Engineering Data Science (ENGR)
Description
The data science major prepares students with skills and competency in data analysis and interpretation, algorithm design and implementation, and helps them develop aptitudes for interdisciplinary problem-solving. The interdisciplinary program enables students to take advantage of career and employment opportunities across diverse fields involving data-rich, data-driven systems and applications. Ultimately, this will help address the increasing societal and economic need for a qualified workforce in our digital age.
Students can select a major in data science through one of three colleges: Arts and Sciences (Department of Mathematics), Engineering (School of Computing), or Agricultural Science and Natural Resources (Department of Statistics). Students in the College of Engineering (COE) will have the opportunity to investigate and learn about the various aspects of data science from data collection to data visualization, from foundations of computational methodologies to software and hardware applications in data science. In particular, students in the COE track will have a year-long senior capstone and a practicum to enrich their experience in building Data Science solutions and working with research and development in data science. The data science program offers flexibility for non-engineering students to earn a dual degree in data science and their chosen discipline’s degree program. In addition, students may choose to add a minor that both complements and enhances the data science major.
College Requirements
College Admission
College Entrance Requirements
Students must have high school credit for (one unit is equal to one high school year):
- Mathematics – 4 units: 2 of algebra, 1 of geometry, and 1 of precalculus and trigonometry
- English – 4 units
- Natural sciences – 3 units that must include 1 unit of physics and 1 unit of chemistry (chemistry requirement waived for students in construction management or computer science)
- Foreign language – 2 units of a single foreign language
- Social studies – 3 units
- Students having a composite ACT score of 28 or greater (or equivalent SAT score) will be admitted to the College of Engineering even if they lack any one of the following: trigonometry, chemistry, or physics. Students without test scores who are missing a full unit of trigonometry/pre-calculus/calculus or chemistry or physics will be evaluated through College Review.
- Students having an ACT score of 19 or less in English (or equivalent SAT score) or a grade lower than B in high school English, must take ENGL 150 Writing and Inquiry or ENGL 151 Writing and Argument.
A total of 16 units is required for admission.
Engineering requires that student performance meet one of the following standards: composite ACT of 24, SAT of 1180, ACT Math subscore of 24, SAT Math subscore of 580, or a 3.5 cumulative GPA.
Any domestic first-year student who does not gain admission to Engineering but does gain admission to the University of Nebraska-Lincoln (UNL) will be reviewed through College Review. College Review is conducted through the College Review Committee which considers factors beyond standardized testing. Any first-year student who is not admitted through college review is placed in Pre-Engineering (PENG) with the Exploratory and Pre-Professional Advising Center (Explore Center). Students in the Explore Center can transfer to the College of Engineering once college admission requirements are met.
Students for whom English is not their language of nurture must meet the minimum English proficiency requirements of the University.
Students who lack entrance units may complete precollege training by Independent Study through the University of Nebraska–Lincoln Office of On-line and Distance Education, in summer courses, or as a part of their first or second semester course loads while in the Explore Center or other colleges at UNL.
Students should consult their advisor, their department chair, or Engineering Student Services (ESS) if they have questions on current policies.
Other Admission Requirements
Students who transfer to the University of Nebraska–Lincoln from other accredited colleges or universities and wish to be admitted to the College of Engineering (COE) must meet COE first-year student entrance requirements, have a minimum cumulative GPA of 2.5, and be calculus-ready. Students not meeting either of these requirements must enroll in the Explore Center or another University college until they meet COE admission requirements. Students transferring from UNO, UNL, or UNK to the College of Engineering must be in good academic standing with their institution.
The COE accepts courses for transfer for which a C or better grade was received. Although the University of Nebraska–Lincoln accepts D grades from the University of Nebraska Kearney and the University of Nebraska Omaha, not all majors in the COE accept such low grades. Students must conform to the requirements of their intended major and, in any case, are strongly encouraged to repeat courses with a grade of C- or less.
Students who were previously admitted to COE and are returning to the College of Engineering must demonstrate a cumulative GPA of 2.5 to be readmitted to COE.
College Degree Requirements
Grade Rules
Grade Appeals
In the event of a dispute involving any college policies or grades, the student should appeal to their instructor, and appropriate department chair or school director (in that order). If a satisfactory solution is not achieved, the student may appeal their case through the College Academic Appeals Subcommittee.
Catalog Rule
Students must fulfill the requirements stated in the catalog for the academic year in which they are first admitted at the University of Nebraska–Lincoln. In consultation with advisors, a student may choose to follow a subsequent catalog for any academic year in which they are admitted to and enrolled as a degree-seeking student at Nebraska in the College of Engineering. Students must complete all degree requirements from a single catalog year. The catalog which a student follows for degree requirements may not be more than 10 years old at the time of graduation.
Students who have transferred from a community college may be eligible to fulfill the requirements as stated in the catalog for an academic year in which they were enrolled at the community college prior to attending the University of Nebraska-Lincoln. This decision should be made in consultation with the student’s College of Engineering academic advising team (e.g., ESS professional advisor and the chief faculty advisor for the student’s declared degree program). The chief faculty advisor has the final authority for this decision. Eligibility is based on a) enrollment in a community college during the catalog year the student wishes to utilize, b) maintaining continuous enrollment of at least 12 credit hours per semester at the previous institution for at least 2 semesters, and c) continuous enrollment at the University of Nebraska-Lincoln within 1 calendar year from the student’s last term at the previous institution. Students must complete all degree requirements from a single catalog year and within the timeframe allowable for that catalog year.
Learning Outcomes
The primary student learning outcomes of the interdisciplinary data science major are:
- Foundational knowledge and expertise in the analysis of large-scale data sources from the interdisciplinary perspectives of applied computer science, data modeling, mathematics, and statistics.
- Foundational knowledge and expertise in the application of computing, informatics, and modeling to solve multidisciplinary problems.
- Abilities and professional skills to solve multidisciplinary data science problems as a member of an interdisciplinary team.
- Familiarity with ethical challenges in data science, including ethical collection of data, responsible use of data and algorithmic bias.
Major Requirements
Complete the data science foundations
Core Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Foundations | ||
| CSCE 155T | Computer Science I: Informatics Focus 1 | 3 |
| CSCE 311 | Data Structures and Algorithms for Informatics 2 | 3 |
| CSCE 320 | Data Analysis | 3 |
| MATH 104 | Applied Calculus (ACE 3) | 3-5 |
| or MATH 106 | Calculus I | |
| MATH 203 | Contemporary Mathematics | 3-4 |
| or MATH 203J | Contemporary Math | |
| or MATH 107 | Calculus II | |
| MATH 315 | Linear Algebra for Data Science | 3 |
| STAT 101 | Introduction to Data | 3 |
| STAT 102 | Principles of Statistical Analysis | 3 |
| Credit Hours Subtotal: | 24-27 | |
| Data Science Professional Experience 3 | ||
| CSCE 386 | Practice and Professional Development: Design and Implementation | 3 |
| or CSCE 492 | Special Topics in Computer Science | |
| or CSCE 495 | Internship in Computing Practice | |
| CSCE 486 | Computer Science Professional Development (ACE 8) 4 | 3 |
| or CSCE 486H | Honors Computer Science Professional Development | |
| CSCE 487 | Computer Science Senior Design Project (ACE 10) | 3 |
| or CSCE 493A | Interdisciplinary Capstone | |
| or MATH 435 | Math in the City | |
| Credit Hours Subtotal: | 9 | |
- 1
CSCE 155T is recommended, but any of the CSCE 155 courses may be used.
- 2
- 3
CSCE 492 may be used only if topic is related to Data Science.
- 4
May be replaced by a student's second major's capstone course.
Specific Major Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Focus Areas | ||
| Select 4 courses from 2 of the focus areas below for at least 12 credit hours | 12 | |
| Artificial Intelligence | ||
| Foundations of Constraint Processing | ||
| Digital Image Processing | ||
| Computer Vision | ||
| Introduction to Data Mining | ||
| Multiagent Systems | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Machine Learning | ||
| Introduction to Deep Learning | ||
| Applied Computing: Sociology | ||
| Applied Sociology: Community-based Research I | ||
| Applied Sociology: Community-based Research II | ||
| Applied Research in Public Opinion | ||
| Ethics and the Responsible Conduct of Research | ||
| Strategies of Social Research: Qualitative Methods | ||
| Advanced Social Network Analysis | ||
| Survey Design and Analysis | ||
| Software Development | ||
| Software Engineering | ||
| Human-Computer Interaction | ||
| Data Visualization | ||
| Software Engineering for Robotics | ||
| Advanced Topics in Software Engineering | ||
| Internet Systems and Programming | ||
| Software Design and Architecture | ||
| Testing, Verification and Analysis | ||
| Requirements Elicitation, Modeling and Analysis | ||
| Data Pipeline | ||
| Statistical Computing I: Data Wrangling | ||
| Statistical Computing II: Data Management and Visualization | ||
| Data Modeling for Systems Development | ||
| Database Systems | ||
| Advanced Embedded Systems | ||
| Internet of Things | ||
| Molecular and Nanoscale Communication | ||
| Data and Network Security | ||
| Wireless Communication Networks | ||
| Statistical Modeling | ||
| Principles of Study Design | ||
| Mathematical Statistics and Modeling I | ||
| Mathematical Statistics and Modeling II | ||
| Statistical Collaboration I | ||
| Advanced Statistical Design | ||
| Introduction to Survey Sampling | ||
| Introduction to Spatial Statistics | ||
| Statistical Analysis of Genomics Data | ||
| Introduction to Regression Analysis | ||
| Introduction to Mathematical Statistics I: Distribution Theory | ||
| Introduction to Mathematical Statistics II: Statistical Inference | ||
| Model Selection and Prediction | ||
| Introduction to Nonparametric Statistics | ||
| Introduction to Categorical Data Analysis | ||
| Introduction to Time Series Analysis | ||
| Introduction to Bayesian Analysis | ||
| Survey Design and Analysis | ||
| Bioinformatics Applications in Agriculture | ||
| Mathematical Modeling | ||
| Calculus III | ||
| Differential Equations | ||
| Theory of Linear Transformations | ||
| Introduction to Partial Differential Equations | ||
| Principles of Operations Research | ||
| Nonlinear Optimization | ||
| Numerical Analysis I | ||
| Numerical Methods for Applied Math | ||
| Combinatorics | ||
| Graph Theory | ||
| Introduction to Topology | ||
| Probability Theory | ||
| Stochastic Processes | ||
| Applied Computing: Journalism & Humanities | ||
| UX/UI Design | ||
| Geospatial Approaches in Digital Humanities and Social Sciences | ||
| Digital History | ||
| Data Journalism | ||
| Data Visualization | ||
| Analysis for the National Security Establishment | ||
| Sports Data Visualization and Analytics | ||
| Applied Computing: Natural Resources | ||
| Introduction to Geospatial Technologies | ||
| GIS for Agriculture and Natural Resources | ||
| Introduction to Remote Sensing | ||
| Advanced Farm Management and Linear Programming | ||
| Commodity Price Forecasting | ||
| Bioinformatics Applications in Agriculture | ||
| Site-specific Crop Management | ||
| Equipment and Tractor Testing | ||
| Credit Hours Subtotal: | 12 | |
Ancillary Requirements
| Code | Title | Credit Hours |
|---|---|---|
| BREADTH COURSES - ARTS, HUMANITIES AND SOCIAL SCIENCES | ||
| Complete 3 credits in Arts, Humanities and Social Science Courses 1,2 | 3 | |
Select any course from AHIS, ANTH, CLAS, COMM, ECON, ENGL, ETHN, GEOG, HIST, PHIL, POLS, PSYC, RELG, SOCI, GREK, LATN, FREN, GERM, SPAN, JAPN, RUSS, HEBR, CHIN, CZEC, ARAB, WMNS | ||
| Complete 3 credits in “Diversity in U.S. Communities” | 3 | |
Select from a list of approved courses listed in the “CDR: Human Diversity in U.S. Communities” section of the Arts and Sciences section of the catalog at https://catalog.unl.edu/undergraduate/arts-sciences/#text. | ||
| Credit Hours Subtotal: | 6 | |
| TECHNICAL WRITING | ||
| JGEN 200 | Technical Communication I (ACE 1) | 3 |
| or BSAD 220H | Honors Business Writing | |
| Credit Hours Subtotal: | 3 | |
| SCIENCE (ACE 4) | ||
| Select 6 credit hours of courses intended for science or engineering majors including at least one laboratory. Acceptable disciplines and courses are (not an exhaustive list): | 6 | |
| Chemistry | ||
| General Chemistry I and General Chemistry I Laboratory 3 | ||
| General Chemistry II and General Chemistry II Laboratory 3 | ||
| Elementary Quantitative Analysis and Elementary Quantitative Analysis Laboratory 3 | ||
| Fundamental Chemistry I and Fundamental Chemistry I Laboratory 3 | ||
| Fundamental Chemistry II | ||
| Physics and Astronomy | ||
| Elementary General Physics I 3 | ||
| Elementary General Physics II 3 | ||
| General Physics I | ||
| General Physics II | ||
| General Physics Laboratory I 3 | ||
| General Physics Laboratory II 3 | ||
| General Physics III | ||
| General Physics Laboratory III 3 | ||
| Introduction to Astronomy and Astrophysics | ||
| Astronomy and Astrophysics Laboratory 3 | ||
| Biological Sciences | ||
| Introduction to Microbiology and Human Health 3 | ||
| Genetics, Molecular and Cellular Biology Laboratory 3 | ||
| General Genetics | ||
| Ecology and Evolution 3 | ||
| Fundamentals of Biology I and Fundamentals of Biology I laboratory 3 | ||
| Fundamentals of Biology II and Fundamentals of Biology II Laboratory 3 | ||
| Earth and Atmospheric Sciences | ||
| Elements of Physical Geography 3 | ||
| Dynamic Earth 3 | ||
| Earth Through Time 3 | ||
| Geochemistry | ||
| Weather and Climate 3 | ||
| Introduction to Atmospheric Science 3 | ||
| Applied Climatology | ||
| Anthropology | ||
| Introduction to Biological Anthropology | ||
| Introduction to Biological Anthropology Laboratory 3 | ||
| Credit Hours Subtotal: | 6 | |
| ACE Requirements | ||
| Select one course each from ACE outcomes 2, 5, 6, 7, and 9 | 15 | |
| Credit Hours Subtotal: | 15 | |
- 1
ARAB, CHIN, CZEC, FREN, GERM, GREK, HEBR, JAPN, LATN, RUSS, and SPAN courses must be numbered 300 or above. ENGL courses must be 170, 180, or 200 level and above.
- 2
Excluded courses: CLAS 116, ENGL 254, ENGL 300, ENGL 354, SPAN 300A, SPAN 303, and SPAN 304, ANTH 242/ANTH 242L, GEOG 155, GIST 111, GIST 311, POLS 101, POLS 250, PSYC 100, PSYC 273. Internship (395 or 495), independent study or readings (396 or 496), research (398 or 498), and thesis (399, 399H, 499, or 499H) will not satisfy the requirement. Other courses with a 9 in the middle number (ex. PSYC 292) will not satisfy the requirement unless approved by an advisor.
- 3
Indicates a lab course or that a lab is included with the course.
Additional Major Requirements
Grade Rules
C- and D Grades
A grade of C or above is required for all courses in the major (core requirements and focus areas), excluding ancillary courses.
Pass/No Pass
No course taken Pass/No Pass will be counted toward the major (core requirements and focus areas), unless offered exclusively with a grade option of Pass/No Pass.
Course Level Requirement
Thirty (30) of the 120 credit hours must be in courses numbered at the 300 or 400 level. Of those 30 hours, 15 credit hours must be completed in residence at the University of Nebraska–Lincoln.
Residency Requirement
Students must complete at least 30 of the 120 total hours for their degree at the University of Nebraska–Lincoln. Students must complete at least half of their major coursework, including 6 credit hours at the 300 or 400 level in their major, and 15 of the 30 credit hours required at the 300 or 400 level in residence. Credit earned during education abroad may be used toward the residency requirement only if students register through the University of Nebraska–Lincoln.