Courses - Fall 2024
DATA
Data Science and Analytics
Open Seats as of
07/18/2024 at 07:30 AM
DATA100
Elementary Statistics and Probability
Credits: 3
GenEd: ,
Prerequisite: MATH110, MATH112, MATH113, or MATH115; or permission of CMNS-Mathematics department; or must have math eligibility of STAT100 or higher and math eligibility is based on the Math Placement Exam or the successful completion of Math 003 with appropriate eligibility.
Restriction: Must not have completed MATH111; or must not have completed any STAT course with a prerequisite of MATH141.
Cross-listed with: STAT100.
Credit only granted for: DATA100 or STAT100.
Simplest tests of statistical hypotheses; applications to before-and-after and matched pair studies. Events, probability, combinations, independence. Binomial probabilities, confidence limits. Random variables, expected values, median, variance. Tests based on ranks. Law of large numbers, normal approximation. Estimates of mean and variance.
DATA110
Applications of R for Data Science
Credits: 1
Prerequisite: DATA100, STAT100, or MATH135; or any 400-level STAT course.
Cross-listed with: STAT110.
Credit only granted for: STAT110 or DATA110.
Intended to prepare students for subsequent courses requiring computation with R, providing powerful and easy to use tools for statistical data analysis. Covers basics of R and R Studio including file handling, data simulation, graphical displays, vector and function operations, probability distributions, and inferential techniques for data analysis.
DATA120
Python Programming for Data Science
Credits: 1
Prerequisite: STAT100, MATH135, or any 400-level STAT course.
An introduction to programming in Python language, using Jupyter Notebooks and Python scripts. Covers variables, conditionals, loops, functions, lists, strings, tuples, sets, dictionaries, files and visualization.
DATA200
Knowledge in Society: Science, Data and Ethics
Credits: 3
Prerequisite: STAT100, MATH135, or any 400-level STAT course.
Course dedicated to the study of ethical issues associated with data science, including data collections, gathering existing data, ethical use of data, data analysis with teams, repeatability and reproducibility of data analysis, and academic and scientific integrity.
DATA250
Discrete Mathematics
Credits: 4
Prerequisite: DATA110 or DATA120; and MATH141.
Introduction to basic discrete mathematical and linear algebraic structures and use of these mathematical structures to solve programming problems. Logic, set theory, formal proof methodology, functions, and basic linear algebra.
DATA320
Introduction to Data Science
Credits: 3
Prerequisite: DATA110 or DATA120; and DATA200 and DATA250; or by permission of the DATA Program Director.
Restriction: Must not be a Computer Science major.
Jointly offered with: CMSC320.
Credit only granted for: CMSC320 or DATA320.
An introduction to data science i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of several topics including statistical data analysis, basic data mining and machine learning algorithms, large-scale data management, cloud computing, and information visualization.
DATA350
Data Visualization and Presentation
Credits: 3
Prerequisite: STAT100, MATH135, or any 400 level STAT course; and DATA110 or DATA120.
Introduction to effective and intuitive visual representations of data, including customizing graphics, plotting arrays, statistical graphics, and representing time series.
DATA400
Applied Probability and Statistics I
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (MATH131, MATH141); or students who have taken courses with comparable content may contact the department.
Cross-listed with: STAT400.
Credit only granted for: DATA400, ENEE324, or STAT400.
Random variables, standard distributions, moments, law of large numbers and central limit theorem. Sampling methods, estimation of parameters, testing of hypotheses.
DATA601
Probability and Statistics
Credits: 3
Prerequisite: Undergraduate courses in calculus and basic linear algebra.
Cross-listed with: BIOI601, MSML601.
Credit only granted for: BIOI601, DATA601 or MSML601.
Provides a solid understanding of the fundamental concepts of probability theory and statistics. The course covers the basic probabilistic concepts such as probability space, random variables and vectors, expectation, covariance, correlation, probability distribution functions, etc. Important classes of discrete and continuous random variables, their inter-relation, and relevance to applications are discussed. Conditional probabilities, the Bayes formula, and properties of jointly distributed random variables are covered. Limit theorems, which investigate the behavior of a sum of a large number of random variables, are discussed. The main concepts random processes are then introduced. The latter part of the course concerns the basic problems of mathematical statistics, in particular, point and interval estimation and hypothesis testing.
DATA602
Principles of Data Science
Credits: 3
Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning).
Cross-listed with: BIOI602, MSML602.
Credit only granted for: BIOI602, DATA602, MSML602 or CMSC641.
Formerly: CMSC641.
An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.
DATA603
Principles of Machine Learning
Credits: 3
Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning).
Cross-listed with: BIOI603, MSML603, MSQC603.
Credit only granted for: BIOI603, DATA603, MSML603, MSQC603 or CMSC643.
Formerly: CMSC643.
A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.
The lecture may be conducted online some weeks and in person other weeks. Please see ELMS for Class meeting details.
DATA607
Communication in Data Science and Analytics
Credits: 3
Prerequisite: DATA602.
Expected learning outcomes include that, in the context of data science and analytics, students should be able to: summarize, report, organize prose, statistics, graphics, and presentations; explain uncertainty, sensitivity/robustness, limitations; describe model generation and representation; discuss interpretations and implications; communicate effectively to diverse audiences within a business organization, and possibly other outcomes.
DATA641
Natural Language Processing
Credits: 3