DS 233: Intro to Data Science II -- Resources (spring2025)

Lecture slides:

• Day 1: Introduction

• Day 2

• Day 3

Download example code: Day3URL.py

• Day 4 database

• Day 5: load data into database and catch up

• Day 6: Working with data
Download example code: WorkingWithData.ipynb Download example data: comma_delimited_stock_prices.csv

• Day 7: Working with data and Natural Language Processing
Download example code: textBlob_Demo.ipynb

• Day 12: kNN
Download example code: Day12_K_nearest_neighbor.ipynb

• Day 11: Naive Bayes
Download example code: NaiveBayes.ipynb

• Day 15: mid term review, claim Google Cloud, Linear Regression
Download example code: Day15_linearRegression.ipynb

• Day 16: Multiple linear regression

Download example code: Day16_multipleRegression.ipynb

• Day 17: catch-up simple and Multiple linear regression

Download example code: Day17_multipleRegression_reference.ipynb

Work on in-class exercise: Day 17 practice

• Day18_logistic_regression

Download example code: Day18_logistic_regression.ipynb

Work on in-class exercise and submit on Sakai

• Day19_decision_Tree

Download example code: Day19_decision_Tree.ipynb

Work on in-class exercise and submit on Sakai before next Tuesday

• Day21_neural_network

Download example code: Day21_neural_network.ipynb

Work on in-class exercise and submit on Sakai

Class Materials:

• Account Requests via the CS HUB

Other useful software:

• Rstudio, great R IDE
• Atom editor
• FileZilla, PuTTY, OpenSSH and WinSCP Clients via the HUB (tools and instructions for accessing your files on the CS server).
• Eclipse (free, full featured Python IDE)