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DATA 5000

Introduction to Data Science

0.5 credit · Graduate

Machine learning; statistics; data science; data ethics; data cleaning; data preparation; data visualization; communication skills to explain advanced analytics. Students work on an interdisciplinary project in data science.

2026

Topics

  • Foundations of Data Science and the Data Science Lifecycle
  • Exploratory Data Analysis and Data Visualization
  • Data Wrangling and Cleaning Techniques
  • Supervised Learning: Regression and Classification
  • Unsupervised Learning: Clustering and Dimensionality Reduction
  • Working with Real-world Datasets (e.g., open data, health, transportation)
  • Reproducibility and Scientific Computing Practices
  • Perform exploratory data analysis and generate meaningful visualizations using R or Python
  • libraries.
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