Vassar CMPU 100 §1: Programming with Data

Monday	10:30	–	11:45 a.m.
Wednesday	10:30	–	11:45 a.m.
Thursday	1:00	–	3:00 p.m.

Sanders Classroom 006

Professor Gordon

↓ Current

Part 1: Foundations	Monday	Wednesday	Thursday
Introduction Read Syllabus		Sep. 3 Class 1 Programming with data Slides Notebook	Sep. 4 Lab 0 Getting started
Writing and evaluating code Read CIT §3.1–3.3 and CIT §4.1–4.2 Read CIT §4.3 Assignment 1	Sep. 8 Class 2 Expressions, values, and names Slides Notebook	Sep. 10 Class 3 Asking questions Slides Notebook	Sep. 11 Lab 1 Stormy weather
Functions and sequences Read CIT §8 Read CIT §5–5.3 Assignment 2	Sep. 15 Class 4 Functions Slides Notebook	Sep. 17 Class 5 Data sequences Slides Notebook	Sep. 18 Lab 2 Much ado about lists
Tabular data Read CIT §6–6.4 Read CIT §7.1–7.3 Assignment 3	Sep. 22 Class 6 Tables	Sep. 24 Class 7 Columns and charts	Sep. 25 Lab 3
End of Part 1	Sep. 29 Class 8 Review	Oct. 1 Class 9 Case study: Ethics of data collection	Oct. 2 Exam 1 This is Yom Kippur; make-up exams will be on Friday and/or Monday.
Part 2: Data manipulation and analysis
Data cleaning Read CIT §8.1 Assignment 4	Oct. 6 Class 10 Data sanitizing	Oct. 8 Class 11 Normalizing strings	Oct. 9 Lab 4
Combining perspectives Read CIT §8.2–8.3 Read CIT §8.4 Assignment 5	Oct. 13 Class 12 Grouping and aggregation	Oct. 15 Class 13 Joining datasets	Oct. 16 Lab 5
October Break	Oct. 20 🍁	Oct. 22 🍂	Oct. 23 🐿️
Seeing relationships Read CIT §7 Read CIT §8.5 Assignment 6	Oct. 27 Class 14 Relationships and scatter plots	Oct. 29 Class 15 Maps and geographic data	Oct. 30 Lab 6
End of Part 2	Nov. 3 Class 16 Review	Nov. 5 Class 17 Case study: Misleading visualizations	Nov. 6 Exam 2
Part 3: Advanced topics
Iteration and simulation Read CIT §9–9.2 Read CIT §9.3–9.5 Assignment 7	Nov. 10 Class 18 Iteration	Nov. 12 Class 19 Simulation and probability	Nov. 13 Lab 7
Sampling and prediction Read CIT §10–10.4 Read CIT §15–15.1 Assignment 8	Nov. 17 Class 20 Sampling and distributions	Nov. 19 Class 21 Prediction	Nov. 20 Lab 8
Working with text	Nov. 24 Class 22 Text as data	Nov. 26 Class 23 Case study: Bias in language models	Nov. 27 🥧
Dictionaries and data sources Assignment 9	Dec. 1 Class 24 Dictionaries	Dec. 3 Class 25 JSON and Web APIs	Dec. 4 Lab 9
Conclusion ECS	Dec. 8 Class 26 Computational thinking and data science	Dec. 10 No class	Dec. 11 📚
Final exams	Exam 3 To be scheduled by the registrar, with review during study week.

Part 1: Foundations

Monday

Wednesday

Thursday

Introduction

Read Syllabus

Sep. 3

Class 1

Programming with data

Sep. 4

Lab 0

Getting started

Writing and evaluating code

Sep. 8

Class 2

Expressions, values, and names

Sep. 10

Class 3

Asking questions

Sep. 11

Lab 1

Stormy weather

Functions and sequences

Sep. 15

Class 4

Functions

Sep. 17

Class 5

Data sequences

Sep. 18

Lab 2

Much ado about lists

Tabular data

Read CIT §6–6.4
Read CIT §7.1–7.3
Assignment 3

Sep. 22

Class 6

Tables

Sep. 24

Class 7

Columns and charts

Sep. 25

Lab 3

End of Part 1

Sep. 29

Class 8

Review

Oct. 1

Class 9

Case study: Ethics of data collection

Oct. 2

Exam 1

This is Yom Kippur; make-up exams will be on Friday and/or Monday.

Part 2: Data manipulation and analysis

Data cleaning

Read CIT §8.1
Assignment 4

Oct. 6

Class 10

Data sanitizing

Oct. 8

Class 11

Normalizing strings

Oct. 9

Lab 4

Combining perspectives

Read CIT §8.2–8.3
Read CIT §8.4
Assignment 5

Oct. 13

Class 12

Grouping and aggregation

Oct. 15

Class 13

Joining datasets

Oct. 16

Lab 5

October Break

Oct. 20

🍁

Oct. 22

🍂

Oct. 23

🐿️

Seeing relationships

Read CIT §7
Read CIT §8.5
Assignment 6

Oct. 27

Class 14

Relationships and scatter plots

Oct. 29

Class 15

Maps and geographic data

Oct. 30

Lab 6

End of Part 2

Nov. 3

Class 16

Review

Nov. 5

Class 17

Case study: Misleading visualizations

Nov. 6

Exam 2

Part 3: Advanced topics

Iteration and simulation

Read CIT §9–9.2
Read CIT §9.3–9.5
Assignment 7

Nov. 10

Class 18

Iteration

Nov. 12

Class 19

Simulation and probability

Nov. 13

Lab 7

Sampling and prediction

Read CIT §10–10.4
Read CIT §15–15.1
Assignment 8

Nov. 17

Class 20

Sampling and distributions

Nov. 19

Class 21

Prediction

Nov. 20

Lab 8

Working with text

Nov. 24

Class 22

Text as data

Nov. 26

Class 23

Case study: Bias in language models

Nov. 27

🥧

Dictionaries and data sources

Assignment 9

Dec. 1

Class 24

Dictionaries

Dec. 3

Class 25

JSON and Web APIs

Dec. 4

Lab 9

Conclusion

Dec. 8

Class 26

Computational thinking and data science

Dec. 10

No class

Dec. 11

📚

Final exams

Exam 3

To be scheduled by the registrar, with review during study week.

Schedule