This is archival data. This system was retired as of August 21, 2023 and the information on this page has not been updated since then. For current information, visit catalogs.umn.edu.
Requirements for this program are current for
Fall 2023
Required credits in this minor: 26
to 32
N/A
Data science is one of the fastest growing segments in the modern economy. It is inherently multidisciplinary and offers high job satisfaction. The Division of Science and Math is dedicated to "quality undergraduate instruction in the natural and mathematical sciences so that its graduates are well prepared to seek employment at the B.A. level, to enter graduate or professional school, or to teach at the secondary school level."
UMM is also dedicated to "preparing its students to be global citizens who value and pursue intellectual growth, civic engagement, intercultural competence, and environmental stewardship." Being an informed global citizen and making informed decisions about public policy (civic engagement) and environmental issues (stewardship) is enhanced by the ability to understand, interpret, and analyze data-- all skills developed by the minor. Jobs not directly data-related are increasingly data-driven and the more data-savvy a student, the more prepared they will be to pursue their aspirations.
Although the majority of courses in the minor are Stats and CSci, data science has its own techniques, concerns, and professional communities. A data science minor will aid students interested in pursuing a career in data science or one that entails working with data scientists. A strong industry drive for practitioners to develop 'non-technical coursework' strongly aligns with the values of a liberal arts education and provides students the opportunity to leverage non-technical interests and coursework to increase their attractiveness to potential employers and graduate programs.
Objectives:
• Familiarize students with the techniques and foundational material necessary for students to pursue future studies or careers in data science.
• Ensure that students understand the ethical implications inherent in the data science field.
• Develop the knowledge, skills, and experiences necessary to properly deal with data (data acumen).
• Ensure that students can properly communicate data science ideas and results to both broad and specialized audiences.
Learning Outcomes:
• Students will gain the ability to apply knowledge of data science to other disciplines
• Students will develop their data acumen.
• Students will be able to demonstrate an understanding of the ethical implications inherent in the data science discipline.
• Students will be able to communicate data science ideas and results to both broad and specialized audiences effectively using presentation skills and visualizations.
Program Delivery
This program is available:
via classroom (the majority of instruction is face-to-face)
Minor Requirements
Courses may not be taken S/N unless offered S/N only.
A minimum GPA of 2.00 is required in the minor to graduate. The GPA includes all, and only, University of Minnesota coursework. Grades of "F" are included in GPA calculation until they are replaced.
No more than one course with a grade of D or D+, offset by an equivalent number of credits of A or B grades, may be used to meet the requirements for a data science minor
Statistical Literacy
STAT 1601 - Introduction to Statistics
[M/SR]
(4.0 cr)
or
STAT 2601 - Statistical Methods
[M/SR]
(4.0 cr)
Computational Literacy
CSCI 1251 - Computational Data Management and Manipulation
[M/SR]
(4.0 cr)
or
CSCI 1301 - Problem Solving and Algorithm Development
[M/SR]
(4.0 cr)
or
CSCI 1221 - Introductory Programming Concepts I
[M/SR]
(2.0 cr)
CSCI 1222 - Introductory Programming Concepts II
(2.0 cr)
Ethics
IS 1091W - Ethical and Social Implications of Technology
[E/CR]
(2.0 cr)
or
PHIL 2115 - Ethics and Artificial Intelligence
[E/CR]
(4.0 cr)
Core
Introduction to Data Science
CSCI 2701 - Introduction to Data Science
[M/SR]
(4.0 cr)
or
STAT 2701 - Introduction to Data Science
[M/SR]
(4.0 cr)
One Stop for tuition, course registration, financial aid, academic calendars, and more
STAT 1601 - Introduction to Statistics
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Scope, nature, tools, language, and interpretation of elementary statistics. Descriptive statistics; graphical and numerical representation of information; measures of location, dispersion, position, and dependence; exploratory data analysis. Elementary probability theory, discrete and continuous probability models. Inferential statistics, point and interval estimation, tests of statistical hypotheses. Inferences involving one and two populations, ANOVA, regression analysis, and chi-squared tests; use of statistical computer packages.
prereq: high school higher algebra
STAT 2601 - Statistical Methods
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Descriptive statistics, elementary probability theory; laws of probability, random variables, discrete and continuous probability models, functions of random variables, mathematical expectation. Statistical inference; point estimation, interval estimation, tests of hypotheses. Other statistical methods; linear regression and correlation, ANOVA, nonparametric statistics, statistical quality control, use of statistical computer packages.
prereq: Math 1101 or Math 1021
CSCI 1251 - Computational Data Management and Manipulation
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Introduction to principles and practices of computational data management such as using advanced spreadsheet operations, designing and implementing algorithms to summarize and transform data sets, understanding organization of databases, writing and executing simple database queries, and creating effective data visualizations. Topics include basic issues of information security and introduction to modern technologies that support collaboration. [Note: no elective credit for CSci majors or minors]
CSCI 1301 - Problem Solving and Algorithm Development
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Introduction to different problem solving approaches, major programming paradigms, hardware, software, and data representations. Study of the functional programming paradigm, concentrating on recursion and inductively-defined data structures. Simple searching and sorting algorithms.
CSCI 1221 - Introductory Programming Concepts I
(M/SR)
Credits:
2.0
[max 2.0]
Course Equivalencies:
CSci 1221/CSci 1201
Typically offered:
Every Spring
Basic ideas of data representation, data manipulation, problem solving, and programming. Introduction to programming constructs including defining functions, using conditional execution, and repetition.
CSCI 1222 - Introductory Programming Concepts II
Credits:
2.0
[max 2.0]
Typically offered:
Every Spring
Introduction to programming concepts including recursion, higher-order functions, and object-oriented programming. Application of basic programming constructs in increasingly complex ways.
IS 1091W - Ethical and Social Implications of Technology
(E/CR)
Credits:
2.0
[max 2.0]
Typically offered:
Every Spring
Description of appropriate technological advances. Historical development related to technology and its development cycle. Discussion of the ethical and social implications of technology.
PHIL 2115 - Ethics and Artificial Intelligence
(E/CR)
Credits:
4.0
[max 4.0]
Typically offered:
Periodic Fall & Spring
This course surveys a number of ethical issues that arise from emerging forms of artificial intelligence. Topics include: superintelligence, robot rights, self-driving cars, autonomous weapons, sex/love/friendship robots, data privacy, online manipulation, algorithmic bias, and the future of work.
CSCI 2701 - Introduction to Data Science
(M/SR)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 2701/Stat 2701
Typically offered:
Every Spring
Same as Stat 2701. Introduction to data science and informatics and their application to real world scenarios. Computational approaches to data types; database creation including technologies such as SQL/no-SQL; data visualization; data reduction, condensation, partitioning; statistical modeling; and communicating results.
prereq: CSci 1222 (or CSci 1201) or CSci 1251 or CSci 1301, Stat 1601 or Stat 2601 or Stat 2611 or instr consent
STAT 2701 - Introduction to Data Science
(M/SR)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 2701/Stat 2701
Typically offered:
Every Spring
Same as CSci 2701. Introduction to data science and informatics and their application to real world scenarios. Computational approaches to data types; database creation including technologies such as SQL/no-SQL; data visualization; data reduction, condensation, partitioning; statistical modeling; and communicating results.
prereq: Stat 1601 or Stat 2601 or Stat 2611, CSci 1222 (or CSci 1201) or CSci 1301 or CSci 1251 or instr consent
CSCI 3701 - Intermediate Data Science
Credits:
4.0
[max 40.0]
Course Equivalencies:
CSci 3701/Stat 3701
Typically offered:
Every Fall
Same as Stat 3701. Continued development of topics introduced in Introduction to Data Science. Data mining techniques; applied machine learning techniques; mathematical fundamentals such as introductory linear algebra; graphical models such as Bayesian networks; network analysis; special topics such as topological data analysis; and a strong emphasis on communicating results.
prereq: CSci 2701 or Stat 2701 or instr consent.
STAT 3701 - Intermediate Data Science
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 3701/Stat 3701
Typically offered:
Every Fall
Same as CSci 3701. Continued development of topics introduced in Introduction to Data Science. Data mining techniques; applied machine learning techniques; mathematical fundamentals such as introductory linear algebra; graphical models such as Bayesian networks; network analysis; special topics such as topological data analysis; and a strong emphasis on communicating results.
prereq: CSci 2701 or Stat 2701 or instr consent
STAT 3501 - Survey Sampling
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Fall Even Year
Introduction to basic concepts and theory of designing surveys. Topics include sample survey designs including simple random sampling, stratified random sampling, cluster sampling, systemic sampling, multistage and two-phase sampling including ratio and regression estimation, Horvitz-Thomson estimation, questionnaire design, non-sampling errors, missing value-imputation method, sample size estimation, and other topics related to practical conduct of surveys.
prereq: 1601 or 2601 or instr consent
STAT 4601 - Biostatistics
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Design and analysis of biological studies: biological assays, case-control studies, randomized clinical trials, factorial designs, repeated measures designs, observational studies, and infectious disease data. Analysis of survival data: basic concepts in survival analysis, group comparisons, and Cox regression model. Use of statistical computer packages.
prereq: 1601 or 2601 or 2611 or instr consent
STAT 4631 - Design and Analysis of Experiments
Credits:
4.0
[max 4.0]
Typically offered:
Periodic Fall & Spring
Design and analysis of experimental designs; blocking, randomization, replication, and interaction; complete and incomplete block designs; factorial experiments; crossed and nested effects; repeated measures; confounding effects.
prereq: 3601 or instr consent
STAT 4651 - Applied Nonparametric Statistics
Credits:
4.0
[max 4.0]
Typically offered:
Periodic Fall & Spring
Application of nonparametric statistical methods. Examples use real data, gleaned primarily from results of research published in various journals. Nonparametric inference for single samples, paired samples, and independent samples, correlation and concordance, nonparametric regression, goodness-of-fit tests, and robust estimation.
prereq: 1601 or 2601 or 2611 or instr consent
STAT 4671 - Statistical Computing
Credits:
4.0
[max 4.0]
Typically offered:
Periodic Summer
Entering, exploring, modifying, managing, and analyzing data by using selected statistical software packages such as R or SAS. The use of statistical software is illustrated with applications of common statistical techniques and methods. Designed for students who have a basic understanding of statistics and want to learn the computing tools needed to carry out an effective statistical analysis.
prereq: 1601 or 2601 or 2611 or instr consent
STAT 4681 - Introduction to Time Series Analysis
Credits:
4.0
[max 4.0]
Typically offered:
Fall Odd Year
Introduction to the analysis of time series including those with a connection to environment such as spatial and spatio-temporal statistics. Randomness test, ARMA, ARIMA, spectral analysis, models for stationary and non-stationary time series, seasonal time series models, conditional heteroscedastic models, spatial random processes, covariance functions and variograms, interpolation and kriging.
prereq: 3601 or instr consent
CSCI 2101 - Data Structures
(M/SR)
Credits:
5.0
[max 5.0]
Typically offered:
Every Fall
Introduction to data structures, including stacks, queues, trees, and graphs; implementation of abstract data types and introduction to software testing, using object-oriented techniques and reusable libraries. (4 hrs lect, 2 hrs lab)
prereq: 1222 (or 1201) or 1301 or instr consent
CSCI 3501 - Algorithms and Computability
Credits:
5.0
[max 5.0]
Typically offered:
Every Fall
Models of computation (such as Turing machines, deterministic and non-deterministic machines); approaches to the design of algorithms, determining correctness and efficiency of algorithms; complexity classes, NP-completeness, approximation algorithms. (4 hrs lect, 2 hrs lab)
prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
STAT 3611 - Multivariate Statistical Analysis
(M/SR)
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Analysis of categorical data. Loglinear models for two- and higher-dimensional contingency tables. Logistic regression models. Aspects of multivariate analysis, random vectors, sample geometry and random sampling, multivariate normal distribution, inferences about the mean vector, MANOVA. Analysis of covariance structures: principal components, factor analysis. Classification and grouping techniques: discrimination and classification, clustering, use of statistical computer packages.
prereq: 1601 or 2601 or 2611 or instr consent
