Data Science (DS)

Credits: Required. Contact Hours: Lecture 1.

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. (Typically Offered: Fall)

Credits: 3. Contact Hours: Lecture 2, Laboratory 2.

Prereq: Satisfactory math placement test score (ALEKS, 51+)
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. Placement scores can be found at: https://math.iastate.edu/academics/undergraduate/aleks/placement/. Typically Offered: Fall (annually), Spring (annually), Summer (irregularly).

Credits: 3. Contact Hours: Lecture 3.

Prereq: DS 2010
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. (Typically Offered: Fall, Spring)

Credits: 3. Contact Hours: Lecture 3.

Prereq: DS 2020 and STAT 1010, STAT 1040, STAT 1050, STAT 2010, STAT 2260, STAT 2310, STAT 3050, STAT 3220 or STAT 3300
Elements of predictive analysis such as training and test sets; feature extraction; survey of algorithmic machine learning techniques, e.g. decision trees, Naive 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. (Typically Offered: Fall, Spring)

Credits: 3. Contact Hours: Lecture 3.

Prereq: DS 2020, MATH 2070, MATH 2650, and STAT 3010
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. (Typically Offered: Fall)

Credits: 1-3. Contact Hours: Lecture 3.
Repeatable, maximum of 6 credits.

Prereq: Instructor Permission for Course
Lecture/seminar on special topics in Data Science. (Typically Offered: Fall, Spring, Summer)

Credits: 3. Contact Hours: Lecture 3.

Prereq: DS 3010 or DS 3030
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. Offered irregularly. (Typically Offered: Fall, Spring)

(Cross-listed with MATH 4220X).
Credits: 3. Contact Hours: Lecture 3.

Mathematical foundations of algorithms in data science. Topics include Riemann-Stieltjes integration, Riesz-Markov theorem, Stone-Weierstrass theorem, Universal Approximation theorem, reproducing kernel Hilbert spaces, Cauchy and Fourier kernels, convergence of clustering algorithms, and topological persistence. (Typically Offered: Spring)