Overview
Data Science is a rapidly growing academic discipline fueled by the proliferation of rich and complex data emerging from activities in science, industry, and governments. As a result, there is strong demand for data science professionals today in Iowa as well as across the nation and globe, and this market is expected to continue to grow in the next decade. The data science programs are intended for students who wish to study the data science discipline for its own sake as well as for students studying any discipline at Iowa State University with the goal of enabling them to work in data science. The courses in the data science program are designed to provide students with the requisite background that would enable them to take jobs with significant data science components, e.g., establishing and operating data analysis pipelines. The capstone will provide an opportunity for students to apply data science concepts to a domain problem while working in a multi-disciplinary team setting.
The Data Science major is intended for students with strong quantitative backgrounds and has the goal of educating students on the technical fundamentals of Data Science, with a focus on developing the knowledge and skills needed to transform data into insights. The major is an excellent opportunity for individuals who want to prepare themselves for the exciting Data Scientist positions that are in high demand today.
The minor in Data Science is intended for students studying any discipline at Iowa State and is designed to give students an in-depth understanding of data science as it is applied to a variety of domains.
The certificate in Data Science is intended for students studying any discipline at Iowa State and is designed to prepare them for future work with significant data science components. The capstone will provide an opportunity for students to apply data science concepts to a domain problem while working in a multi-disciplinary team setting.
Student Learning Outcomes for Data Science Major
After successfully completing the program, students majoring in Data Science will demonstrate:
- An understanding of and an ability to apply the following data science concepts, tools and methods to data analysis pipelines:
- data acquisition
- data preprocessing
- exploratory data analysis
- inferential and predictive thinking, modeling and analysis
- computational thinking, data structures, and algorithms
- An understanding of ethical, legal, societal, and economic concerns in the application of data science concepts.
- An ability to visualize, interpret and communicate the output of data analysis pipelines to stakeholders.
- An ability to function on multi-disciplinary teams using concepts and tools from data science.
See Undergraduate Minor and Undergraduate Certificate subpages for the respective learning outcomes.
Data Science Major
Purpose
The Bachelor of Science degree program in Data Science is intended for students with strong quantitative backgrounds and has the goal of educating students on the technical fundamentals of data sciences, with a focus on developing the knowledge and skills needed to manage and analyze large-scale, heterogeneous data to address a wide range of problems.
Requirements
The B.S. in Data Science consists of 120 total credit hours including: (1) 39 credit hours in the major core, three credits of which constitute a capstone course that is expected to provide experiential learning; (2) 9 credit hours in an application emphasis area to examine applications and theory of data sciences in a specific area; and (3) 23 credit hours of foundation courses. The capstone course will provide an opportunity for students to apply data science concepts to an application area while working in a multi-disciplinary team setting.
Data Science Major Requirements
| Data Science Core Courses | 39 | |
| Orientation to Data Science | ||
| DS 2010 | Introduction to Data Science | 3 |
| DS 2020 | Data Acquisition and Exploratory Data Analysis | 3 |
| DS 3030 | Concepts and Applications of Machine Learning | 3 |
| DS 4010 | Data Science Capstone | 3 |
| COMS 2280 | Introduction to Data Structures | 3 |
| COMS 2300 | Discrete Computational Structures | 3 |
| or CPRE 3100 | Theoretical Foundations of Computer Engineering | |
| COMS 3110 | Introduction to the Design and Analysis of Algorithms | 3 |
| COMS 3630 | Introduction to Database Management Systems | 3 |
| CPRE 4190 | Software Tools for Large Scale Data Analysis | 4 |
| STAT 3010 | Intermediate Statistical Concepts and Methods | 4 |
| STAT 3470 | Probability and Statistical Theory for Data Science | 4 |
| STAT 4770 | Introduction to Categorical Data Analysis | 3 |
At least 9 credits from the following categories to fulfill the emphasis area:
| At least 6 credits from courses at the 3000, 4000, or 5000 level from the following designations: | 6 | |
ABE, ADVRT, AGRON, ANS, ARCH, BCBIO, COMS, CRP, CPRE, CYBE, CYBSC, DS, EE, ECON, FIN, GIS, IE, JLMC, LA, LING, MATH, MIS, MKT, POLS, SE, SOC, STAT, TSM | ||
| At least 3 credits from one of the following courses: | 3 | |
| Bioinformatics of Sequences | ||
| Bioinformatics of OMICS | ||
| Principles of Programming Languages | ||
| Program Analysis | ||
| Logic for Mathematics and Computer Science | ||
| Introduction to High Performance Computing | ||
| Introduction to Parallel Algorithms and Programming | ||
| Algorithms for Large Data Sets: Theory and Practice | ||
| Principles and Practice of Compiling | ||
| Distributed Systems | ||
| Principles and Internals of Database Systems | ||
| Introduction to Machine Learning | ||
| Embedded Systems II: Mobile Platforms | ||
| Software Evolution and Maintenance | ||
| High Performance Computing for Scientific and Engineering Applications | ||
| Basics of Information System Security | ||
| Machine learning: A Signal Processing Perspective | ||
| Optimization | ||
| Data Mining | ||
| Big Data Analytics and Optimization | ||
| Introduction to Scientific Computing | ||
| Applied Linear Algebra | ||
| Introduction to High Performance Computing | ||
| Numerical Methods for Differential Equations | ||
| Introduction to Experimental Design | ||
| Introduction to Survey Sampling | ||
| Introduction to Multivariate Data Analysis | ||
| Total Credits | 9 | |
Toward satisfying pre-requisites and requirements of the College of Liberal Arts and Sciences, the following courses or their equivalents are also required:
| COMS 1270 | Introduction to Computer Programming | 3 |
| COMS 2270 | Object-oriented Programming | 4 |
| MATH 1650 | Calculus I | 4 |
| MATH 1660 | Calculus II | 4 |
| MATH 2650 | Calculus III | 4 |
| MATH 2070 | Matrices and Linear Algebra | 3 |
| STAT 2010 | Introduction to Statistical Concepts and Methods | 4 |
| World Language 3 years in high school or 1 year in college | 0 - 8 | |
| Natural Science | 8 | |
| Social Science | 9 | |
| Arts and Humanities | 12 | |
| LAS 2030 | Professional Career Preparation | 1 |
The following courses meet the communication proficiency requirement:
| LIB 1600 | Introduction to College Level Research | 1 |
| ENGL 1500 | Critical Thinking and Communication | 3 |
| ENGL 2500 | Written, Oral, Visual, and Electronic Composition | 3 |
| One of the following: | ||
| ENGL 3020 | Business Communication | 3 |
| ENGL 3140 | Technical Communication | 3 |
| ENGL 3320 | Visual Communication of Quantitative Information (cross-listed as STAT 3320) | 3 |
As majors in the College of Liberal Arts and Sciences, Data Science students must meet College of Liberal Arts and Sciences and University-wide requirements for graduation in addition to those stated above for the major.
LAS majors require a minimum of 120 credits, including a minimum of 45 credits at the 3000/4000 level or above taken at a four-year college. All such credits, including courses taken on a pass/not pass basis, may be used to meet this requirement. You must also complete the LAS world language requirement and career proficiency requirement.
Students in all ISU majors must complete a three-credit course in U.S. diversity and a three-credit course in international perspectives. Check (http://www.registrar.iastate.edu/courses/div-ip-guide.html) for a list of approved courses. Discuss with your advisor how the two courses that you select can be applied to your graduation plan.
According to the university-wide Communication Proficiency Grade Requirement, students must demonstrate their communication proficiency by earning a grade of C or better in ENGL 2500. The Data Science program requires a C or higher in the upper-level ENGL course (3020, 3140, or 3320).
Four Year Plan
B.S., Data Science
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| DS 1100 | R | MATH 1660 | 4 |
| MATH 1650 | 4 | COMS 2270 | 4 |
| COMS 1270 | 3 | STAT 2010 | 4 |
| ENGL 1500 | 3 | ENGL 2500 | 3 |
| LIB 1600 | 1 | ||
| Social Science | 3 | ||
| 14 | 15 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| DS 2010 | 3 | DS 2020 | 3 |
| MATH 2650 | 4 | MATH 2070 | 3 |
| COMS 2280 | 3 | COMS 2300 or CPRE 3100 | 3 |
| STAT 3010 | 4 | Social Science/International Perspectives | 3 |
| Arts and Humanities/U.S. Diversity | 3 | Arts and Humanities | 3 |
| LAS 2030 | 1 | ||
| 17 | 16 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| DS 3030 | 3 | COMS 3630 | 3 |
| STAT 3470 | 4 | STAT 4770 | 3 |
| COMS 3110 | 3 | Arts and Humanities | 3 |
| Arts and Humanities (3000+ level) | 3 | Natural Science | 4 |
| Elective or World Language | 3-4 | Elective or World Language | 3-4 |
| 16-17 | 16-17 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| Application Emphasis Area | 3 | DS 4010 | 3 |
| Application Emphasis Area | 3 | CPRE 4190 | 4 |
| ENGL 3020, 3140, or 3320 | 3 | Application Emphasis Area | 3 |
| Natural Science | 4 | Social Science (3000+ Level) | 3 |
| 13 | 13 | ||
The major elective courses will come from Data Science emphasis areas as outlined on the Undergraduate Major page. A student must take at least 9 credits of 3000+ courses from a wide variety of designations.
Additionally, 3 credits must be chosen from a list of elective courses with a data or analysis emphasis.
All students are required to take at least 45 hours of courses at the 3000+ level or above. This may require taking additional electives.
Data Science Minor
Purpose
The minor in data science is intended for students studying any discipline at Iowa State and is designed to give students an in-depth understanding of data science as it is applied to a variety of domains. The minor in data science will prepare students with the technical and communication skills to enter the workforce as domain experts with data science skills.
Learning Outcomes for Data Science Minor
After completing the minor in data science, students will demonstrate:
- An ability to apply data science concepts, tools and technologies to data analysis pipelines,
- An understanding of ethical, legal, societal, and economic concerns in application of data science concepts,
- An ability to visualize, interpret and communicate the output of data analysis pipelines to stakeholders, and
- An ability to function on multi-disciplinary teams using concepts and tools from data science.
Requirements
The minor in data science requires the completion of 15 credit hours, including 9 credits from the data science core and 6 credits from approved data science electives.
At least 6 credits in courses numbered 3000-level or above taken at ISU with a grade of C or higher.
At least 9 credits used for the minor cannot be used to meet any other department, college or university requirement for the baccalaureate degree except to satisfy the total credit requirement for graduation and to meet credit requirements in courses numbered 3000 or above.
Courses for the minor cannot be taken on a pass/not-pass basis.
Course Requirements for Data Science Minor
| Core Courses (9 credits) | ||
| DS 2010 | Introduction to Data Science (Required) | 3 |
| DS 2020 | Data Acquisition and Exploratory Data Analysis (Required) | 3 |
| DS 3010 | Applied Data Modeling and Predictive Analysis (Required) | 3 |
| * DS 3010 has a prerequisite of an introductory statistics course: STAT 1010, STAT 1040, STAT 1050, STAT 2010, STAT 2260, STAT 2310, STAT 3050, STAT 3220, or STAT 3300. | ||
| Electives (6 credits) | ||
| ABE 3160 | Applied Numerical Methods for Agricultural and Biosystems Engineering | 3 |
| ADVRT 3350 | Advertising Media Planning | 3 |
| ADVRT 4970 | Special Topics in Communication | 1-3 |
| AGRON 2700 | Geospatial Technologies | 3 |
| AGRON 4250 | Crop and Soil Modeling | 3 |
| BCBIO 3220 | Introduction to Bioinformatics and Computational Biology | 3 |
| COMS 3110 | Introduction to the Design and Analysis of Algorithms | 3 |
| COMS 3630 | Introduction to Database Management Systems | 3 |
| COMS 4240 | Introduction to High Performance Computing | 3 |
| COMS 4350 | Algorithms for Large Data Sets: Theory and Practice | 3 |
| COMS 4530 | Privacy Preserving Algorithms and Data Security | 3 |
| COMS 4740 | Introduction to Machine Learning | 3 |
| CRP 2510 | Fundamentals of Geographic Information Systems | 3 |
| CRP 3510 | Intermediate Geographic Information Systems | 3 |
| CRP 4520 | Geographic Data Management and Planning Analysis | 3 |
| CRP 4540 | Fundamentals of Remote Sensing and Spatial Analysis | 3 |
| CRP 4560 | GIS Programming and Automation | 3 |
| CPRE 4190 | Software Tools for Large Scale Data Analysis | 4 |
| CPRE 4260 | Introduction to Parallel Algorithms and Programming | 4 |
| ECON 3710 | Introductory Econometrics | 4 |
| EE 4280X | Image Analysis using Machine Learning | 3 |
| ENGL 3320 | Visual Communication of Quantitative Information | 3 |
| FIN 4500 | Analytical Methods in Finance | 3 |
| IE 3120 | Optimization | 3 |
| IE 4830 | Data Mining | 3 |
| LA 5580 | Web Mapping and Spatial Data Visualization | 3 |
| LING 4100 | Language as Data | 3 |
| MATH 3040 | Combinatorics | 3 |
| MATH 3140 | Graph Theory | 3 |
| MATH 3730 | Introduction to Scientific Computing | 3 |
| MATH 4220X | Mathematical Principles of Data Science | 3 |
| MIS 4360 | Introduction to Business Analytics | 3 |
| MIS 4460 | Advanced Business Analytics | 3 |
| MKT 3680 | Marketing Analytics | 3 |
| STAT 3010 | Intermediate Statistical Concepts and Methods | 4 |
| STAT 3300 | Probability and Statistics for Computer Science | 3 |
| STAT 4750 | Introduction to Multivariate Data Analysis | 3 |
| STAT 4770 | Introduction to Categorical Data Analysis | 3 |
| STAT 4830 | Empirical Methods for the Computational Sciences | 3 |
| STAT 4860 | Introduction to Statistical Computing | 3 |
| TSM 4330 | Precision Agriculture | 3 |
Data Science Certificate
Purpose
The certificate in data science is intended for students studying any discipline at Iowa State and is designed to prepare them for future work with significant data science components. The data science certificate is also available to students who have already earned a Baccalaureate degree from Iowa State or elsewhere. The capstone will provide an opportunity for students to apply data science concepts to a domain problem while working in a multi-disciplinary team setting. The certificate in data science will prepare students with the technical and communication skills to enter the workforce as domain experts with data science skills.
Learning Outcomes for Data Science Certificate
After completing the certificate in data science, students will demonstrate:
- An ability to apply data science concepts, tools and technologies to data analysis pipelines,
- An understanding of ethical, legal, societal, and economic concerns in application of data science concepts,
- An ability to visualize, interpret and communicate the output of data analysis pipelines to stakeholders, and
- An ability to function on multi-disciplinary teams using concepts and tools from data science.
Requirements
The certificate in data science requires the completion of 21 credit hours, including 9 credits from the data science core, 9 credits from approved data science electives, and a three-credit data science capstone experience.
At least 9 credits must be taken in courses numbered at the 3000-level or above.
At least 9 credits used for the certificate cannot be used to meet any other department, college or university requirement for the baccalaureate degree except to satisfy the total credit requirement for graduation and to meet credit requirements in courses numbered 3000 or above.
Courses for the certificate cannot be taken on a pass/not-pass basis.
Course Requirements for Data Science Certificate
| Core Courses (9 credits) | ||
| DS 2010 | Introduction to Data Science (Required) | 3 |
| DS 2020 | Data Acquisition and Exploratory Data Analysis (Required) | 3 |
| DS 3010 | Applied Data Modeling and Predictive Analysis (Required) | 3 |
| * DS 3010 has a prerequisite of an introductory statistics course: STAT 1010, STAT 1040, STAT 1050, STAT 2010, STAT 2260, STAT 2310, STAT 3050, STAT 3220, or STAT 3300. | ||
| Electives (9 credits) | ||
| ABE 3160 | Applied Numerical Methods for Agricultural and Biosystems Engineering | 3 |
| ADVRT 3350 | Advertising Media Planning | 3 |
| ADVRT 4970 | Special Topics in Communication | 1-3 |
| BCBIO 3220 | Introduction to Bioinformatics and Computational Biology | 3 |
| COMS 3110 | Introduction to the Design and Analysis of Algorithms | 3 |
| COMS 3630 | Introduction to Database Management Systems | 3 |
| COMS 4240 | Introduction to High Performance Computing | 3 |
| COMS 4350 | Algorithms for Large Data Sets: Theory and Practice | 3 |
| COMS 4530 | Privacy Preserving Algorithms and Data Security | 3 |
| COMS 4740 | Introduction to Machine Learning | 3 |
| CRP 2510 | Fundamentals of Geographic Information Systems | 3 |
| CRP 3510 | Intermediate Geographic Information Systems | 3 |
| CRP 4520 | Geographic Data Management and Planning Analysis | 3 |
| CRP 4560 | GIS Programming and Automation | 3 |
| CPRE 4190 | Software Tools for Large Scale Data Analysis | 4 |
| CPRE 4260 | Introduction to Parallel Algorithms and Programming | 4 |
| ECON 3710 | Introductory Econometrics | 4 |
| ENGL 3320 | Visual Communication of Quantitative Information | 3 |
| FIN 4500 | Analytical Methods in Finance | 3 |
| IE 3120 | Optimization | 3 |
| IE 4830 | Data Mining | 3 |
| LING 4100 | Language as Data | 3 |
| MATH 3040 | Combinatorics | 3 |
| MATH 3140 | Graph Theory | 3 |
| MATH 3730 | Introduction to Scientific Computing (MATH 4220X::Mathematical Principals of Data Science) | 3 |
| MATH 4220X | Mathematical Principles of Data Science | 3 |
| MIS 4360 | Introduction to Business Analytics (::Mathematical Principals of Data Science) | 3 |
| MIS 4460 | Advanced Business Analytics | 3 |
| MKT 3680 | Marketing Analytics | 3 |
| STAT 3010 | Intermediate Statistical Concepts and Methods | 4 |
| STAT 3300 | Probability and Statistics for Computer Science | 3 |
| STAT 4750 | Introduction to Multivariate Data Analysis | 3 |
| STAT 4770 | Introduction to Categorical Data Analysis | 3 |
| STAT 4830 | Empirical Methods for the Computational Sciences | 3 |
| STAT 4860 | Introduction to Statistical Computing | 3 |
| Data Science capstone experience (3 credits) | ||
| DS 4010 | Data Science Capstone | 3 |