CMSC320 Calendar

Date	Title	Additional Prep	Work Due
1/27	What is Data Science, Course Introduction and Logistics The Movie Taxonomy Analysis Lecture Slides: Introduction and Overview
1/29	Preliminaries 1: Setting up Preliminaries 1: Measurement and data types Lecture Slides: Measurement and data types Preliminaries 2: Basic data operations Lecture Slides: Basic data operations Python notebook for data operations
2/3	Preliminaries 2: More data operations Lecture Slides: More data operations Basic data plotting Lecture Slides: Basic data plotting Introduction to literate programming: Notebooks
2/5	Guest Lecture (John Dickerson): Best Practices for Data Science Projects [slides in pdf] Guest lecture slidedeck in pptx
2/10	Data representation models: The ER model SQL I: Single-table Queries Lecture Slides: Data representation models
2/12	Relational data operations: Joins Rmarkdown and SQL Pandas and SQL Lecture Slides: Joins and Entity Resolution
2/17	Notebook: Building a complex SQL query Entity Resolution and Record Linkage Lecture Slides: Joins and Entity Resolution
2/19	DB parting shots: query optimization and the JSON data model Lecture Slides: Query optimization and JSON Common operations for data tidying Lecture Slides: Common operations for data tidying Data acquisition: loading and scraping Scraping with python: Beautiful Soup Data cleaning: handling text and dates
2/24	Lecture Slides: Scraping Representing and operating on network data
2/26	Midterm I catch-up and review
3/2	Midterm I
3/4	Iteration over rows in two data frames (R) Iteration over rows in two data frames (python) Exploratory Data Analysis 1: Visualization Lecture Slides: Exploratory Data Analysis 1 - Visualization
3/9	Exploratory Data Analysis 2: Summary statistics Lecture Slides: Exploratory Data Analysis 2 - Summary Statistics
3/11	Data transformations Lecture Slides: Data Transformations Lecture Recording: Data Transformations
3/16	SPRING BREAK
3/18	SPRING BREAK
3/23	CLASS CANCELLED - PREP WEEK FOR ONLINE MOVE
3/25	CLASS CANCELLED - PREP WEEK FOR ONLINE MOVE Handling missing data Lecture slides: Handling missing data Lecture recording: Handling missing data
3/30	Univariate distributions and statistics Lecture slides: Univariate distributions and statistics Lecture Recordings: Univariate distributions and statistics
4/1	Experiment design and hypothesis testing Multivariate probability and statistics Lecture Slides: Design and multivariate probability Lecture Recordings: Design and multivariate probability
4/6	Data Analysis with Geometry Lecture Slides: Data analysis with geometry Linear models for regression Lecture Slides: Linear models for regression Lecture Recording: Data analysis with geometry and linear regression Jupyter Notebook: Linear regression in python with statsmodels
4/8	Linear models for classification Lecture Slides: Linear models for classification Lecture Recording: Linear and logistic regression
4/13	Solving linear ML problems Lecture Slides: Solving linear ML problems Lecture Recording: Solving linear ML problems (Gradient Descent)
4/15	Midterm II catch-up and review
4/20	Midterm II
4/22	Non-linear models for regression and classification: Trees Random Forests Lecture Slides: Tree-Based Methods Lecture Videos: Tree-based Methods
4/27	Classification Metrics Model evaluation using resampling Lecture slides: Classifer evaluation and model selection Lecture videos: Model evaluation and selection
4/29	Unsupervised Learning: Clustering Lecture Slides: Clustering Unsupervised Learning: Dimensionality Reduction Lecture Slides: PCA Lecture Videos: Unsupervised Learning
5/4	Introduction to interactive data visualization Lecture Videos: Interactive Visualization
5/6	Writing about data, analysis and inferences Design principles for data visualization Lecture Videos: Communication
5/11	The Keras way to (deep) learn Course wrapup and perspective Lecture Videos: Deep Learning and Wrapup