Welcome to CMSC 320. This course focuses on (i) data management systems, (ii) exploratory and statistical data analysis, (iii) data and information visualization, and (iv) the presentation and communication of analysis results. It will be centered around case studies drawing extensively from applications, and will yield a publicly-available final project that will strengthen course participants' data science portfolios.
| Date | (When) |
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|---|---|---|---|
| 05/28 | Course Intro | What the fox knows | Project 0 (11:59 PM 05/31) |
| 05/29 | Python Primer | ||
| 05/30 | Data Collection | ||
| 05/31 | Data Processing | ||
| 06/03 | Quiz, Data Processing, Pandas |
Introduction to Pandas | |
| 06/04 |
Tidy Data, SQL
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| 06/05 |
Tidy Data, Regression Overview |
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| 06/06 | Missing Values | ||
| 06/07 | Data Wrangling, Entity Resolution (Slides) | ||
| 06/10 | Quiz, Exploratory Data Analysis I | Project 1 (11:59 PM 06/11) |
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| 06/11 | Exploratory Data Analysis II, Graphs | ||
| 06/12 | Centrality, Community Detection | ||
| 06/13 | Intro to NLP | ||
| 06/14 | Linear Classifier, Language Modeling | ||
| 06/17 | Quiz, Statistical Inference | Project 2 (11:59 PM 06/21) |
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| 06/18 | Hypothesis testing, K-NN | ||
| 06/19 | Loss function, Optimization | ||
| 06/20 | Linear Regression, Gradient Descent | ||
| 06/21 | Review | ||
| 06/24 | Midterm | Loss functions, regression and Gradient descent (Class slides) | |
| 06/25 | Regression and Gradient Descent Contd. | ||
| 06/26 | Stochastic Gradient Descent, Logistic Regression | ||
| 06/27 |
Logistic Regression, Model Selection (Overfitting) |
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| 06/28 | Linear Regression Regularization (Ridge Regression) | ||
| 07/01 | Quiz, Logistic Regression Regularization | Logistic Regression, Regularization (Class slides) | Project 3 (11:59 PM 07/03) |
| 07/02 | Cross Validation | ||
| 07/03 | Decision Trees Example , Final Tutorial Introductions | ||
| 07/04 | No Class - 4th of July | ||
| 07/05 | No Class - Campus Closed, 4th of July extended | ||
| 07/08 | Quiz, Bagging, Random Forests, Intro to SVM | Cross validation, Decision Trees, Random Forests (class slides) | |
| 07/09 | SVM contd. | ||
| 07/10 | Clustering | ||
| 07/11 | PCA | ||
| 07/12 | More PCA | ||
| 07/15 | Quiz, Intro to Neural Networks | Final Tutorial (11:59 PM 07/19) |
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| 07/16 | More Neural Networks | ||
| 07/17 | Wrap up | ||
| 07/18 | Final Presentations | ||
| 07/19 | Final Presentations |
Instructor: Mohammad Nayeem Teli (nayeem at cs.umd.edu)
Office: 1228 IRB
Office Hours: ThF 3:30 - 4:30 PM
| Name | Office hours (at IRB Level 2 lobby) | |
|---|---|---|
| Hanyu Wang | hywang66@cs.umd.edu | TuTh 11:00 AM - 1:00 PM |
| Jue Xu | juexu@terpmail.umd.edu | MW 11:00 AM - 1:00 PM |
| Homework | Due Date*** |
|---|---|
| Project 0 | May 31, 2019 |
| Project 1 | June 11, 2019 |
| Project 2 | June 21, 2019 |
| Project 3 | July 3, 2019 |
| Final Project | July 19, 2019 |