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	Sep. 4 Lab 0 Getting started
Writing and evaluating code Read CIT §3.1–3.3 and CIT §4.1–4.3 Assignment 1	Sep. 8 Class 2 Expressions, values, and names	Sep. 10 Class 3 Functions	Sep. 11 Lab 1
Tabular data Read CIT §6 (optional) Read CIT §7.1–7.3 Assignment 2	Sep. 15 Class 4 Tables	Sep. 17 Class 5 Visualizing tabular data	Sep. 18 Lab 2
Tables and arrays Read CIT §6.2 Read CIT §5–5.3 Assignment 3	Sep. 22 Class 6 Selecting and filtering	Sep. 24 Class 7 Columns and arrays	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 types and missing values	Oct. 8 Class 11 Working with text strings	Oct. 9 Lab 4
Combining perspectives Read CIT §8.2 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 Assignment 6	Oct. 27 Class 14 Scatter plots and relationships	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 and CIT §10–10.4 Assignment 7	Nov. 10 Class 18 Loops and comprehensions	Nov. 12 Class 19 Randomness, simulation, and sampling	Nov. 13 Lab 7
Dictionaries and data sources Assignment 8	Nov. 17 Class 20 Dictionaries	Nov. 19 Class 21 JSON and Web APIs	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 🥧
Going further Read CIT § 15–15.1 (optional) Assignment 9	Dec. 1 Class 24 Pandas and friends	Dec. 3 Class 25 Prediction	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

Read CIT §3.1–3.3 and CIT §4.1–4.3
Assignment 1

Sep. 8

Class 2

Expressions, values, and names

Sep. 10

Class 3

Functions

Sep. 11

Lab 1

Tabular data

Read CIT §6 (optional)
Read CIT §7.1–7.3
Assignment 2

Sep. 15

Class 4

Tables

Sep. 17

Class 5

Visualizing tabular data

Sep. 18

Lab 2

Tables and arrays

Read CIT §6.2
Read CIT §5–5.3
Assignment 3

Sep. 22

Class 6

Selecting and filtering

Sep. 24

Class 7

Columns and arrays

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 types and missing values

Oct. 8

Class 11

Working with text strings

Oct. 9

Lab 4

Combining perspectives

Read CIT §8.2
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
Assignment 6

Oct. 27

Class 14

Scatter plots and relationships

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 and CIT §10–10.4
Assignment 7

Nov. 10

Class 18

Loops and comprehensions

Nov. 12

Class 19

Randomness, simulation, and sampling

Nov. 13

Lab 7

Dictionaries and data sources

Assignment 8

Nov. 17

Class 20

Dictionaries

Nov. 19

Class 21

JSON and Web APIs

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

🥧

Going further

Read CIT § 15–15.1 (optional)
Assignment 9

Dec. 1

Class 24

Pandas and friends

Dec. 3

Class 25

Prediction

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.