ENROLL Course Search
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Alternatives: For requirement lists, please refer to the current catalog. For up-to-the-minute enrollment information, use the "Search for Classes" option in The Hub. If you have any other questions, please email registrar@carleton.edu.
Your search for courses for 19/WI and in CMC 102 found 5 courses.
CS 111.01 Introduction to Computer Science 6 credits
Closed: Size: 34, Registered: 32, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 9:50am11:00am | 9:50am11:00am | 9:40am10:40am |
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Other Tags:
Sophomore Priority
Waitlist for Juniors and Seniors: CS 111.WL1 (Synonym 51312)
CS 111.03 Introduction to Computer Science 6 credits
Closed: Size: 34, Registered: 29, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 3:10pm4:20pm | 3:10pm4:20pm | 3:30pm4:30pm |
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CS 201.00 Data Structures 6 credits
Open: Size: 34, Registered: 21, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |
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Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem"? The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs, and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201.
Prerequisite: Computer Science 111 or instructor permission
Sophomore Priority
Waitlist for Juniors and Seniors: CS 201.WL0 (Synonym 51315)
MATH 245.00 Applied Regression Analysis 6 credits
Closed: Size: 24, Registered: 28, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |
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Prerequisite: Mathematics 215 (or equivalent) or 275
MATH 285.00 Introduction to Data Science 6 credits
Closed: Size: 30, Registered: 34, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |
Requirements Met:
This course will cover the computational side of data analysis, including data acquisition, management and visualization tools. Topics may include: data scraping, clean up and manipulation, data visualization using packages such as ggplots, understanding and visualizing spatial and network data, and supervised and unsupervised classification methods. We will use the statistics software R in this course.
Prerequisite: Mathematics 215 or Mathematics 275
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