Research Methods & Data AnalysisRepresentative program

AI and Data Science for Real-World Research

Build a research workflow from Python data preparation to machine-learning evaluation and an applied project.

Overview

Learners practice Python, NumPy/pandas, and machine-learning workflows for an interdisciplinary research question. Depending on preparation, the program introduces scikit-learn, neural-network concepts, or text-analysis methods. The course prioritizes sound evaluation and a reproducible project over covering every advanced technique in a single offering.

Learning goals and possible work

Proposed learning outcomes for this program example:

  • Create a documented data-processing and modeling pipeline.
  • Compare models using a suitable baseline and evaluation strategy.
  • Communicate an interdisciplinary analysis with code and limitations.

Illustrative learning sequence

The sequence below illustrates how this program’s content can be organized. Topics, pacing, and project depth are adapted for each offering. This is not an archived record of a specific cohort’s weekly syllabus.

  1. 01An applied research question and the AI research workflow
  2. 02Python foundations and computational notebooks
  3. 03Data processing with NumPy and pandas
  4. 04Exploratory analysis and feature preparation
  5. 05Supervised learning and baseline models
  6. 06Model validation, leakage, and error analysis
  7. 07Neural-network concepts and an introductory modeling exercise
  8. 08Text analysis and language-model applications as an elective extension
  9. 09Project refinement, interpretability, and responsible data use
  10. 10Reproducible project presentation and research report
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