Courses - Fall 2025
DATA
Data Science and Analytics
Open Seats as of
04/18/2025 at 03:30 PM
DATA110
Applications of R for Data Science
Credits:
1
Grad Meth:
Reg, P-F, Aud
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
Grad Meth:
Reg
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
Grad Meth:
Reg
Prerequisite: STAT100, MATH135, or any 400-level STAT course.
An introduction to the fundamental concepts and principles that govern ethical data collection, analysis, and usage. Students will explore various methods of data collection and gain an understanding of the ethical implications associated with each approach. Key topics include data ownership, data privacy, data anonymity, and data validity, providing students with a solid foundation in ethical data practices.
DATA250
Discrete Mathematics
Credits:
4
Grad Meth:
Reg, P-F, Aud
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, combinatorics, advanced counting techniques, and elements of linear algebra.
DATA320
Introduction to Data Science
Credits:
3
Grad Meth:
Reg, P-F, Aud
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
Grad Meth:
Reg, P-F, Aud
Prerequisite: DATA100, 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
Grad Meth:
Reg, P-F, Aud
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.
Additional information: Not acceptable toward graduate degrees in MATH/STAT/AMSC.
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
Grad Meth:
Reg
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
Grad Meth:
Reg
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
Grad Meth:
Reg
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
Grad Meth:
Reg
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.
DATA612
Deep Learning
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: DATA603 or MSML603.
Cross-listed with: MSML612.
Credit only granted for: DATA612 or MSML612.
Provides an introduction to the construction and use of deep neural networks: models that are composed of several layers of nonlinear processing. The class will focus on the main features in deep neural nets structures. Specific topics include backpropagation and its importance to reduce the computational cost of the training of the neural nets, various coding tools available and how they use parallelization, and convolutional neural networks. Additional topics may include autoencoders, variational autoencoders, convolutional neural networks, recurrent and recursive neural networks, generative adversarial networks, and attention-based models. The concepts introduced will be illustrated by examples of applications chosen among various classification/clustering questions, computer vision, natural language processing.
DATA641
Natural Language Processing
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: DATA603 or MSML603.
Cross-listed with: MSML641.
Credit only granted for: DATA641 or MSML641.
Introduces fundamental concepts and techniques involved in getting computers to deal more intelligently with human language. Focused primarily on text (as opposed to speech), the class will offer a grounding in core NLP methods for text processing (such as lexical analysis, sequential tagging, syntactic parsing, semantic representations, text classification, unsupervised discovery of latent structure), key ideas in the application of deep learning to language tasks, and consideration of the role of language technology in modern society.
DATA643
Times Series Analysis
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: DATA601, DATA602, and DATA603. Students should also be familiar with linear algebra including eigendecomposition and the basics of the Fourier transform in the context of differential equations or signal processing.
Time series arise in multiple disciplines ranging from epidemiology and climate science to finance and predictive logistics whenever current values are influenced by previously observed ones. This course will cover the behavior and characteristics of time series along with decomposition and forecasting methodologies. We will begin by analyzing random walks, and move onto ARIMA, exponential smoothing, and spectral analysis. The course will conclude with contemporary deep learning methodologies such as LSTM and generative pretrained transformer based architectures.
DATA650
Cloud Computing
Credits:
3
Grad Meth:
Reg
Cross-listed with: MSML650.
Credit only granted for: MSML650 or DATA650.
Presents the state of the art in cloud computing technologies and applications. Topics will include: telecommunications needs, architectural models for cloud computing, cloud computing platforms and services. Data center networking, server, network and storage virtualization technologies, and containerization. Cloud operating and orchestration systems. Security, privacy, and trust management; resource allocation and quality of service; interoperability and internetworking.
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University of Maryland, College Park, MD 20742, USA
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