DATA 301: Introduction to Data Science
Winter 2024

Instructor: Alexander Dekhtyar, dekhtyar@calpoly.edu, 14-212

Office Hours:
When
Who Where
Monday 11:10am - 12:00pm Alex 14-212
Tuesday 1:10pm - 3:00pm Alex 14-212
Wednesday 11:10am - 12:00pm Alex 14-212

Additional appoinments: send email.


News and Notes

Old News and Notes

Course Materials

Syllabus Postscript PDF
Dennis Sun's Texbook Github
Jupyter Labs Server https://dev2.csc.calpoly.edu:5000/

Labs

LabDue DateTopic Assignment/Notebooks Submission Instructions Posted on
Lab 1 Wednesday, January 17 Python Data Frames Chapter 1.2 , Chapter 1.3 , Chapter 1.4 Instructions [January 12 , 2024]
Lab 2 Monday, January 29 Categorical and Quantitative Variables Chapter 2.1, Chapter 2.2, Chapter 3.1, Instructions [January 17 , 2024]
Lab 3 Monday, February 5 Dealng with Quantitative Varables, Regression Chapter 3.2, Chapter 3.3, Chapter 5.0,
Chapter 5.1, CA-Alignment2015.csv
Instructions [February 2, 2024]
Lab 4 Wednesday, February 12 Regression, Evaluation of Machine Learning Models Chapter 5.2 , Chapter 5.4 ,
Chapter 5.5 , Chapter 5.6
Instructions [February 9 , 2024]
Lab 5 Wednesday, February 21 Machine Learning: Regression (also, midterm prep) Case Study 3, Case Study 4, Case Study 5 Instructions [February 12, 2024]
Lab 6 Monday, February 26 Regression w/ Categorical Variables and more Chapter 3.6, Chapter 5.3, Chapter 3.5, Instructions [February 12 , 2024]
Project Wednesday, February 21 Analytical Course Project Project Description (PDF) [February 14, 2024]
Lab 7 Monday, March 4 Classification Chapter 6.1, Chapter 6.2, Chapter 6.3 Instructions [February 23 , 2024]
Lab 8 Monday, March 11 Clustering Chapter 3.7, Chapter 7.1, Instructions [March 1 , 2024]
Lab 9 Monday, March 18 Information Retrieval/Text Processing Chapter 10.1 Chapter 10.2 Instructions [March 6 , 2024]
Lab 10 Friday, March 15 Midterm 2 Preparation Chapter 6.4 Case Study 6 Instructions [March 6 , 2024]
Project Wednesday, March 20 Analytical Course Project Project Deliverables [March 6, 2024]

Assigned Reading

Homeworks

Lecture Notes

Lecture 1 What is Data Science? Postscript PDF [March 28, 2016]
Lecture 2 Data Science Process Postscript PDF [April 3, 2016]
Lecture 3 Data Acquisition Postscript PDF [April 3, 2016]
Lecture 4 Tabular Data Postscript PDF [April 3, 2016]
Lecture 5 Textual Data Postscript PDF [April 5, 2016]
Lecture 6 XML Data Postscript PDF [April 11, 2016]
Lecture 7 Document Object Model (DOM) Postscript PDF [April 11, 2016]
Lecture 8 HTML and Beautiful Soup Postscript PDF [April 20, 2016]
Lecture 9 Maps and JSON Postscript PDF [April 20, 2016]
Lecture 14 Recommendation Predictions Postscript PDF [May 11, 2016]
Lecture 15 Supervised Learning (Classification) Postscript PDF [May 18, 2016]
Lecture 16 Unsupervised Learning (Clustering) Postscript PDF [May 23, 2016]


Other Materials


March 29, 2022, dekhtyar at csc.calpoly.edu