The prerequisites for this course are Calculus I, Data Structures, and Discrete Mathematics (this last is a co-req).
This course will introduce core principles of learning from data. More and more decisions are being made by algorithms that operate on large datasets, and this course will give students the tools to understand and contribute to this process. Throughout we will emphasize the ethical use of data and analyze case studies of how data science has intersected with society. This course will have a significant theory component, covering introductory linear algebra, probability, statistics, modeling, information theory, and optimization. However, we will also implement these ideas (in Python) and apply them to concrete datasets from a variety of fields (including images, video, text, DNA, music, art, etc).
The language for this course is Python 3.
See the Schedule for each week's reading assignment.
The schedule is tentative and subject to change throughout the semester.
1 | Sep 02 | Introduction to Data Science and Python
Reading:
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Lab 1: Computing and Plotting in Python | |
Sep 04 | ||||
2 | Sep 09 | Introduction to Modeling
Reading:
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Lab 2: Modeling Climate Change | |
Sep 11 | ||||
3 | Sep 16 | Applied Linear Algebra and Optimization
Reading:
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Lab 3: Gradient Descent Last day to drop (Sep 19) | |
Sep 18 | ||||
4 | Sep 23 | Evaluation Metrics
Reading:
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Lab 4: Evaluation Metrics | |
Sep 25 | ||||
5 | Sep 30 | Probabilistic Modeling I (+ review)
Reading:
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Oct 02 | ||||
6 | Oct 07 | Ethics: Disparate Impact
Reading:
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Midterm 1 |
Lab 5: Naive Bayes |
Oct 09 | ||||
Oct 14 | Fall Break | |||
Oct 16 | ||||
7 | Oct 21 | Information Theory
Reading:
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Lab 6: Information Theory | |
Oct 23 | ||||
8 | Oct 28 | Probabilistic Modeling II + Visualization
Reading:
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Lab 7: Logistic Regression and Visualization | |
Oct 30 | ||||
9 | Nov 04 | Introduction to Statistics I
Reading:
| Lab 8: Statistics and Visualization | |
Nov 06 | ||||
10 | Nov 11 | Introduction to Statistics II
Reading: | ||
Nov 13 | ||||
11 | Nov 18 | Midterm 2 Review
Reading: | ||
Nov 20 |
Midterm 2 | |||
12 | Nov 25 | Unsupervised Learning
Reading:
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Nov 27 | Thanksgiving (no class) | |||
13 | Dec 02 | Introduction to Neural Networks
Reading:
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Dec 04 | ||||
14 | Dec 09 | Project Presentations
| Last day to pass/fail (Dec 12) | |
Dec 11 |