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, Spring)

Credits: 3. Contact Hours: Lecture 3.

Prereq: Satisfactory math placement test score (ALEKS, 51+)
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 2010, DS 2020 and STAT 1010, STAT 1040, STAT 2010, STAT 2026, STAT 3005, STAT 3022, STAT 3030, or STAT 3031
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 2010, DS 2020, MATH 2070, MATH 2650, and STAT 3201
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
Project-based capstone experience to integrate and apply knowledge and skills gained throughout data science studies. Teamwork to tackle real-world data science problems. Emphasizes entire data science lifecycle — from data acquisition, cleaning, and exploration to model building, evaluation, and effective communication of results. Culminates in a final project, oral presentation, and written report, showcasing technical expertise and professional development. (Typically Offered: Spring)

(Dual-listed with MATH 5220X). (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)