysis; and to construct quantitative summaries and basic visualizations. Programming is used throughout to assemble data-processing pipelines. Students will also discuss ethical and social considerations of data collection and data-driven systems.

Prerequisite: CS 104, CS 106, or CS 108. A minimum grade of C in the chosen course is required.

Learning Outcomes

Upon successful completion of this course students will be able to:

  • Prepare tabular data for visualization and analytics by constructing generalizable workflows for loading, transforming, joining, and reshaping data.
  • Create, interpret, critique, and refine graphical and tabular visualizations of data.
  • Translate a real-world need into an appropriate analytics task, considering available data.
  • Select analytics methods appropriate to the data and task from among the approaches surveyed in the course.
  • Interpret the outcomes of selected predictive analytics techniques.
  • Communicate analytics results using reports that clearly motivate the problem and approach, elucidate the data used, and present both the strengths and shortcomings of analytics results.
  • Discuss ethical and social considerations of data collection and data-driven systems.

Additionally, using transparent reproducible workflows and regularly acknowledging limitations will help students practice virtues including integrity, humility, and justice.

Schedule

Subject to change. Updates will be made here and on Moodle.

week Topic Notes
1 (Aug 28) Intro 1 Reading: LGN 1-2
Exercise 1
2 (Sep 04) Intro 2 No Monday (Labor Day)
Reading: LGN 3-5 (selections)
Exercise 2
3 (Sep 11) Visualization Design Reading: Wilke 2-5
Quiz 1
Exercise 3
4 (Sep 18) Visualization Implementation Reading: LGN 10-11
Exercise 4
Vis Project Milestone 1
5 (Sep 25) Wrangling 1 Reading: LGN 6 (except 6.3), LGN 9 (except 9.6), work through a pandas tutorial of your choice
Quiz 2
Exercise 5
6 (Oct 02) Wrangling 2 Reading: LGN 8-9, pandas tutorials
Project Milestone 2
Exercise 6
7 (Oct 09) Project (Communication) No Friday (Fall Break)
Reading: LGN 12, Wilke selections
Quiz 3
8 (Oct 16) (continued) No Monday and Wednesday (Fall Break, Advising)
Visualization Replication project due
9 (Oct 23) Modeling—Design Reading: sklearn user guide (selections)
Exercise 7
10 (Oct 30) Modeling—Neighbors and trees Quiz 4
Exercise 8
11 (Nov 06) Modeling—Other models Exercise 9
12 (Nov 13) Validation Quiz 5, Exercise 10
13 (Nov 20) LLMs No Wednesday or Friday (Thanksgiving)
14 (Nov 27) Topics Reading: LGN 13-15
Quiz 6, Exercise 11
15 (Dec 04) Project (Communication) Project Milestone

See Moodle for details. We will also be discussing a variety of societal and ethical topics.

Optional material

  • Clustering (Unsupervised Learning)
  • Databases and APIs
  • Text Data
  • Geospatial Data
  • Audio and Image Data

Staff

Materials

Communication

We will use the following communication tools:

  • Outside of class, we’ll communicate primarily using the forums on Perusall. See link on Moodle.
    • Post questions about assignments, concepts, or when you have problems getting code to run.
    • Post answers as well. Answering helps the community and also gives you practice explaining something you just learned.
    • Post notes about interesting articles or events.
  • Use email for personal issues.
  • Use Teams for friendly chat; I’ll redirect most questions about course content to Perusall.

Textbook

All readings wil be posted in Perusall. They will be taken from:

Technology

We will be using Python and the following tools:

  • pandas for data wrangling
  • Plotly for data visualization
  • scikit-learn for modeling
  • Quarto for creating reports
  • RStudio Server
    • The course lab fee funds our contribution towards the shared resources that provide this service. If the fee is a hardship for you, please contact the instructor.
    • You may also install RStudio Desktop on your own computer.

Grading

Unless otherwise arranged, grades will be weighted as follows:

  • Quizzes (7, including during final exam time): 35%
  • Projects: 30%
    • Midterm project: 10%
    • Final project: 20%
  • Exercises (labs and homework): 20%
  • Readings and discussion forums (Perusall): 10%
  • Participation: 5%

Your lowest quiz score will get thrown out.

Late work will be accepted without penalty if you make prior arrangements or present documentation of a hardship. No late work will be accepted after Reading Recess.

Changes to this policy may be made at the instructor’s discretion.

Class Structure

  1. Preparation (before class): Readings and quizzes to prepare you for class.
  2. Lecture (in class): We’ll review the material from the preparation, and add some additional content.
  3. Exercises to practice what we’re learning. We will usually start some of the week’s exercises in class and finish them at home.
  4. Project (outside of class): You will complete two multi-week projects in this class.
  5. Discussion forums (outside of class): We will discuss a variety of societal and ethical topics through a Reformed Christian lens.

We will typically meet in the classroom on Monday and Wednesday and in the lab on Friday. If you have a laptop, please bring it to Monday and Wednesday classes.

Projects

You will complete two multi-week projects in this class.

In the Midterm Project, you will practice some parts of the data science lifecycle by reproducing a published visualization of your choice from source data. In the Final Project, you will additionally apply predictive analytics. You may choose to use the same or different dataset. Details about the projects

Final projects may be completed in teams of up to 3. Teams will have the following additional expectations:

  • Teams must submit a team contract about how they will work together
  • Teams must convince the instructional staff that each team member learned something substantial from completing the project.
  • Each team member must submit an assessment of how they and other team members fulfilled their contract.

Academic Integrity

As the Calvin Academic Integrity Policy says, “At Calvin, the student-faculty relationship is based on trust and mutual respect.”

Data science is a fundamentally collaborative endeavor. Collaboration brings the benefits of multiple perspectives, needed to tackle complex problems faithfully and responsibly. But teamwork also brings the risk of one person doing all of the “learning” for the other. Thus:

  • Collaboration on homework and labs is encouraged. For integrity, humility, and gratitude, you should acknowledge any help you receive by name in your submission.
    • Even if you work side by side with someone, submissions should be your own words and code.
    • Exception: Some assignments will be pair or team work. The assignment will indicate which.
    • It is okay and sometimes encouraged to look up how to do something online! But if you do:
      1. Record the exact URL that had the information that helped you. (This will help improve our instructional materials for next year.)
      2. Retype any code yourself, from memory, even if you have to switch back and forth a lot. (This will help you internalize what you’re borrowing.)
      3. Beware that there is lots of bad code out there. Strive to do better.
  • When asking for help (and everyone should ask for help when they need it), try to solve the problem on your own first. This is critical. Then, when you ask for help, share what you’ve tried and what leads you to think it’s not working. (not just “It’s not working!!”)

Diversity and Inclusion

I (Prof. Arnold) came to Calvin because I wanted to explore what our Christian calling to “act justly” means in the context of data and the technologies that we use with it. Engaging that question wholeheartedly requires that each of us, me included, engage respectfully with perspectives very different from our own. For example, we must question those who abuse data for selfish gain, but we also must question the perspectives of those who challenge those abuses on purely secular grounds.

We intend for this class to be an environment where we equally respect people of every ethnicity, gender, socioeconomic background, political learning, religious background, etc. We will try to create that community by having us read diverse voices, engage with issues of importance to people unlike ourselves, and structure discussions that require students to engage respectfully with perspectives different from their own. We invite your help.

We will not always do this well. If you or someone else in this class is hurt by something we say or do in class, we would like to work to remedy it. We will welcome this feedback in whatever way is comfortable for you: in public, in private, anonymously via Perusall, via my department chair (Professor VanderLinden), or via a report to Safer Spaces or the provost’s office.

Special Circumstances

Occasionally there are special circumstances that require that course policies be adjusted for a particular student. In such cases, it is the responsibility of the student to inform us of the situation as soon as possible, so that the appropriate arrangements can be made. This includes, but is not limited to, students with documented disabilities.

Calvin University is committed to providing access to all students. If you need additional accommodations to succeed in this class, please contact Disability Services in the Center for Student Success (disabilityservices@calvin.edu) as soon as possible to explore what arrangements can be made. The three of us (student, instructor, and Disability Services) will work together to come up with an appropriate solution.

We will give a grade of Incomplete (I) only in unusual circumstances that have been confirmed by the Student Life office.

Wellness

A wide range of things can interfere with your learning: trouble concentrating, stress/anxiety, relationship troubles, family situations, food or housing insecurity, substance use, and many more. You are encouraged to care well for yourself by keeping a consistent sleep schedule, eating well, avoiding drugs and alcohol, exercising, and taking time to relax and connect with friends and family.

Also, learning how to ask for help is an important part of the college experience. Many people on campus are eager to support you. The Center for Counseling and Wellness sees one out of five students each year and can connect you with a variety of mental health resources on and off campus. You can also reach out to your instructor, another faculty/staff, a friend, or a family member you trust for help getting connected to support. You are not alone, and help is available!

License

Creative Commons License Creative Commons Attribution-ShareAlike 4.0 International.

Acknowledgments

A substantial amount of content for the first few weeks of this course is based on material from the “Data Science in a Box” (abbreviated “dsbox” in the materials here) project led by Dr. Mine Çetinkaya-Rundel.

Keith Vander Linden made major revisions to the material in the first half of this course.

Some content co-written with GitHub Copilot.