Below is a up-to-date view of the class schedule. Adjustments will be made throughout the term to accomodate the pace of the course and any unexpected events or cancellations.
| Date | Week | Day | Topic | Type | Assignment | Due |
|---|---|---|---|---|---|---|
| 8/27/2019 | 1 | Tue | Lecture 0: Course Introduction | Lecture | Assignment 0: Fork Repo | |
| 8/29/2019 | 1 | Thu | Lecture 1: Statistics for Data Scientists: Distributions, Tests, Normality | Lecture | Textbook Reading: Applied Predictive Modeling pages 1-26 | |
| 9/3/2019 | 2 | Tue | Lecture 2: Learning Things: Clustering, Applied Predictive Modeling, Cross-Validation and Performance Evaluation |
Lecture | Textbook Reading: (See Slides) | Assignment 0: Fork Repo |
| 9/5/2019 | 2 | Thu | Guest: Dr. Karen Kazor (Oracle) | Guest | ||
| 9/10/2019 | 3 | Tue | Guest: Community Data Science - Boulder Nonprofits | Guest | Sign up for a 1-1 Meeting Slot | |
| 9/12/2019 | 3 | Thu | Guest: Dr. Monte Lunacek (NREL) | Guest | Assignment 1: Project Ideas & Research Questions | |
| 9/17/2019 | 4 | Tue | Lecture 3: Be a Polyglot - Practical Data Science Programming in Python and R |
Lecture | ||
| 9/19/2019 | 4 | Thu | 1-1 Meetings | Project | Reading 1: Regression | Assignment 1: Project Ideas & Research Questions |
| 9/24/2019 | 5 | Tue | 1-1 Meetings | Project | Assignment 2: Data Prep and Methods Plan | |
| 9/26/2019 | 5 | Thu | Lecture 4: Data Munging, ETL, Databases - the thankless 80% | Lecture | Assignment 3: 1-slide pitch | |
| 10/1/2019 | 6 | Tue | Discussion/Office Hours 1 | Discussion | Reading 2: Classification and Clustering | Reading 1 |
| 10/3/2019 | 6 | Thu | Project Pitch | Project | Assignment 2+3: Data Prep, Methods Plan & 1-slide pitch | |
| 10/8/2019 | 7 | Tue | Discussion/Office Hours 2 | Discussion | Assignment 4: Draft Results & Code | Reading 2 |
| 10/10/2019 | 7 | Thu | Lecture 5: Scientific Data Science: Hypothesis driven, Experimental, Reproducible, & Publishable |
Lecture | ||
| 10/15/2019 | 8 | Tue | 1-1 Meetings/Recitation | Project | Reading 3: Visualization and Knowledge Discovery |
|
| 10/17/2019 | 8 | Thu | 1-1 Meetings/Recitation | Project | ||
| 10/22/2019 | 9 | Tue | Discussion/Office Hours 3 | Discussion | Reading 3 | |
| 10/24/2019 | 9 | Thu | Guest: Dr. Galen MacLauren (NREL) | Guest | Reading 4: Geospatial Analysis | Assignment 4: Draft Results & Code |
| 10/29/2019 | 10 | Tue | Guest: Anirudh Rathore (CU Boulder) and Mike McCabe (CU Boulder) | Guest | Assignment 5: Draft Paper: Data and Methods | |
| 10/31/2019 | 10 | Thu | Discussion/Office Hours 4 | Discussion | Reading 5: Deep Neural Networks | Reading 4 |
| 11/5/2019 | 11 | Tue | Guest: Dr. Kristi Potter (NREL) | Guest | Assignment 6: Draft Paper: Results | Assignment 5: Draft Paper: Data and Methods |
| 11/7/2019 | 11 | Thu | Discussion/Office Hours 5 | Discussion | Reading 5 | |
| 11/12/2019 | 12 | Tue | 1-1 Meetings/Recitation | Project | ||
| 11/14/2019 | 12 | Thu | 1-1 Meetings/Recitation | Project | ||
| 11/19/2019 | 13 | Tue | Guest: Dr. Peter Graf (NREL) | Guest | Assignment 6: Draft Paper: Results | |
| 11/21/2019 | 13 | Thu | Guest: Dr. Lee Becker (Pearson) | Lecture | Assignment 7: Full Rough Draft of Paper & Presentation | |
| 11/26/2019 | Break | Tue | Fall Break | No Class | ||
| 11/28/2019 | Break | Thu | Fall Break | No Class | ||
| 12/3/2019 | 15 | Tue | Guest: Dr. Dmitry Duplyakin (U Utah) | Guest | Assignment 8: Final Paper, Code & Presentation | Assignment 7: Full Rough Draft of Paper & Presentation |
| 12/5/2019 | 15 | Thu | Guest: Dr. Sam Hoda (Well Data Labs) | Guest | ||
| 12/10/2019 | Dead | Tue 8:00-10:45AM | Presentation A | Project | Assignment 8: Presentation | |
| 12/12/2019 | Dead | Thu 8:00-10:45AM | Presentation B | Project | ||
| 12/14/2019 | Final | Sat | No Final | Project | Assignment 8: Final Paper & Code (Due 11:59PM on 12/14) |