Scott Emrich
Office: 608 Min Kao Hall
Phone: (865) 974-3891; E-mail: semrich at utk.edu
Office hours: whenever my office door is open; and by appointment
Overview
This course equips PhD students from diverse research domains with the computational thinking, data fluency, and collaborative development skills essential for modern data-driven research. It blends foundational computer science concepts (e.g., data structures, version control), applied statistics (e.g., inference, simulation), and core data science workflows (e.g., data wrangling, ML, reproducibility) to foster a deep understanding of how to design and implement reproducible, scalable computational systems.
Designed for students with varied programming backgrounds, DSE 511 emphasizes real-world
application, cross-disciplinary problem-solving, and ethical considerations in data science. Students will
gain confidence writing code, collaborating via version control, processing and analyzing data, and
building tools that support their thesis or domain-specific research.
Syllabus
The syllabus can be found here
Schedule
| Date | Topic | Homework | Notes | |
| 8/19/2025 | (re)Intro to Computational Thinking | Wing, J. M. (2006). "Computational Thinking". Communications of the ACM, 49(3), 33-35.
"The missing semester of your CS education" (MIT) |
||
| 8/21/2025 | Problem decomposition | How to write pseudocode from GeeksforGeeks | ||
| 8/26/2025 | Programming fundamentals (Python) | Python for Data Science Intro course (YouTube, 4 hour primer for diverse backgrounds) Official Python tutorial |
||
| 8/28/2025 | Version control | Atlassian git tutorials | ||
| 9/02/2025 | Collaborative coding | Git.... the game! Pro git book (free online version) |
||
| 9/04/2025 | Stat review I: Exploratory Data Analysis | Seeing Theory online text/visualizations of statistical topics Freedman, D., Pisani, R., Purves, R. (2007). Statistics (4th Edition), Chapters 1-5 |
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| 9/09/2025 | Stat review I (cont): Inference | Think stats (2e) | ||
| 9/11/2025 | Statistical computing | |||
| 9/16/2025 | Data structures and alg basics (1) | Big-O Cheat Sheet | ||
| 9/18/2025 | Data structures and alg basics (2) | zyBook on Data Structures and Algorithms (Python; optional) | 9/23/2025 | Software development + best practices | The Good Research Code Handbook (free online book) Good enough practices in scientific computing (paper) |
| 9/25/2025 | Data acquisition | Our World in Data Practical introduction to scraping (RealPython) |
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| 9/30/2025 | Data privacy and ethics | The ethical algorithm | ||
| 10/02/2025 | Data wrangling | Tidyverse cheat sheet for beginners Python Data Science Handbook Chapter 3 |
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| Fall Break! | 10/09/2025 | Responsible AI | Fairness and machine learning (free online) "Why should I trust you" by Ribeiro et al. |
|
| 10/14/2025 | No class: fairness in AI challenge | |||
| 10/16/2025 | Brief introduction to R | R for Data Science (2e) | ||
| 10/21/2025 | Numerical Methods (review) | |||
| 10/23/2025 | Deep learning: theory | |||
| 10/28/2025 | Deep learning: training | Python Data Science Handbook Chapter 5 | ||
| 10/30/2025 | Model evaluation and interpretability | Interpretable Machine Learning (free online book) | ||
| 11/04/2025 | No class Election day | |||
| 11/06/2025 | Deep learning: convolutional neural networks | Papers with Code arxiv Sanity |
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| 11/11/2025 | Frontiers of Data Science: transformers | The Turing Way Community See papers posted on Canvas |
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| 11/13/2025 | Guest lecture: Applied LLMs | |||
| 11/18/2025 | Modern machine learning tools (student lecture) | |||
| 11/20/2025 | Team Science and technical collaboration / Capstone final checkpoint | Ten simple rules to ruin a collaborative environment (paper) | ||
| 11/25/2025 | Reproducible research (asyncronous/no class) | Boettiger, C. (2015). "An introduction to Docker for reproducible research", SIGOPS Oper. Syst. Rev. Perkel, J. M. (2019). "Workflow systems turn raw data into scientific knowledge", Nature FAIR Principles: https://www.go-fair.org/fair-principles/ |
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| 12/02/2025 | Final capstone project presenations | |||
All are required to abide by the EECS and University honor code. Discussions are encouraged, but all answers/programs must be written/developed individually. Final projects will be performed as a group.