CS 4630 : Python for Computational and Data Sciences

CS 4630: Python for Computational and Data Sciences

Semester Hours:   3.0
Contact Hours:    3
Coordinator:   Ray Kresman
Text:   Various
Author(s):   VARIOUS
Year:   Various

SPECIFIC COURSE INFORMATION

Catalog Description

Accelerated introduction to Python. Sample problems in STEM domains and computational approaches to solving them. Generic, and domain-specific libraries and tools. Introduction to data variety, analysis, and visualization. Prerequisite: MATH 1310 and C or better in CS 1010 or CS 2010 or consent of instructor. Cannot earn credit for both CS 4630 and CS 5630.

Course type: ELECTIVE

SPECIFIC COURSE GOALS

• I can use language libraries to solve basic computational problems in STEM domain [examples: a) sequence alignment and use of STEM datasets; b) scripting in STEM applications; c) hypothesis testing and optimization].
• I can explain language mechanisms for handling missing data, and cite sample STEM applications where missing data is prevalent.
• I can use basic visualization and data classification on STEM datasets.
• I can explain certain data formats in STEM fields.
• I can use the primitives in certain libraries, for example: Numpy, Scipy, BiopythonSympy, Pyomo, Mathplotlib, Pandas.

LIST OF TOPICS COVERED

• Accelerated introduction to Python (~ 15%)
• Datasets in the sciences (~ 10%)
  • Data formats in STEM fields, examples: atmospheric science, biology
  • Missing data - for example, radar measurements
  • Data wrangling and analysis
• Applications - Math & Physics (~ 15%)
  • Matrix operations & ODE
  • Projectile motion and simple harmonic motion
  • Optimization
• Applications - Geology/Hydrology/GIS (~ 15%)
  • Raster & vector data
  • Line and contour plots
  • Basics of filtering and noise reduction
  • Process map layers and time series data
• Applications - Psychology and Statistics (~ 15%)
  • Descriptive and inferential statistics,
  • Models & hypothesis
  • Significance and hypothesis testing
• Applications - Chemistry/Biology/CS (~ 25%)
  • Chemical equations, stoichiometry
  • Bioinformatics and sequence alignment
  • Dynamic programming
  • Data and spatial visualization
  • Data science programming