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 10-14 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. | Research Guide 00: Converting the Problem to a Research Framework. | Project Proposal (200–300 words). |
| 01 | Work Plan in storytelling format: Background → Hypothesis → Experiment Design → Tool Design (optional) → Data Collection → Data Analysis → Results → Report. | Module 00A Guide 00, 01: Presenting Research Plans and Proposals; Computation Guide 01: Getting Started with Python. Lab — Notebook 01: Introduction to Python Programming (Data in Python). | Presentation 01 — Research Proposal |
| 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. | Module 00A Guide 03; Module 00B Notebook 02: Data Automation. | Research protocol with a Data Schema and Data Management Plan (DMP). |
| 03 | Preliminary data collection and redesign experiment plan with feedback from the preliminary data. Update draft of project report. Recording fieldcraft: video basics (camera placement, fps, shutter speed, aperture); audio basics (signal vs noise, room treatment). Finalize primary data parameters. Collect and label preliminary data. | Lab — Notebook 03: Data Visualization; Audio Basics / Video Basics. | First draft of project report. Data schema: structure, processing, and analysis plan |
| 04 | Data collection continues. Data extraction using ML: Develop pipeline - from raw video/ audio data (primary) to analyzable formats (secondary data). Data processing: developing pipelines to convert secondary data into tertiary datasets. | Module 00B Notebook 04: Computational Thinking & Fluency (Data Structures and Algorithms). Optional Lab — Module 00B Notebook 05: Python Classes and Decorators. | Data Documentation linked to data uploaded on the cloud. Notebook: converting primary → secondary data; Notebook: visualizing your secondary data. |
| 05 | Data collection, extraction continues. Data wrangling: creating tertiary datasets — e.g. feature clusters / segments, or kinematics (velocity / angles / counts). Introduction to GitHub. | Module 00A Guide 02: Writing Research Plans and Proposals; Module 03 Guide 01: Art of Communicating Science. | Registered Analysis Plan (why you chose these tests, model formulas, strategies for predictive tasks). Notebook on converting secondary → tertiary datasets: first draft. |
| 06 | Data collection, extraction and wrangling continue. Introduction to statistical tests: choosing appropriate statistical tests. Assessing work Progress: Alignment of research question → hypothesis → experimental design → data schema → data-collection status → Data visualization; projected timeline. | Module 02 Guide 01 “A Navigational Guide to Statistical Methods” | Notebook on converting secondary → tertiary dataset: final draft. First draft of the “Work Progress presentation”. |
| 07 | Data collection, extraction, wrangling, and analysis continue. | Module 03 Guide 01: Art of Communicating Science. | Presentation 02 — Work Progress. |
| 08 | Data collection and extraction ends; Data wrangling and analysis continues. | Module 00B & 02 | At least two plots for the final report. Create GitHub project repo (optional). |
| 09 | Data wrangling ends. Data analysis sprint: create all the graphs / plots for the final report. Update project report | Lab — Conducting Data Analyses. Optional Reading — Using AI to Make Sense of Data: When to Use ML (vs. classical stats)? | First draft of final report with all plots. |
| 10 | Data analysis ends. Report writing continues; create graphs / plots for the final report; Finalize code + data packaging, documentation, README, quick-start, config file. | Module 00B & 02 | Final draft of report with all plots. Update GitHub repo. |
| 11 | Report writing continues: polish and review the final report, focus on visualization / storytelling. Preparing the final presentation: practice the group final presentation; peer review using a structured rubric (clarity, methods, results, limitations, ethics, reproducibility). | — | First draft of the research report. Practice presentation. |
| 12 | Final Presentation: Research Question; Hypothesis; Experimental Design; Data visualization; Research Findings; Conclusion; Future Direction. | — | Presentation 03 — Final Presentation. |
| 13 | Incorporate feedback into the final report. | — | Final report. |