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Data Science

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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:

  1. An understanding of and an ability to apply the following data science concepts, tools and methods to data analysis pipelines:
    1. ​data acquisition
    2. data preprocessing
    3. exploratory data analysis
    4. inferential and predictive thinking, modeling and analysis
    5. computational thinking, data structures, and algorithms
  2. An understanding of ethical, legal, societal, and economic concerns in the application of data science concepts.
  3. An ability to visualize, interpret and communicate the output of data analysis pipelines to stakeholders.
  4. 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 Courses39
Orientation to Data Science
DS 2010Introduction to Data Science3
DS 2020Data Acquisition and Exploratory Data Analysis3
DS 3030Concepts and Applications of Machine Learning3
DS 4010Data Science Capstone3
COMS 2280Introduction to Data Structures3
COMS 2300Discrete Computational Structures3
or CPRE 3100 Theoretical Foundations of Computer Engineering
COMS 3110Introduction to the Design and Analysis of Algorithms3
COMS 3630Introduction to Database Management Systems3
CPRE 4190Software Tools for Large Scale Data Analysis4
STAT 3010Intermediate Statistical Concepts and Methods4
STAT 3470Probability and Statistical Theory for Data Science4
STAT 4770Introduction to Categorical Data Analysis3

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 Credits9

Toward satisfying pre-requisites and requirements of the College of Liberal Arts and Sciences, the following courses or their equivalents are also required:

COMS 1270Introduction to Computer Programming3
COMS 2270Object-oriented Programming4
MATH 1650Calculus I4
MATH 1660Calculus II4
MATH 2650Calculus III4
MATH 2070Matrices and Linear Algebra3
STAT 2010Introduction to Statistical Concepts and Methods4
World Language 3 years in high school or 1 year in college0 - 8
Natural Science8
Social Science9
Arts and Humanities12
LAS 2030Professional Career Preparation1

The following courses meet the communication proficiency requirement:

LIB 1600Introduction to College Level Research1
ENGL 1500Critical Thinking and Communication3
ENGL 2500Written, Oral, Visual, and Electronic Composition3
One of the following:
ENGL 3020Business Communication3
ENGL 3140Technical Communication3
ENGL 3320Visual 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. cultures and communities (formerly 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
FallCreditsSpringCredits
DS 1100RMATH 16604
MATH 16504COMS 22704
COMS 12703STAT 20104
ENGL 15003ENGL 25003
LIB 16001 
Social Science3 
 14 15
Sophomore
FallCreditsSpringCredits
DS 20103DS 20203
MATH 26504MATH 20703
COMS 22803COMS 2300 or CPRE 31003
STAT 30104Social Science/International Perspectives3
Arts and Humanities/U.S. Cultures and Communities (formerly U.S. Diversity)3Arts and Humanities3
 LAS 20301
 17 16
Junior
FallCreditsSpringCredits
DS 30303COMS 36303
STAT 34704STAT 47703
COMS 31103Arts and Humanities3
Arts and Humanities (3000+ level)3Natural Science4
Elective or World Language3-4Elective or World Language3-4
 16-17 16-17
Senior
FallCreditsSpringCredits
Application Emphasis Area3DS 40103
Application Emphasis Area3CPRE 41904
ENGL 3020, 3140, or 33203Application Emphasis Area3
Natural Science4Social 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 2010Introduction to Data Science (Required)3
DS 2020Data Acquisition and Exploratory Data Analysis (Required)3
DS 3010Applied 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 3160Applied Numerical Methods for Agricultural and Biosystems Engineering3
ADVRT 3350Advertising Media Planning3
ADVRT 4970Special Topics in Communication1-3
AGRON 2700Geospatial Technologies3
AGRON 4250Crop and Soil Modeling3
BCBIO 3220Introduction to Bioinformatics and Computational Biology3
COMS 3110Introduction to the Design and Analysis of Algorithms3
COMS 3630Introduction to Database Management Systems3
COMS 4240Introduction to High Performance Computing3
COMS 4350Algorithms for Large Data Sets: Theory and Practice3
COMS 4530Privacy Preserving Algorithms and Data Security3
COMS 4740Introduction to Machine Learning3
CRP 2510Fundamentals of Geographic Information Systems3
CRP 3510Intermediate Geographic Information Systems3
CRP 4520Geographic Data Management and Planning Analysis3
CRP 4540Fundamentals of Remote Sensing and Spatial Analysis3
CRP 4560GIS Programming and Automation3
CPRE 4190Software Tools for Large Scale Data Analysis4
CPRE 4260Introduction to Parallel Algorithms and Programming4
ECON 3710Introductory Econometrics4
EE 4280XImage Analysis using Machine Learning3
ENGL 3320Visual Communication of Quantitative Information3
FIN 4500Analytical Methods in Finance3
IE 3120Optimization3
IE 4830Data Mining3
LA 5580Web Mapping and Spatial Data Visualization3
LING 4100Language as Data3
MATH 3040Combinatorics3
MATH 3140Graph Theory3
MATH 3730Introduction to Scientific Computing3
MATH 4220XMathematical Principles of Data Science3
MIS 4360Introduction to Business Analytics3
MIS 4460Advanced Business Analytics3
MKT 3680Marketing Analytics3
STAT 3010Intermediate Statistical Concepts and Methods4
STAT 3300Probability and Statistics for Computer Science3
STAT 4750Introduction to Multivariate Data Analysis3
STAT 4770Introduction to Categorical Data Analysis3
STAT 4830Empirical Methods for the Computational Sciences3
STAT 4860Introduction to Statistical Computing3
TSM 4330Precision Agriculture3

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 2010Introduction to Data Science (Required)3
DS 2020Data Acquisition and Exploratory Data Analysis (Required)3
DS 3010Applied 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 3160Applied Numerical Methods for Agricultural and Biosystems Engineering3
ADVRT 3350Advertising Media Planning3
ADVRT 4970Special Topics in Communication1-3
BCBIO 3220Introduction to Bioinformatics and Computational Biology3
COMS 3110Introduction to the Design and Analysis of Algorithms3
COMS 3630Introduction to Database Management Systems3
COMS 4240Introduction to High Performance Computing3
COMS 4350Algorithms for Large Data Sets: Theory and Practice3
COMS 4530Privacy Preserving Algorithms and Data Security3
COMS 4740Introduction to Machine Learning3
CRP 2510Fundamentals of Geographic Information Systems3
CRP 3510Intermediate Geographic Information Systems3
CRP 4520Geographic Data Management and Planning Analysis3
CRP 4560GIS Programming and Automation3
CPRE 4190Software Tools for Large Scale Data Analysis4
CPRE 4260Introduction to Parallel Algorithms and Programming4
ECON 3710Introductory Econometrics4
ENGL 3320Visual Communication of Quantitative Information3
FIN 4500Analytical Methods in Finance3
IE 3120Optimization3
IE 4830Data Mining3
LING 4100Language as Data3
MATH 3040Combinatorics3
MATH 3140Graph Theory3
MATH 3730Introduction to Scientific Computing (MATH 4220X::Mathematical Principals of Data Science)3
MATH 4220XMathematical Principles of Data Science3
MIS 4360Introduction to Business Analytics (::Mathematical Principals of Data Science)3
MIS 4460Advanced Business Analytics3
MKT 3680Marketing Analytics3
STAT 3010Intermediate Statistical Concepts and Methods4
STAT 3300Probability and Statistics for Computer Science3
STAT 4750Introduction to Multivariate Data Analysis3
STAT 4770Introduction to Categorical Data Analysis3
STAT 4830Empirical Methods for the Computational Sciences3
STAT 4860Introduction to Statistical Computing3
Data Science capstone experience (3 credits)
DS 4010Data Science Capstone3