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This introductory course, offered by the Department of Scientific Computing at Florida State University, teaches Python to undergraduates. It covers Python basics, including loops, lists, functions, and classes. Additionally, it explores popular packages like NumPy, SciPy, Pandas, and Matplotlib, emphasizing Python's role in solving scientific comp

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ISC-3313 Introduction to Scientific Computing

All content for this course can be viewed online using the link above.

Course Information

Time: Tu/Th 11:30am - 12:45pm

Location: DSL 0152

Dates: May 15 – August 4 (12 weeks)

Instructor Information

Name: Pankaj Chouhan

Email: pc19d@fsu.edu

Course Outline

We will cover the basics of programming with python as well as some more advanced features. The topics will be broken up into four modules as such:

  • Python Fundamentals

    • Introduction, Installation, and Basic Syntax
    • Variables, Arithmetic and Boolean Operations, Scalar and Structured Data Types
    • Conditional Flow and Loops, Defining Functions
  • Beyond Base Python

    • Creating/Installing Modules, Docstrings, Conda vs. Pip
    • Numpy, Ndarrays, Ufuncs, Aggregations
    • Matplotlib, Point/Line Plots, Contour/3D Plots, Subplots/Customization
  • Advanced Python

    • Classes, Attributes, Methods, Polymorphism
    • List Comprehension, Flexible Arguments, Map/Filter
    • File IO, Command Line Arguments, OS and Sys modules
  • Scientific Python

    • Scipy, Linalg, Stats, Interpolate, Optimize
    • Pandas, DataFrames, Time Series
    • Machine Learning, Scikit-Learn, Student Presentations

In addition to the outline above, we will have select lectures on specific math topics, including but not limited to:

  • Ordinary Differential Equations
  • Numerical Calculus
  • Random Numbers/Monte Carlo

Acknowledgements

Special thanks to:

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This introductory course, offered by the Department of Scientific Computing at Florida State University, teaches Python to undergraduates. It covers Python basics, including loops, lists, functions, and classes. Additionally, it explores popular packages like NumPy, SciPy, Pandas, and Matplotlib, emphasizing Python's role in solving scientific comp

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