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

• Our Jupyter Labs Server.

• Welcome to DATA 301! Please, take the survey:

  DATA 301 Survey

Course Materials

Labs

Lab	Due Date	Topic	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

• Week 1. Due: January 10
  Framingham Heart Study Longitudinal Dataset description, PDF
  [January 7, 2024]

• Week 1. Due: January 12
  Vasant Dhar, Data Science and Prediction, Communications of the ACM, vol. 56 (12), PDF
  [January 7, 2024]

  Day by Day

  https://docs.google.com/document/d/1YSHji2xF_2xC3XCkNN5rpKovPpVUmWluZ2keSF2P7rw/edit

  DoW	Date	Lecture	Notebooks				Lab	Materials	Other
  			Original	Pristine	Section 01	Section 03
  Monday	January 8	Syllabus					Lab quiz	Lecture 1, Lecture 2
  Wednesday	January 10	Data Science Process	Chapter 1.2 Chapter 1.3	Chapter 1.2 Chapter 1.3	Chapter 1.2 Chapter 1.3	Chapter 1.2 Chapter 1.3	Complete Chapter 1.2 and Chapter 1.3 exercises	Lecture 1, Lecture 2
  Friday	January 12	Tabular Data: Data Frames and Variables	Chapter 1.3 Chapter 1.4	Chapter 1.3 Chapter 1.4	Chapter 1.3 Chapter 1.4	Chapter 1.3 Chapter 1.4	Complete Chapter 1.3 and Chapter 1.4 exercises
  Tuesday	January 16	Work with Categorical Variables	Chapter 2.1	Chapter 2.1 h	Chapter 2.1	Chapter 2.1	Complete Chapter 2.1 exercises
  Wednesday	January 17	Work with Categorical Variables	Chapter 2.1 Chapter 2.2	Chapter 2.1 Chapter 2.2	Chapter 2.1 Chapter 2.2	Chapter 2.1 Chapter 2.2	Complete Chapter 2.1 and Chapter 2.2 exercises
  Friday	January 19	Work with Quantitative Variables	Chapter 3.1 Chapter 3.2	Chapter 3.1 Chapter 3.2	Chapter 3.1 Chapter 3.2	Chapter 3.1 Chapter 3.2	Complete Chapter 3.1 Assignments
  Wednesday	January 24	Case Studies Part 1		Case Study 1	Case Study 1	Case Study 1	Complete Case Study 1 exercises
  Friday	January 26	Case Study 2		Case Study 2			Complete Case Study 2 exercises	CA-Alignment2015.csv
  Monday	January 29	Work with Quantitative Variables, Scatter plots	Chapter 3.2 Chapter 3.3	Chapter 3.2 Chapter 3.3	Chapter 3.2 Chapter 3.3	Chapter 3.2 Chapter 3.3	Complete Chapter 3.2 and Chapter 3.3 Assignments
  Wednesday	January 31	Introduction to Regression Models		Chapter 5.0	Chapter 5.0	Chapter 5.0	Complete Chapter 5.0 Exercises
  Friday	February 2	Linear Regression	Chapter 5.1.	Chapter 5.1	Chapter 5.1	Chapter 5.1	Complete Chapter 5.1 Exercises	CA-Alignment2015.csv
  Monday	February 5	K-Nearest Neighbors Regression	Chapter 5.2	Chapter 5.2	Chapter 5.2	Chapter 5.2	Complete Chapter 5.2 Exercises	image for notebook (PNG)
  Wednesday	February 7	Training and Test Error, Cross-Validation	Chapter 5.4 Chapter 5.5	Chapter 5.4 Chapter 5.5	Chapter 5.4 Chapter 5.5	Chapter 5.4 Chapter 5.5	Complete Chapter 5.4,Chapter 5.5 Exercises
  Friday	February 9	Hyperparameter Tuning and Model Selection	Chapter 5.6	Chapter 5.6	Chapter 5.6	Chapter 5.6	Complete Chapter 5.6 Exercises
  Monday	February 12	Training and Test Error, Cross-Validation		Chapter 3.6 Chapter 5.3 Case Study 3	Chapter 3.6 Chapter 5.3	Chapter 3.6 Chapter 5.3	Lab 5: Complete Case Study 3 Lab 6:Complete Chapter 3.6,Chapter 5.30 Exercises
  Wednesday	February 14	Visualizing Complex Relationships	Chapter 3.5	Chapter 3.5	Chapter 3.5	Chapter 3.5	Lab 5: Complte Case Study 4 Lab 6: Complete Chapter 3.5 Exercises
  Friday	February 16	Visualizing Complex Relationships		Case Study 5			Lab 5: Complete Case Study 5
  Friday	February 23	Classification (K Nearest Neighbors)	Chapter 6.1	Chapter 6.1	Chapter 6.1	Chapter 6.1	Complete Chapter 6.1 Exercises
  Monday	February 26	Classification: Evaluation	Chapter 6.2	Chapter 6.2,	Chapter 6.2,	Chapter 6.2,	Complete Chapter 6.2
  Wednesday	February 28	Classification: Logistic Regression and SVMs	Chapter 6.3	Chapter 6.3, Chapter 6.4	Chapter 6.3, Chapter 6.4	Chapter 6.3, Chapter 6.4	Complete Chapter 6.3
  Friday	March 1	Distance Metrics	Chapter 3.7	Chapter 3.7,	Chapter 3.7,	Chapter 3.7,	Complete Chapter 6.2
  Monday	March 4	Clustering: K-Means Clustering	Chapter 7.1	Chapter 7.1	Chapter 7.1,	Chapter 7.1,	Complete Chapter 7.1
  Friday	March 8	Text Processing and Analysis	Chapter 10.1 Chapter 10.2	Chapter 10.1 Chapter 10.2	Chapter 10.1 Chapter 10.2	Chapter 10.1 Chapter 10.2	Complete Chapter 10.1, Chapter 10.2
  Monday	March 11	Vector Space Model	Chapter 10.2	Chapter 10.2 Case Study 6	Chapter 10.2	Chapter 10.2	Complete Chapter 10.2 (Lab 9), Case Study 6 (Lab 10)	marketing_campaign.csv

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