Twin Cities campus
 
Twin Cities Campus

Data Science in Astrophysics Minor

Astrophysics, Minnesota Institute for
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Minnesota Institute for Astrophysics, John T. Tate Hall, 116 Church Street S.E., Minneapolis, MN 55455 (612-624-4811; fax: 612-626-2029)
Email: MIfA@umn.edu
  • Program Type: Graduate minor related to major
  • Requirements for this program are current for Spring 2022
  • Length of program in credits (master's): 8
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
The minor in Data Science in Astrophysics is designed to be interdisciplinary and integrates data science (statistics, data processing, artificial intelligence) with the field of astrophysics. Students pursuing the minor will receive the training needed to advance the field of astrophysics, while simultaneously preparing to be successful professionals and leaders in the modern data-driven workforce. The curriculum covers the fundamental concepts in statistics, data processing and data management, as well as the modern machine learning and deep learning techniques needed for analyzing the ever-increasing astrophysics data-sets. Students will have opportunities to conduct frontier research projects using modern astrophysics data-sets, and will work in interdisciplinary teams mentored by interdisciplinary faculty. They will also have opportunities to develop their professional skills, such as communications and leadership.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
Special Application Requirements:
Students interested in the minor are strongly encouraged to confer with their major field advisor and director of graduate studies, and the Data Science in Astrophysics director of graduate studies regarding feasibility and requirements. A background in science, engineering, or statistics is preferred.
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
Courses offered on both the A-F and S/N grading basis must be taken A-F, with a minimum grade of C earned for each course. The minimum cumulative GPA for the minor is 3.00.
Required courses (8 credits)
Astrostatistics (4 credits)
All students select 1 of the following in consultation with the Data Science in Astrophysics director of graduate studies.
AST 5731 - Bayesian Astrostatistics (4.0 cr)
or STAT 5731 - Bayesian Astrostatistics (4.0 cr)
Big Data (4 credits)
All students select 1 of the following in consultation with the Data Science in Astrophysics director of graduate studies.
AST 8581 - Big Data in Astrophysics (4.0 cr)
or CSCI 8581 - Big Data in Astrophysics (4.0 cr)
or PHYS 8581 - Big Data in Astrophysics (4.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Masters
Doctoral
Electives (4 credits)
Doctoral students select 4 additional credits in consultation with the Data Science in Astrophysics director of graduate studies to meet the 12-credit minimum.
AST 5022 - Relativity, Cosmology, and the Universe (4.0 cr)
AST 8001 - Radiative Processes in Astrophysics (4.0 cr)
AST 8011 - High Energy Astrophysics (4.0 cr)
AST 8990 - Research in Astronomy and Astrophysics (1.0-4.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
CSCI 5609 - Visualization (3.0 cr)
CSCI 5707 - Principles of Database Systems (3.0 cr)
EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
EE 5251 - Optimal Filtering and Estimation (3.0 cr)
EE 5531 - Probability and Stochastic Processes (3.0 cr)
EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
EE 5561 - Image Processing and Applications (3.0 cr)
EE 8581 - Detection and Estimation Theory (3.0 cr)
EE 8591 - Predictive Learning from Data (3.0 cr)
PHYS 8611 - Cosmic Rays and Plasma Astrophysics (3.0 cr)
PUBH 7460 - Advanced Statistical Computing (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5421 - Analysis of Categorical Data (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 5601 - Nonparametric Methods (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
 
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· Fall 2022

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AST 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
STAT 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
AST 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 8581/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics, such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
CSCI 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 8581/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
PHYS 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 8581/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
AST 5022 - Relativity, Cosmology, and the Universe
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5022/Phys 5022
Typically offered: Periodic Fall & Spring
Large-scale structure/history of universe. Introduction to Newtonian/relativistic world models. Physics of early universe, cosmological tests, formation of galaxies. prereq: [2001, Phys 2601] or instr consent
AST 8001 - Radiative Processes in Astrophysics
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Introduction to classical/quantum physics of electromagnetic radiation as it applies to astro-physics. Emphasizes radiative processes (e.g., emission, absorption, scattering) in astrophysical contexts (e.g., ordinary stars, ISM, neutron stars, active galaxies). prereq: instr consent
AST 8011 - High Energy Astrophysics
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Energetic phenomena in the universe. Radiative processes in high energy regimes; supernovae, pulsars, and X-ray binaries; radio galaxies, quasars, and active galactic nuclei. prereq: instr consent
AST 8990 - Research in Astronomy and Astrophysics
Credits: 1.0 -4.0 [max 4.0]
Typically offered: Every Fall & Spring
Research under supervision of a graduate faculty member. prereq: instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5609 - Visualization
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent
CSCI 5707 - Principles of Database Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4707/CSci 5707/INET 4707
Typically offered: Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
EE 5531 - Probability and Stochastic Processes
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks. prereq: [3025, CSE grad student] or dept consent
EE 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, CSE grad student] or dept consent
EE 5561 - Image Processing and Applications
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5561/EE 8541
Typically offered: Every Spring
Two-dimensional digital filtering/transforms. Application to image enhancement, restoration, compression, and segmentation. prereq: [4541, 5581, CSE grad student] or instr consent
EE 8581 - Detection and Estimation Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Risk theory approach to detection and estimation, random process representation, signal parameter estimation. Waveform estimation; detection of phase, frequency, and delay in signals. Applications to communications and radar-sonar signal design and processing. prereq: 5531 or instr consent
EE 8591 - Predictive Learning from Data
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of ‘new’ algorithms. prereq: CSE grad student or instr consent
PHYS 8611 - Cosmic Rays and Plasma Astrophysics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Properties of energetic particles in heliosphere and in astrophysical environments; solar physics, including radiation and magnetic effects; solar wind and magnetospheric physics; physics of radiation belts. prereq: 5012 or instr consent
PUBH 7460 - Advanced Statistical Computing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent