56:219:500
Foundations of Data Science: Programming and Reasoning (3)
Introduction to programming and inferential reasoning in Python for graduate students in Data Science with no prior background in computer science. Topics covered include the basics of language features such as variables, statements (assignment, control, and iteration) , functions, classes, objects and methods, working with file streams and serialized data. Students will also learn to understand and apply fundamental inferential reasoning and statistical ideas to real-world data.
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56:219:501
Algorithmic Problem Solving for Data Science (3)
Introduction to algorithms, data structures and algorithmic paradigms used in data science. Topics include data structures for search and retrieval (arrays, heaps, search trees and hash tables); design techniques such as greedy and dynamic programming and using graph-theoretic algorithms for novel analyses of datasets.
56:219:500 or equivalent
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56:219:511
Statistical Methods for Data Science (3)
Descriptive statistics, probability, random variables, probability distributions, estimation and tests of hypotheses, Analysis of Variance, Chi-square Tests, and Nonparametric Statistics.
Introduction to R and computation for all the above topics using data in different fields.
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56:219:512
Probability and Stochastic Processes (3)
This course teaches the basic concepts of probability theory, whose aim is the description and study of random events. The following basic principles are introduced: stochastic modeling, conditional probabilities and independence of random variables, as well as their expectation and variance. The course concludes with an introduction to Markov chains and predictions on what happens in the long term.
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56:219:513
Regression and Time Series Forecasting (3)
Time series regression is a statistical method for predicting a future response based on the response history (known as autoregressive dynamics) and the transfer of dynamics from relevant predictors. The regression method of forecasting means studying the relationships between data points, e.g., to predict sales in the near and long term, understand inventory levels, understand supply and demand, and review and understand how different variables impact all of these data factors.
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56:219:521
Data Visualization (3)
An introduction to data management including best practices for identifying and using data sources; data cleaning, organization, quality control, and visual analysis and reporting of data using programming languages such as Python. Programming topics will consist of an introduction to data types, functions, conditional and loop statements, proper code documentation, and the use of packages for processing and visualization of text and scientific data.
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56:219:522
Data Management (3)
The course covers the principles and practical techniques of data cleaning, data organization, quality control, and automation of research tasks. Topics include: data types, useful text and math functions, labeling, recoding, data documentation, merging datasets, reshaping, and programming structures such as macros, loops, and branching.
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56:219:523
Geographic Information Systems (3)
An introductory course, with an emphasis on application; training primarily uses open-source GIS software. Students will be able to produce maps and conduct basic research using geographical data in any discipline that uses such data.
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56:219:531
Applied Data Mining and Machine Learning (3)
Introduction to the theory and application of computational techniques for mining big data, especially mathematical and statistical algorithms for discovering patterns from data such as k-nearest-neighbors, clustering and classification. Simple machine learning models like perceptrons, linear and stochastic regression, and support vector machines will also be studied in the context of large, real-world datasets.
56:219:511
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56:219:593
Special Topics in Data Science (3)
Thematic course dealing with a variety of emerging, special topics related to Data Science chosen at the discretion of the instructor. Topics may range from theoretical to practical aspects of Data Science, e.g., aspects of natural language processing, large language models, data ethics, security, data provenance etc.
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56:219:600
Data Science Internship (3)
The practical application of data science knowledge and skills through an approved internship in a sponsoring organization. Arrangements for the internship must be agreed upon by the sponsoring organization and approved by the MS Data Science graduate director before the beginning of the semester. Students should consult the graduate director for detailed instructions before registering for this course.
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56:219:601
Independent Study (3)
Study of a particular subject independently but with frequent consultations with a faculty member.
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56:219:603
Master's Capstone Project (3)
Open only to students pursuing the project option. Design, implementation, and demonstration of a significant software project. Project proposals must be approved by instructor. The project completion requires a software demonstration, a project report and a presentation.
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56:219:701
Master's Thesis (3)
Open only to students pursuing the thesis option. This will involve substantial and independent research on a topic approved and supervised by a faculty member (the thesis adviser) who will work closely with the student. This research will be exposited in the student¿s M.S. thesis and will be defended in front of a thesis committee.
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56:219:800
Matriculation Continued (0)
Continuous registration may be accomplished by enrolling for at least 3 credits in standard course offerings, including research courses, or by enrolling in this course for 0 credits. Students actively engaged in study toward their degree who are using university facilities and faculty time are expected to enroll for the appropriate credits.
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Graduate School-Camden 
