This page is for students enrolled in LS100. It maps the modules onto the weekly meeting schedule. Self-paced learners can follow the same content through Start Here.
Student expectations¶
Work in teams of 2–4, while submitting assignments individually.
Expected effort: approximately 6–10 hours per week.
Attend weekly meetings and review assigned materials in advance.
Present at least one relevant research paper during the semester.
Participate actively in discussion, peer feedback, and project presentations.
Curriculum & Weekly Timeline¶
The timeline below gives a mental map of what happens each week — the focus and tasks, the reading and lab that support them, and what you submit. Weeks run alongside your research project, so most weeks combine learning with hands-on progress on your own data.
| Week | Focus & tasks | Reading & lab | Deliverable |
|---|---|---|---|
| 00 | Draft research proposal and form research team. Frame research questions, why they matter, hypotheses, the test / experimental approach, and expected outcome. | Guide 01: Writing Research Plans and Proposals. | Project Proposal (200–300 words). |
| 01 | Work Plan in storytelling format: Background → Hypothesis → Experiment Design → Tool Design (optional) → Data Collection → Data Analysis → Results → Report. | Guide 01: Writing Research Plans and Proposals; Guide 02: Getting Started with Python. Lab — Notebook 01: Introduction to Python Programming (Data in Python). | Presentation 01 — Work Plan. |
| 02 | Experimental Design and Data Schema: choose primary “raw” data format (image / video / audio / digital records); define data schema (variables, metrics) and a data-labeling guideline; define collection methods and success criteria; schedule data collection. | Lab — Notebook 02: Data Automation. | Minimal protocol with a Data Management Plan (DMP). |
| 03 | Preliminary data collection and redesign of the work plan: recording fieldcraft; video basics (camera placement, fps, shutter speed, aperture); audio basics (signal vs noise, room treatment); using AI for data collection (computer-vision and audio models); collect and label preliminary data; redesign the experiment; introduction to GitHub. | Lab — Notebook 03: Data Visualization; Audio Basics / Video Basics. | Submit the data structure, processing, and analysis plan |
| 04 | Data processing: convert primary data into an analyzable format (secondary data); finalize methods for converting secondary data into a tertiary dataset. | Lab — Notebook 04: Computational Thinking & Fluency (Data Structures and Algorithms). Optional Lab — Notebook 05: Introduction to Python Classes and Decorators. | Notebook: converting primary → secondary data; Notebook: visualizing your secondary data. |
| 05 | Data processing: data collection and labeling; create a tertiary dataset — e.g. feature clusters / segments, or kinematics (velocity / angles / counts). | Art of Communicating Science. | Notebook: converting secondary → tertiary datasets; first draft of the project report. |
| 06 | Work Progress: Research Question; Hypothesis; Experimental Design; Data schema; Data-collection status; Data visualization from primary, secondary, and tertiary data; projected timeline. | Art of Communicating Science. | Presentation 02 — Work Progress. |
| 07 | Introduction to statistical tests: data collection and labeling; data wrangling; choosing appropriate statistical tests. | — | Registered Analysis Plan (why you chose these tests, model formulas, strategies for predictive tasks). |
| 08 | Data finalization: data collection and labeling ends; data wrangling. | — | Data Documentation linked to data uploaded on the cloud. |
| 09 | Data analysis sprint; create the first graph / plot for the final report. | Lab — Conducting Data Analyses. Optional Reading — Using AI to Make Sense of Data: When to Use ML (vs. classical stats)? | Submit at least one plot |
| 10 | Data analysis & report writing: analysis sprint + writing window; create graphs / plots for the final report; code + data packaging, documentation, README, quick-start, config file. | — | Submit all plots |
| 11 | Data analysis & report writing: analysis sprint + writing window; writing focus on visualization / storytelling. | — | — |
| 12 | Report writing & preparing for the final presentation: polish and review the final report; practice the group final presentation; peer review using a structured rubric (clarity, methods, results, limitations, ethics, reproducibility). | — | First draft of the final report. |
| 13 | Final Presentation: Research Question; Hypothesis; Experimental Design; Data visualization; Research Findings; Conclusion; Future Direction. | — | Presentation 03 — Final Presentation. |
| 14 | Incorporate feedback into the final report. | — | Final report. |