## ENROLL Course Search

**NOTE:** There are some inconsistencies in the course listing data - ITS is looking into the cause.

**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 23/SP and with Curricular Exploration: FSR found 39 courses.

### CS 111.01 Introduction to Computer Science 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

### CS 111.02 Introduction to Computer Science 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

Sophomore Priority

**Waitlist for Juniors and Seniors:** CS 111.WL2 (Synonym 64307)

### CS 111.03 Introduction to Computer Science 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

#### Other Tags:

This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

Sophomore Priority

**Waitlist for Juniors and Seniors:** CS 111.WL3 (Synonym 64308)

### CS 201.01 Data Structures 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

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.WL1 (Synonym 64311)

### CS 201.02 Data Structures 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

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

### CS 202.00 Mathematics of Computer Science 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

#### Other Tags:

*Prerequisite:* Computer Science 111 and Mathematics 111 or instructor permission

### CS 208.00 Introduction to Computer Systems 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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8:30am9:40am | 8:30am9:40am | 8:30am9:30am |

#### Requirements Met:

Are you curious what's really going on when a computer runs your code? In this course we will demystify the machine and the tools that we use to program it. Our broad survey of how computer systems execute programs, store information, and communicate will focus on the hardware/software interface, including data representation, instruction set architecture, the C programming language, memory management, and the operating system process model.

*Prerequisite:* Computer Science 201 or instructor permission

### CS 252.00 Algorithms 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

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A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis.

*Prerequisite:* Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

### CS 254.00 Computability and Complexity 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

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An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finite-state automata, pushdown automata, and Turing machines; formal languages, including regular expressions and context-free grammars; computability and uncomputability; and computational complexity, particularly NP-completeness.

*Prerequisite:* Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

### CS 257.00 Software Design 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

*Prerequisite:* Computer Science 201 or instructor permission

### CS 304.00 Social Computing 6 credits

Open: Size: 32, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

The last decade has seen a vast increase in the number of applications that connect people with one another. This course presents an interdisciplinary introduction to social computing, a field of study that explores how computational techniques and artifacts are used to support and understand social interactions. We will examine a number of socio-technical systems (such as wikis, social media platforms, and citizen science projects), discuss the design principles used to build them, and analyze how they help people mobilize and collaborate with one another. Assignments will involve investigating datasets from online platforms and exploring current research in the field.

*Prerequisite:* Computer Science 201

### CS 314.00 Data Visualization 6 credits

Open: Size: 15, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

Understanding the wealth of data that surrounds us can be challenging. Luckily, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with taking information and turning it into pictures to better communicate patterns or discover new insights. It combines aspects of computer graphics, human-computer interaction, design, and perceptual psychology. In this course, we will learn the different ways in which data can be expressed visually and which methods work best for which tasks. Using this knowledge, we will critique existing visualizations as well as design and build new ones.

*Prerequisite:* Computer Science 201

### CS 321.00 Making Decisions with Artificial Intelligence 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

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There are many situations where computer systems must make intelligent choices, from selecting actions in a game, to suggesting ways to distribute scarce resources for monitoring endangered species, to a search-and-rescue robot learning to interact with its environment. Artificial intelligence offers multiple frameworks for solving these problems. While popular media attention has often emphasized supervised machine learning, this course instead engages with a variety of other approaches in artificial intelligence, both established and cutting edge. These include intelligent search strategies, game playing approaches, constrained decision making, reinforcement learning from experience, and more. Coursework includes problem solving and programming.

*Prerequisite:* Computer Science 201. Additionally Computer Science 202 is strongly recommended.

### LING 110.00 Introduction to Linguistics 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:15pm3:00pm | 1:15pm3:00pm |

#### Requirements Met:

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Sophomore Priority

**Waitlist for Juniors and Seniors:** LING 110.WL0 (Synonym 64581)

### LING 115.00 Introduction to the Theory of Syntax 6 credits

Open: Size: 20, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

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Sophomore Priority

**Waitlist for Juniors and Seniors:** LING 115.WL0 (Synonym 64583)

### LING 286.07 Japanese Linguistics in Kyoto Seminar: The Structure of Japanese 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

#### Requirements Met:

#### Other Tags:

*Prerequisite:* 100-level Linguistics course

Participation in Carleton OCS Linguistics in Japan Program

### MATH 120.00 Calculus 2 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

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Inverse functions, integration by parts, improper integrals, modeling with differential equations, vectors, calculus of functions of two independent variables including directional derivatives and double integrals, Lagrange multipliers.

*Prerequisite:* Mathematics 101, 111, score of 4 or 5 on Calculus AB Exam or placement via a Carleton placement exam. Not open to students who have received credit for Mathematics 211 or have a score of 4 or 5 on the AP Calculus BC exam

### MATH 210.01 Calculus 3 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

#### Other Tags:

Vectors, curves, calculus of functions of three independent variables, including directional derivatives and triple integrals, cylindrical and spherical coordinates, line integrals, Green's theorem, sequences and series, power series, Taylor series.

*Prerequisite:* Mathematics 120. This course cannot be substituted for Mathematics 211

### MATH 232.01 Linear Algebra 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

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Linear algebra centers on the study of highly structured functions called linear transformations. Given the abundance of nonlinear functions in mathematics, it may come as a surprise that restricting to linear ones opens the door to a rich and powerful theory that finds applications throughout mathematics, statistics, computer science, and the natural and social sciences. Linear transformations are everywhere, once we know what to look for. They appear in calculus as the functions that are used to define lines and planes in Euclidean space. In fact, differentiation is also a linear transformation that takes one function to another. The course focuses on developing geometric intuition as well as computational matrix methods. Topics include kernel and image of a linear transformation, vector spaces, determinants, eigenvectors and eigenvalues.

*Prerequisite:* Mathematics 120 or Mathematics 211

### MATH 232.02 Linear Algebra 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:10pm3:10pm |

#### Requirements Met:

#### Other Tags:

Linear algebra centers on the study of highly structured functions called linear transformations. Given the abundance of nonlinear functions in mathematics, it may come as a surprise that restricting to linear ones opens the door to a rich and powerful theory that finds applications throughout mathematics, statistics, computer science, and the natural and social sciences. Linear transformations are everywhere, once we know what to look for. They appear in calculus as the functions that are used to define lines and planes in Euclidean space. In fact, differentiation is also a linear transformation that takes one function to another. The course focuses on developing geometric intuition as well as computational matrix methods. Topics include kernel and image of a linear transformation, vector spaces, determinants, eigenvectors and eigenvalues.

*Prerequisite:* Mathematics 120 or Mathematics 211

### MATH 236.01 Mathematical Structures 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

#### Other Tags:

*Prerequisite:* Mathematics 232 and either Mathematics 210 or Mathematics 211

Sophomore Priority

**Waitlist for Juniors and Seniors:** MATH 236.WL1 (Synonym 64934)

### MATH 236.02 Mathematical Structures 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

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*Prerequisite:* Mathematics 232 and either Mathematics 210 or Mathematics 211

### MATH 241.00 Ordinary Differential Equations 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

#### Other Tags:

*Prerequisite:* Mathematics 232 or instructor permission

### MATH 261.00 Functions of a Complex Variable 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

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*Prerequisite:* Mathematics 210 or Mathematics 211

### MATH 331.00 Real Analysis II 6 credits

Open: Size: 20, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

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*Prerequisite:* Mathematics 321 or instructor permission

### MATH 341.00 Partial Differential Equations 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

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An introduction to partial differential equations with emphasis on the heat equation, wave equation, and Laplace's equation. Topics include the method of characteristics, separation of variables, Fourier series, Fourier transforms and existence/uniqueness of solutions.

*Prerequisite:* Mathematics 241

### MATH 342.00 Abstract Algebra I 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

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*Prerequisite:* Mathematics 236 or instructor permission

### MATH 395.00 Introduction to Analytic Number Theory 6 credits

Open: Size: 18, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:15pm3:00pm | 1:15pm3:00pm |

#### Requirements Met:

#### Other Tags:

Caroline L Turnage-Butterbaugh

An introduction to the techniques and principles of analytic number theory. Topics covered include arithmetical functions, Dirichlet multiplication, averages of arithmetical functions, elementary theorems on the distribution of the primes, and Dirichlet's theorem on primes in arithmetic progressions.

*Prerequisite:* Mathematics 282 (formerly MATH 312) or Mathematics 342

### PHIL 210.00 Logic 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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10:10am11:55am | 10:10am11:55am |

#### Requirements Met:

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### PSYC 200.00 Measurement and Data Analysis in Psychology 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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10:10am11:55am | 10:10am11:55am |

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*Prerequisite:* Psychology 110, or Psychology/Cognitive Science 232/233, or instructor consent; Concurrent registration in Psychology 201 required

### STAT 120.01 Introduction to Statistics 6 credits

Open: Size: 32, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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8:30am9:40am | 8:30am9:40am | 8:30am9:30am |

#### Requirements Met:

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

*Prerequisite:* Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250 (formerly Mathematics 275).

Sophomore priority

**Waitlist for Juniors and Seniors:** STAT 120.WL1 (Synonym 65282)

### STAT 120.02 Introduction to Statistics 6 credits

Open: Size: 32, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

*Prerequisite:* Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250 (formerly Mathematics 275).

Formerly Mathematics 215

### STAT 120.03 Introduction to Statistics 6 credits

Open: Size: 32, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

*Prerequisite:* Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250 (formerly Mathematics 275).

Sophomore Priority

**Waitlist for Juniors and Seniors:** STAT 120.WL3 (Synonym 65283)

### STAT 120.04 Introduction to Statistics 6 credits

Open: Size: 32, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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3:10pm4:20pm | 3:10pm4:20pm | 3:30pm4:30pm |

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*Prerequisite:* Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250 (formerly Mathematics 275).

Formerly Mathematics 215

### STAT 220.00 Introduction to Data Science 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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(Formerly Mathematics 285) This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.

*Prerequisite:* Statistics 120 (formerly Mathematics 215), Statistics 230 (formerly Mathematics 245) or Statistics 250 (formerly Mathematics 275)

Formerly Mathematics 285

### STAT 230.00 Applied Regression Analysis 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

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(Formerly Mathematics 245) A second course in statistics covering simple linear regression, multiple regression and ANOVA, and logistic regression. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software to analyze real-life data.

*Prerequisite:* Statistics 120 (formerly Mathematics 215), Statistics 250 (formerly Mathematics 275), Psychology 200, or AP Statistics Exam score of 4 or 5.

Sophomore priority

**Waitlist for Juniors and Seniors:** STAT 230.WL0 (Synonym 65286)

### STAT 250.00 Introduction to Statistical Inference 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

#### Requirements Met:

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(Formerly Mathematics 275) Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

*Prerequisite:* Mathematics 240 Probability (formerly Mathematics 265)

Formerly Mathematics 275

### STAT 285.00 Statistical Consulting 2 credits, S/CR/NC only

Closed: Size: 0, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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10:10am11:55am |

#### Requirements Met:

#### Special Interests:

(Formerly MATH 280) Students will apply their statistical knowledge by analyzing data problems solicited from the Northfield community. Students will also learn basic consulting skills, including communication and ethics.

*Prerequisite:* Statistics 230 (formerly Mathematics 245) and instructor permission

Formerly Mathematics 280

### STAT 310.00 Spatial Statistics 6 credits

Open: Size: 20, Registered: 0, Waitlist: 0

M | T | W | TH | F |
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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

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Spatial data is becoming increasingly available in a wide range of disciplines, including social sciences such as political science and criminology, as well as natural sciences such as geosciences and ecology. This course will introduce methods for exploring and analyzing spatial data. Methods will be covered to describe and analyze three main types of spatial data: areal, point process, and point-referenced (geostatistical) data. The course will also extensively cover tools for working with spatial data in R. The goals are that by the end of the course, students will be able to read, explore, plot, and describe spatial data in R, determine appropriate methods for analyzing a given spatial dataset, and work with their own spatial dataset(s) in R and derive conclusions about an application through statistical inference.

*Prerequisite:* Statistics 230 (formerly MATH 245) and Statistics 250 (formerly MATH 275)

### Search for Courses

This data updates hourly. For up-to-the-minute enrollment information, use the Search for Classes option in The Hub