Schedule

8/21

Introduction to the course and ice breaker

8/23

Overview of data science

8/28

No class

8/30

Continue overview of data science

Introduction to Python and Notebooks

9/4

Our first predictive models

9/6

Our first in-class coding

9/11

No class. Dr. Anderson at a conference

9/13

No class. Dr. Anderson at a conference

Regression Assignment Due

9/18

First classification model

Logistic Regression

9/20

First quiz on concepts from our first predictive models and in-class coding

9/25

Probabilities and Statistics

9/27

Quiz over classification homework

Visualizing Decision Trees

10/2

Bayesian Classification

Text Classification with Bayes

10/4

Bayesian Classification In Class Worksheet/Homework (see Dropbox on OAKS)

Text classification Lab and Homework (see Dropbox on OAKS)

No quiz

10/9

Review of Bayesian Classification

Decision Trees

10/11

No class hurricane

10/16

Quiz over Bayesian Classification

Decision Tree Worksheet

10/18

Text Classification

10/23

Decision Tree Worksheet (and Homework)

10/25

Basics of Neural Networks

10/30

More basics of Neural Networks

11/1

Quiz over Decision Trees

Neural Network coding

11/6

Fall break. No class.

11/8

Article about neural networks

PCA Notes

11/13

PCA Worksheet

11/15

Clustering Notes

PCA and Clustering Worksheet

11/20

No in person class. Video lecture and content on OAKS.

Final project assignment

Based on Kaggle Competition

11/27

Final project

11/29

Final project

Last day of class

Final Exam

Thursday, December 6th 8-11 AM (Section 02 and 03) - Presentations

• If you are in section 02, and you can NOT make the December 6th date, please come and see me ASAP.