ENROLL

Cecilia M Cornejo

This class introduces students to the full range of production tools and forms, building both the technical and conceptual skills needed to continue at more advanced levels. We will explore the aesthetics and mechanics of shooting digital video, the role of sound and how to record and mix it, field and studio production, lighting, and editing with Adobe Premiere Pro CC. Course work will include individual and group production projects, readings, and writing. This is an essential foundation for anyone interested in moving-image production and learning the specifics of CAMS' studios, cameras, and lighting equipment.

Sophomore Priority. Extra Time required.

Waitlist for Juniors and Seniors: CAMS 111.WL1 (Synonym 64241)

Cecilia M Cornejo

This class introduces students to the full range of production tools and forms, building both the technical and conceptual skills needed to continue at more advanced levels. We will explore the aesthetics and mechanics of shooting digital video, the role of sound and how to record and mix it, field and studio production, lighting, and editing with Adobe Premiere Pro CC. Course work will include individual and group production projects, readings, and writing. This is an essential foundation for anyone interested in moving-image production and learning the specifics of CAMS' studios, cameras, and lighting equipment.

Sophomore Priority. Extra Time required.

Waitlist for Juniors and Seniors: CAMS 111.WL2 (Synonym 64242)

Richard Wells

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.

Sneha Narayan

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)

Amy Csizmar Dalal

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)

John L Berini

Spatial data analysis using Geographic Information Systems (GIS), remote sensing, global positioning, and related technologies are increasingly important for understanding and analyzing a wide range of biophysical, social, and economic phenomena. This course serves as an overview and introduction to the concepts, algorithms, issues, and methods in describing, analyzing, and modeling geospatial data over a range of application areas.

Sophomore Priority

Waitlist for Juniors and Seniors: ENTS 120.WL1 (Synonym 64741)

Katie R St. Clair

(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)

Deepak Bastola

(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

Adam Loy

(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)

Adam Loy

(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

Deepak Bastola

(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

Andy N Poppick

(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