Vision Statement

The vision statement serves as your proposal for the final portfolio option. Instead of creating a new data project, you’ll be revising and extending creative work from your five programming assignments this quarter. This document outlines what you’ve accomplished, what you plan to improve, and how these revisions will demonstrate your growth as a data programmer.

The goal of the vision statement is for you to describe your work plan in enough detail that the course staff can evaluate whether it is feasible for the quarter. The portfolio is a solo endeavor, so you will not be working with others. The portfolio is composed of three main parts, a proposal phase, a deliverable phase, and a review phase. These cannot be submitted late and cannot be resubmitted through the resubmission process. Once the deadline for a portfolio part passes, you will no longer be able to make further submissions for that part of the portfolio.

Requirements¶

The following sections describe some requirements for your vision statement and portfolio more broadly. Your vision statement will probably be about 2-3 pages long, but it is acceptable for the vision statement to be longer or shorter as long as it sufficiently covers all of the required sections. Do not worry too much about the length, you should just focus on conveying the required information in as much detail as you think is relevant. Submit your vision statement as a PDF file. (In Microsoft Word, you can choose “Save As” to save your document as a PDF file. In LibreOffice, you can choose “Export to” and save your document as a PDF. Do not turn in a Word document, ODT, or plain text file.)

Vision Statement Format¶

Your vision statement should include the following sections:

• Title and author(s). The title should reflect your portfolio’s theme or focus (not just “CSE 163 Portfolio”).
• Portfolio theme and narrative. Describe the overarching theme or narrative that connects your selected work. This could focus on a particular domain (e.g., environmental science, sports, music, etc.), a value (e.g., interpretability, data transparency, utility, etc.), a set of related technical skills, or your growth trajectory over the quarter. Your chosen theme does not have to be set in stone now, but it should reflect how your selected creative tasks work together to tell a coherent story about your learning.
• Selected creative work. At this point, you will have completed 3 assignments’ worth of creative work. Choose one task from each of THA 1, 2, and 3. Summarize what you have so far, including each of the following sections:
  • Assignment name and number. For example, “THA 1 - Processing”
  • Description of task and code. You can copy these over from your assignment notebook
  • Reflection. What was your approach to proposing and/or solving this task? What were your goals in defining the solution to this task? What does this work demonstrate about your skills or growth?
  • Feedback. Summarize your peers’ feedback on this task, including their “I Like”s, “I Wish”s, and “What if”s. Then, describe how you will respond to this feedback in your portfolio, even if you ultimately choose not to incorporate it.
• Revision plan. For each selected work, describe specific enhancements or refinements you plan to make for the final portfolio submission. These enhancements should go beyond the original assignment requirements. Provide enough detail that a TA can understand what improvements you’ll implement.
• Future creative work. There are still two THAs left in the quarter (about object-oriented programming and mapping, respectively), which you will not have seen by the time you turn in your vision statement. In this section, describe your guiding principles for how you intend to complete the remaining creative components. These should be connected to your portfolio’s theme and narrative. It’s OK if you don’t have a clear idea of the work you’ll do yet, but write down any general ideas or things you would like to do in future creative work.
• Challenge goals. Select at least 2 challenge goals that you are planning to meet through your portfolio enhancements (from the list below). Justify why you think your enhanced portfolio will meet each goal. If you would like to meet more than 2 goals, discuss the 2 goals that you’re most passionate about.
• Work Plan. Describe your plan to divide the portfolio development into at least 3 (but no more than 7) discrete tasks, each with an estimate of the time in hours required for each task. Then, describe your workflow for incorporating peer feedback, and for developing and testing code.
  • While you don’t need to submit any proof that you’ve done so, you should verify that your development environment is properly set up and that you can run all the code you plan to revise.

Challenge Goals¶

Challenge goals help us to define expectations while still offering flexibility for you to design your own portfolio. Meeting 2 or more challenge goals will likely require revising or adding about 120 lines of Python code. In order to qualify for a challenge goal, you must enhance your work with something not explicitly discussed in the course. Here are the challenge goals you can choose from. We provide some examples for some of the bullets that are not meant to be exhaustive lists.

• Integration Across Assignments: Combine or connect analyses from multiple assignments to create a more comprehensive or comparative study. This could involve merging datasets from different assignments, comparing results across different analyses, or building a unified narrative that spans multiple topics covered in the course.
• Advanced Visualizations: Some of your creative work uses a visual component from libraries that we have used in class. For this challenge goal, embellish your work with sophisticated or interactive visualizations that go beyond the original assignment requirements. This could include learning a new visualization library, creating interactive visualizations, or developing animated or dynamic visualizations that better communicate insights.
• Technical Deep Dive: Conduct a mini-research investigation into the theoretical foundations, algorithmic details, or implementation choices underlying your work. This involves proposing a research question about why or how something works (e.g., “Why does this clustering algorithm perform better on this type of data?” or “How do different distance metrics affect the results of my analysis?”), finding and synthesizing academic or technical references, and presenting your findings. This challenge goal emphasizes understanding over implementation; you may not need to write much new code, but you should demonstrate deep engagement with the conceptual foundations of your work.
• Comprehensive Documentation: Create exceptional documentation for your portfolio that includes detailed explanations of your methodology, clear instructions for reproducing your work, thorough code comments, and a polished presentation of results. This should go substantially beyond commenting requirements in the original assignments and demonstrate professional-level documentation practices.
• Narrative Portfolio: Create a compelling narrative that weaves together your portfolio pieces as chapters in a larger story about data, discovery, and learning. This is closer to creative nonfiction that uses storytelling techniques (vivid descriptions, personal reflection, narrative arc, thematic connections) to make your technical work accessible and engaging to a general audience. Your portfolio will read like a data-driven essay or feature article, not a technical summary.
• New Library: Learn a new Python library and use it in your project in a significant way to help with your analysis. Part of this class is being able to learn libraries in Python. Show that you are able to take what you’ve learned in the context of learning a library we have not discussed in-depth in this course. In the Libraries below, we list some recommended libraries (and a complete list of the libraries will cover in this class that do not count as new).
• Other: If you are thinking of other revisions or extensions that you think are challenging enough but does not fit into any of these challenge goals, you can propose a new one to explain why you think your portfolio will be challenging. Do remember that this should be a last resort since it can be challenging to assess the difficulty of your new goal. We reserve the right to deny your proposed challenge goal and you will need to go back and figure out how to make your portfolio challenging enough before submitting.

Challenge Goal Requirements¶

Additional requirements for each of the challenge goals listed above are as follows. Your challenge goals must meet these minimum requirements in order to be counted.

• Integration Across Assignments
  • Must meaningfully connect at least 3 different assignments
  • Integration should involve more than just placing analyses side-by-side; should include synthesis, comparison, or combined insights
  • Must demonstrate how the integrated analysis provides value beyond individual pieces
• Advanced Visualizations
  • Must include at least 3 significantly enhanced or new visualizations beyond original assignment requirements
  • Cannot simply be recreating existing visualizations with different colors or labels
  • Should use visualization techniques not explicitly taught in class assignments
• Technical Deep Dive
  • Must propose and investigate at least one substantial research question about the theoretical or algorithmic foundations at least two components of your work
  • Must cite and synthesize at least 3 credible academic or technical sources (research papers, textbooks, technical documentation)
  • Should include clear explanations of how the research findings relate to your portfolio work
  • If choosing this challenge goal, you must include your research questions in your vision statement to count for completion.
• Comprehensive Documentation
  • Must include a detailed README or documentation file that explains each portfolio piece
  • Code must include thorough, professional-level comments throughout
  • All standards of code quality in CSE 163 are met
  • Optionally, adapt your documentation to an industry standard, such as the Google Style, Sphinx, or another standard that we do not use in this class.
• Narrative Portfolio
  • “Report” will be a unified narrative (separate from in-code documentation) that tells a story across all portfolio pieces.
  • Narratives will be at least 1000 words, written in an engaging and accessible style suitable for general audiences.
  • Narratives will make use of elements of creative nonfiction, such as descriptive writing, thematic develpoment, narrative arc, and denouement.
    • For a better sense of the genre, you may refer to Lee Gutkind’s “What Is Creative Nonfiction?” or Purdue University’s “Creative Nonfiction: An Overview”
• New Library
  • Must use at least one library that was not introduced in class (see below) to answer at least two research questions
  • Multiple libraries do not count as separate challenge goals
    • e.g., using Scipy and plotly counts as one challenge goal
  • The new library may be used in tandem with one of the other challenge goals
    • e.g., using Pytorch to create advanced machine learning models

Advice¶

The following sections have some advice on dataset sources and libraries that you might want to look into. We also provide some more context for what we will be covering for the rest of the quarter.

Developing Your Theme¶

The strongest portfolios have a clear narrative thread connecting the selected works. Consider these approaches:

• Technical progression: Show how you developed mastery of increasingly complex techniques (e.g., from basic data manipulation to advanced machine learning)
• Domain focus: Demonstrate expertise in analyzing data from a particular field that interests you
• Methodological showcase: Highlight your proficiency with specific analytical approaches or visualization techniques
• Problem-solving journey: Illustrate how you approached different types of data challenges throughout the quarter

Revision Strategies¶

Revisions should demonstrate growth and refinement, not just meeting challenge goal checkboxes. When planning revisions or reflecting on feedback for your selected work, consider:

• What aspects of the original assignment felt rushed or incomplete?
• What changes would you make if you had the chance to resubmit your creative component?
• What questions did you have during the original assignment but not have time to explore?
• If you were to write a third task for the creative component, what would you have wanted to write or develop?

Libraries¶

You are free to use most any Python library you find that will be useful to you, especially the ones we have learned in class this quarter. If you know of a library you will use at this time (not required for the proposal) please mention it in your proposal. Below, we list some libraries you might want to look into for your project since students have found them useful in the past:

• Download from Web: requests
• Scientific Computing: SciPy, statsmodels
• Web Scraping: Beautiful Soup, selenium
• Natural Language Processing: spaCy, nltk
• Advanced/Interactive Visualizations: altair or plotly
• Domain-Specific Applications: astropy, chempy, Bioconductor, cantera

You are also encouraged to take advantage of the libraries we learned this quarter to help you on this project (but they won’t count as a new library for that challenge goal). In the list below, we include a list of all libraries we have (or will) discuss in depth in CSE 163.

• pandas
• seaborn
• matplotlib
• scikit-learn
• geopandas (geo-spatial data, covered in Module 5)
• numpy (image data, covered in Module 6)

Example: Weak Portfolio Vision Statement¶

To give some context for our expectations, here’s an example of a portfolio vision that would be too simple.

• Portfolio Theme: “My CSE 163 Work”
• Selections:
  • THA 1: Processing - I made a function to calculate averages in a list
  • THA 3: Education - I created a violin plot
  • THA 4: Networks - I made a network for all the classes I need for my major
• Challenge Goals: I will make the visualizations prettier and add more comments to my code.

Why this is too simple:

• No coherent theme or narrative connecting the works
• Selections don’t demonstrate particular growth, skills, or interests
• Enhancements are superficial (just cosmetic improvements)
• Doesn’t meet any challenge goals meaningfully
• Shows little to no reflection on learning or development

A strong portfolio vision would articulate a clear theme, explain why specific works were selected as representative of that theme, and outline substantial revisions that demonstrate advanced skills or deep analysis. It’s understandable that your work might not seem too connected right now—that’s why we have you write these vision statements in the first place, so you can find those connections!

Grading¶

The vision statement will be graded out of 20 points for satisfactory completion of the written requirements. You will receive full credit for this part of the portfolio as long as you have completed all the requirements.

Submission¶

Your vision statement is due on Thursday Feb 5 at 11:59pm, Seattle time. Submit your proposal as a PDF file on Gradescope. Remember that you cannot submit any part of the project late and you can not use the resubmission process for take-home assessments to make submissions after the due date. The vision statement is a solo submission.

Submit Vision Statement