Lecture, four hours; discussion, two hours. Preparation: familiarity with programming and algorithms, probability, statistics, linear algebra. Study offers background in mathematical and engineering foundations that are building blocks of data science. Topics include linear algebra, probability, and statistics. Overview of science software engineering and reproducibility fundamentals including working on a compute cluster, pipeline development, virtual notebooks, version control. Letter grading.