Jump to: Components, Submission, Milestones.
Individual students will work on a project of their own choice and design over the course of the semester, culminating with a class presentation followed by a final project delivery. The goal of this project is to make a linguistic discovery through application of data-intensive methods.
A project consists of three main components: data, analysis, and presentation.
Start with found data. Many linguistics research projects begin with a targeted data collection effort -- field work, surveys, elicitation, human subjects, and more. But the underlying assumption of data science is that data exists in the wild, and it is up to a data scientist to harness it. True to this assumption, we will have you start with data that is found in the wild, be it published data sets, corpora, or social media streams.
Add value. You should not, however, be content with data as it is packaged and presented to you. In many cases, your data will need a lot of work -- sourcing, cleaning up, and reorganizing. In other cases, you may be dealing with published data that's more or less ready for analysis. You are, then, expected to add value: augmenting, annotating and leveraging multiple data sets are all potential avenues.
Follow best data practices. Throughout this semester, we will be learning about best data practices, both emerging and firmly established in data science circles. Make sure your own data efforts and the output are in compliance.
Linguistic analysis. You will have designed your data with a research question in mind. Your data should make a suitable empirical basis for your linguistic inquiry; your research question should be properly motivated and addressed in a theoretically and methodologically sound manner. You interpretations of the findings should likewise be rigorously supported by your data. Even with meticulous preparation, however, your data in the end may not prove fruitful grounds for your original research question. Pivoting is therefore allowed up to a certain point; whether or not this move is ultimately successful, reasons for pivoting and/or failure of the original research agenda must be thoroughly probed and documented, since this sort of outcome is all part and parcel in research efforts deeply grounded in real-life data and, further, provides valuable insight.
Computational methods. In your linguistic analysis, you are expected to employ various computational methods including natural language processing, statistics, machine learning, topic modeling and more. Proper techniques should be used in accordance with your research question and the specifics of your data. At the same time, you should demonstrate mastery of these techniques by justifying your choice of computational methods and thoroughly evaluating the outcome, rather than blindly applying them and accepting the returned output. As with linguistic analysis, failed experimentation should not be brushed aside, but rather receive proper investigation and documentation, as this is all part of the discovery process.
This component encompasses all audience-facing aspects of your project, which include but are not limited to:
- Proper use of GitHub as a project-hosting and publication platform.
- Overall documentation.
- Structure, readability and organization of your Python code in the form of Jupyter notebooks.
- Visualization through graphs and plots.
- Your oral presentation, scheduled in the last two weeks of class.
- Your final report: language, content, clarity, precision, organization, citation, etc.
Weight distribution. Ideally, a project will have the three components in perfect balance: a total of say 180 points will be equally split between data/analysis/presentation as 60-60-60. In reality, everyone's project will be different: some will have ambitious and challenging data curation plans, while others might wish to focus their efforts on extensive use of advanced computational methods. To accommodate this, a limited amount of trade-off is provisioned between the "data" and the "analysis" components: more data-focused projects therefore may have up to 70-50-60 distribution with more data-side contribution, while projects heavily focused on analysis are allowed to go easier on data-related efforts, with up to 50-70-60 split.
Your project should be initiated and developed in the form of a GitHub-hosted public repository. The final deliverables should include:
- A README document and a LICENSE document accompanying your GitHub repository.
- A written report containing a summary of your data and linguistic analysis. Anywhere between 5 and 10 pages, of which a minimum of 3 pages must be devoted to written descriptions (not including charts, graphs, examples, tables, etc.).
- Your data.
- Python scripts in Jupyter Notebook form, that you created and used to process, explore and analyze the data.
- Slides or other materials you used for your in-class presentation.
The term project carries a total of 400 points, which you accrue over the course of the semester through meeting several, structured, milestones. Refer to the Schedule page for the dates.
| Milestone | Points | Distribution: Data Ⓓ; Analysis Ⓐ; Presentation ⓟ | What | |
| 1 | Project ideas | 20 | ⒹⒹⒶⒶ | Send instructor 1-2 project ideas. |
| 2 | Project plan | 20 | ⒹⒶⓟⓟ | Finalize project plan, create a GitHub project repository. |
| 3 | 1st progress report | 40 | ⒹⒹⒹⒹⒹⒹⓟⓟ | Focus on data curation, report progress. |
| 4 | 2nd progress report | 40 | ⒹⒹⒹⒹⒶⒶⓟⓟ | Continue with data curation, attempt analysis. |
| 5 | 3rd progress report | 40 | ⒹⒹⒶⒶⒶⒶⓟⓟ | Data-side effort should be done; ramp up analysis. |
| 6 | Project presentation | 60 | ⒶⒶⒶⒶⒶⒶⓟⓟⓟⓟⓟⓟ | Oral presentation of your work in classroom. |
| 7 | Final project submission | 180 | ⒹⒹⒹⒹⒹⒹⒶⒶⒶⒶⒶⒶⓟⓟⓟⓟⓟⓟ ⒹⒹⒹⒹⒹⒹⒶⒶⒶⒶⒶⒶⓟⓟⓟⓟⓟⓟ | Turn in final project in the form of a GitHub repository. |
More detail will follow as each milestone approaches.