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Data Science

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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 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 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 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 311Design 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 483Knowledge Discovery and Data Mining3
LING 410XLanguage as Data3
MIS 436Introduction to Business Analytics3
MIS 446Advanced Business Analytics3
MKT 368Spreadsheet-based Marketing 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 311Design 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 483Knowledge Discovery and Data Mining3
LING 410XLanguage as Data3
MIS 436Introduction to Business Analytics3
MIS 446Advanced Business Analytics3
MKT 368Spreadsheet-based Marketing 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. 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. 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.