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Data Science

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.

Data Science Major

​Purpose

This Bachelor’s 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. 

​Learning Outcomes

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 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

Requirements

The B.S. in Data Science consists of 120 total credit hours including: (1) 39 credits 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 one of seven elective tracks 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
DS 110Orientation to Data ScienceR
DS 201Introduction to Data Science3
DS 202Data Acquisition and Exploratory Data Analysis3
DS 303Concepts and Applications of Machine Learning3
DS 401Data Science Capstone3
COM S 228Introduction to Data Structures3
COM S 230Discrete Computational Structures3
or CPR E 310 Theoretical Foundations of Computer Engineering
COM S 311Introduction to the Design and Analysis of Algorithms3
COM S 363Introduction to Database Management Systems3
CPR E 419Software Tools for Large Scale Data Analysis4
STAT 301Intermediate Statistical Concepts and Methods4
STAT 347Probability and Statistical Theory for Data Science4
STAT 457Applied Categorical Data Analysis3

At least 9 credits from any ONE of the following seven application emphasis areas:

Big Data9-10
COM S 424Introduction to High Performance Computing3
COM S 426Introduction to Parallel Algorithms and Programming4
COM S 435Algorithms for Large Data Sets: Theory and Practice3
COM S 454Distributed Systems3
COM S 461Principles and Internals of Database Systems3
COM S 474Introduction to Machine Learning3
Engineering Applications10
CPR E 388Embedded Systems II: Mobile Platforms4
CPR E 425High Performance Computing for Scientific and Engineering Applications (cross-listed as COM S 425)3
E E 425 Machine Learning: A Signal Perspective3
Optimization9
I E 312Optimization3
I E 483Data Mining3
I E 487Big Data Analytics and Optimization3
Security9
COM S 421Logic for Mathematics and Computer Science3
COM S 453Privacy Preserving Algorithms and Data Security3
CPR E 431Basics of Information System Security3
Software Analytics9
COM S 342Principles of Programming Languages3
COM S 413Foundations and Applications of Program Analysis3
COM S 440Principles and Practice of Compiling3
COM S 474Introduction to Machine Learning3
CPR E 416Software Evolution and Maintenance3
Statistics9
STAT 402Statistical Design and the Analysis of Experiments3
STAT 407Methods of Multivariate Analysis3
STAT 421Survey Sampling Techniques3
COM S 474Introduction to Machine Learning3
Computational Biology10
BCBIO 322Introduction to Bioinformatics and Computational Biology3
BCBIO 402Fundamentals of Systems Biology and Network Science3
BCBIO 444Bioinformatic Analysis4

Toward satisfying requirements of the College of Liberal Arts and Sciences, the following courses should be included:

COM S 227Object-oriented Programming4
MATH 165Calculus I4
MATH 166Calculus II4
MATH 265Calculus III4
MATH 207Matrices and Linear Algebra3
STAT 201Introduction to Statistical Concepts and Methods4
Foreign Language 3 years in high school or 1 year in college0 - 8
Natural Science8
Social Science9
Arts and Humanities12

The following courses meet the communication proficiency requirement:

LIB 160Information Literacy1
ENGL 150Critical Thinking and Communication3
ENGL 250Written, Oral, Visual, and Electronic Composition3
One of the following:
ENGL 302Business Communication3
ENGL 314Technical Communication3
ENGL 332Visual Communication of Quantitative Information (cross-listed as STAT 332)3

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 250. The Data Science program requires a C or higher in the upper-level ENGL course (302, 314, or 332).

All students must complete 3 credits of US Diversity and 3 credits of International Perspective courses.

To obtain a bachelor's degree from the College of Liberal Arts and Sciences, curriculum in liberal arts and sciences, a student must earn at least 45 credits at the 300 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.

B.S., Data Science

Freshman
FallCreditsSpringCredits
DS 110RCOM S 2283
ENGL 1503MATH 1664
LIB 1601STAT 2014
MATH 1654Arts and Humanities3
COM S 2274 
Social Science3 
 15 14
Sophomore
FallCreditsSpringCredits
ENGL 2503DS 2023
DS 2013STAT 3014
COM S 230 or CPR E 3103MATH 2073
MATH 2654Social Science3
Natural Science4Arts and Humanities3
 17 16
Junior
FallCreditsSpringCredits
ENGL 302 or 3143DS 3033
STAT 3474STAT 4573
COM S 3113CPR E 4194
COM S 3633Arts and Humanities3
Elective or Foreign Language3-4Elective or Foreign Language3-4
 16-17 16-17
Senior
FallCreditsSpringCredits
Major Elective3DS 4013
Major Elective3Major Elective3
Arts and Humanities3Social Science3
Natural Science4Electives 300+4-6
 13 13-15

The major elective courses will come from any one application emphasis area as outlined on the Undergraduate Major page. A student must take at least 9 credits from any single application emphasis area and may choose from: Big Data; Engineering Applications; Optimization; Security; Software Analytics; Statistics; and Computational Biology.

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

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 must be taken in courses numbered at the 300-level or above.

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 300 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 201Introduction to Data Science (Required)3
DS 202Data Acquisition and Exploratory Data Analysis (Required)3
DS 301Applied Data Modeling and Predictive Analysis (Required)3
* DS 301 has a prerequisite of an introductory statistics course: STAT 101, STAT 104, STAT 105, STAT 201, STAT 226, STAT 231, STAT 305, STAT 322, or STAT 330.
Electives (6 credits)
A B E 316Applied Numerical Methods for Agricultural and Biosystems Engineering3
BCBIO 322Introduction to Bioinformatics and Computational Biology3
COM S 311Introduction to the Design and Analysis of Algorithms3
COM S 363Introduction to Database Management Systems3
COM S 424Introduction to High Performance Computing3
COM S 435Algorithms for Large Data Sets: Theory and Practice3
COM S 453XPrivacy Preserving Algorithms and Data Security3
COM S 474Introduction to Machine Learning3
C R P 251XIntroduction to Geographic Information Systems3
C R P 351XIntermediate Geographic Information Systems3
C R P 452Geographic Data Management and Planning Analysis3
C R P 456GIS Programming and Automation3
CPR E 419Software Tools for Large Scale Data Analysis4
CPR E 426Introduction to Parallel Algorithms and Programming4
ECON 371Introductory Econometrics4
ENGL 332Visual Communication of Quantitative Information3
FIN 450XAnalytical Finance3
I E 312Optimization3
I E 483Data Mining3
LING 410XLanguage as Data3
MIS 436Introduction to Business Analytics3
MIS 446Advanced Business Analytics3
MKT 368Marketing Analytics3
STAT 301Intermediate Statistical Concepts and Methods4
STAT 330Probability and Statistics for Computer Science3
STAT 407Methods of Multivariate Analysis3
STAT 430Empirical Methods for the Computational Sciences3
STAT 457Applied Categorical Data Analysis3
STAT 480Statistical Computing Applications3

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

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 300-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 300 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 201Introduction to Data Science (Required)3
DS 202Data Acquisition and Exploratory Data Analysis (Required)3
DS 301Applied Data Modeling and Predictive Analysis (Required)3
* DS 301 has a prerequisite of an introductory statistics course: STAT 101, STAT 104, STAT 105, STAT 201, STAT 226, STAT 231, STAT 305, STAT 322, or STAT 330.
Electives (9 credits)
A B E 316Applied Numerical Methods for Agricultural and Biosystems Engineering3
BCBIO 322Introduction to Bioinformatics and Computational Biology3
COM S 311Introduction to the Design and Analysis of Algorithms3
COM S 363Introduction to Database Management Systems3
COM S 424Introduction to High Performance Computing3
COM S 435Algorithms for Large Data Sets: Theory and Practice3
COM S 453XPrivacy Preserving Algorithms and Data Security3
COM S 474Introduction to Machine Learning3
C R P 251XIntroduction to Geographic Information Systems3
C R P 351XIntermediate Geographic Information Systems3
C R P 452Geographic Data Management and Planning Analysis3
C R P 456GIS Programming and Automation3
CPR E 419Software Tools for Large Scale Data Analysis4
CPR E 426Introduction to Parallel Algorithms and Programming4
ECON 371Introductory Econometrics4
ENGL 332Visual Communication of Quantitative Information3
FIN 450XAnalytical Finance3
I E 312Optimization3
I E 483Data Mining3
LING 410XLanguage as Data3
MIS 436Introduction to Business Analytics3
MIS 446Advanced Business Analytics3
MKT 368Marketing Analytics3
STAT 301Intermediate Statistical Concepts and Methods4
STAT 330Probability and Statistics for Computer Science3
STAT 407Methods of Multivariate Analysis3
STAT 430Empirical Methods for the Computational Sciences3
STAT 457Applied Categorical Data Analysis3
STAT 480Statistical Computing Applications3
Data Science capstone experience (3 credits)
DS 401Data Science Capstone3

Expand all courses

Courses

Courses primarily for undergraduates:

Cr. R.


Introduction to the procedures and policies of Iowa State University and the Data Science program, test-outs, honorary societies, etc. Issues relevant to student adjustment to college life will also be discussed. Offered on a satisfactory-fail basis only.

Cr. 3. Alt. F., offered irregularly.Alt. S., offered irregularly.

Prereq: 1-1/2 Years of High School Algebra
Data Science concepts and their applications; domain case studies with applications in various fields; overview of data analysis; major components of data analysis pipelines; computing concepts for data science; descriptive data analysis; hands-on data analysis experience; communicating findings to stakeholders, and ethical issues in data science.

Cr. 3. Alt. F., offered irregularly.Alt. S., offered irregularly.

Prereq: DS 201
Data acquisition: file structures, web-scraping, database access; ethical aspects of data acquisition; types of data displays; numerical and visual summaries of data; pipelines for data analysis: filtering, transformation, aggregation, visualization and (simple) modeling; good practices of displaying data; data exploration cycle; graphics as tools of data exploration; strategies and techniques for data visualizations; basics of reproducibility and repeatability; web-based interactive applets for visual presentation of data and results. Programming exercises.

Cr. 3. Alt. F., offered irregularly.Alt. S., offered irregularly.

Prereq: DS 201, one of STAT 101, 104, 105, 201, 226, 231, 305, 322, 330
Elements of predictive analysis such as training and test sets; feature extraction; survey of algorithmic machine learning techniques, e.g. decision trees, Naïve Bayes, and random forests; survey of data modeling techniques, e.g. linear model and regression analysis; assessment and diagnostics: overfitting, error rates, residual analysis, model assumptions checking; communicating findings to stakeholders in written, oral, verbal and electronic form, and ethical issues in data science. Participation in a multi-disciplinary team project.

Cr. 3.

Prereq: DS 201
Machine learning concepts such as training and test sets; feature extraction; principles of machine learning techniques; regression; pattern recognition methods; unsupervised learning techniques; assessment and diagnostics: overfitting, error rates, residual analysis, model assumptions checking, feature selection; ethical issues in data science; communicating findings to stakeholders in written, oral, visual and electronic form.

Cr. 3. Alt. F., offered irregularly.Alt. S., offered irregularly.

Prereq: DS 202X; DS 301X
Students work as individuals and teams to complete the planning, design, and implementation of a significant multi-disciplinary project in data science. Oral and written reports.