1. Final Project Updates
2. End of semester survey

Exams

1. Preclass tasks: focus on Tuesday
2. Review: Machine Learning
3. Notebook: Text Classification (HTML) and [ZIP file]
4. Project 3 Discussion

1. Preclass tasks: focus on Friday
2. Review Notebook: Hypothesis Testing (HTML) and [ZIP folder]

1. Discussion: The Digital Natives Papers
2. Linear Regression; Multiple Linear Regression
3. Notebook: Linear Regression (HTML) and [ZIP File]
4. Fun reading: Gauss and the invention of least squares

1. Discussion: Google Searches Project Results
2. Unsupervised Learning: Clustering
3. Notebook: Clustering (as HTML), get ZIP file

1. Naive Bayes Classifer
2. Text Classification with Naive Bayes [Stanford Notes]

Thanksgiving

1. ML: Supervised Classification
2. Notebook: Classification with NLTK

1. Project Two Page
2. Proability Review
3. ML: Decision Trees

1. Preclass Tasks: Searches & Browser History
2. Notebook: SQL and Chrome History

Tanner

1. Discussion: Wikipedia project results
2. Notebook: Wordcloud generation
3. Notebook: Parsing edits in Wikipedia

1. Tasks for Week 8 [See Tuesday]
2. Notebook: Hypothesis Testing with Wikipedia Data

1. Tasks for Week 8 [See Friday]
2. Due Notebook: Time series with Pandas

1. Preclass tasks [See Tuesday]
2. Notebook: Datetime Operations
3. Notebook: Wikipedia Revisions
4. Correlation and Regression

1. Preclass tasks [See Friday]
2. Notebook: Statistical Significance
3. Statistical Significance

Fall Break

1. Preclass tasks
2. Project Discussion
3. Dash Presentations

1. Due Tasks: Learn Dash; cleaning with Pandas
2. Lecture: Statistics Overview

1. Due Tasks: Quiz notebook; paper summary
2. Wikipedia Hands-on with Sarah Barbrow

1. In-class Quiz: Python Review
2. Introducing Dash
3. Task 1: Learn Dash
4. Task 2: Explore Food data with pandas

1. Preclass Task: Wikipedia project
2. Food data cleaning with Pandas (start)
3. Wikipedia Hands-on with Sarah Barbrow

1. Discussion of the Data Science Cycle
2. Linear Algebra with Python. Notebook link.
3. Application: Ranking Search Results

1. Findings from "Eating Habits" reports.
2. Data Science for Social Good. Google Doc.
3. Blogpost: Are women evil?
4. Blogpost: Presidents in the clan?
5. The Fake News Recipe
6. Trust for anonymous communities

1. Pre-assessment Quiz (20 questions)
2. Brief Review of Probability
3. Class Worksheet: Bayes's Theorem and Oreo Cookies

Additional reading: Monty Hall problem (see chapters in Google Drive)

1. Tasks before class
2. Class Sheet 1: Python Problems
3. Class Sheet 2: Estimate Probabilities
4. Task 1: Eating Habits Web Page
5. Task 2: Pandas Practice
6. Task 3: Labeling your food data

1. Reading before class
2. Class sheet 1: Guessing game
3. Class sheet 2: Python Review Exercise
4. Class discussion summary

Data Collection: One week of eating habits

1. Reading and tasks before class
2. Summary of hypothesis/questions for food data gathering
3. Markdown Tutorial (short)
4. Notebook: Data visualization with matplotlib
5. Notebook: Getting started with Pandas
6. Book Chapter about Pandas (private access)