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CCM 105

Programming & Computational Math

Instructor

Dr. Garcia

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Course Overview

Module 1: Introductin to Programming.

Lesson 1: Introduction to Programming Concepts

  • Overview of programming languages and their importance.
  • Basic concepts such as variables, data types, and operations.

Lesson 2: Getting Started with Python

  • Installing Python and setting up the development environment.
  • Writing your first Python program.

Lesson 3: Python Basics

  • Understanding Python syntax.
  • Working with variables, operators, and basic data types.

Lesson 4: Control Flow

  • Using conditional statements (if, elif, else) in Python.
  • Introduction to loops (for and while) for repetitive tasks.

Lesson 5: Functions

  • Defining and calling functions in Python.
  • Passing arguments and returning values.

Lesson 6: Data Structures

  • Working with lists, tuples, and dictionaries in Python.
  • Understanding the properties and usage of each data structure.

Lesson 7: File Handling

  • Reading from and writing to files in Python.
  • Using file objects and file modes.

Lesson 8: Exception Handling

  • Handling errors and exceptions in Python.
  • Using try, except, and finally blocks.

Lesson 9: Introduction to Object-Oriented Programming (OOP)

  • Understanding OOP concepts such as classes and objects.
  • Creating and using classes in Python.

Lesson 10: Final Project

  • Applying the concepts learned in the course to create a simple program or project.
  • Emphasizing good coding practices and documentation.

Module 2: Introduction to Computational Math

Lesson 1: Introduction to Computational Math

  • Overview of computational mathematics
  • Importance and applications in various fields
  • Basic concepts and principles

Lesson 2: Number Systems and Representation

  • Binary, octal, decimal, and hexadecimal systems
  • Conversion between different number systems
  • Floating-point representation

Lesson 3: Mathematical Functions and Algorithms

  • Common mathematical functions (e.g., trigonometric, logarithmic)
  • Algorithms for basic operations (e.g., addition, multiplication)

Lesson 4: Linear Algebra in Computing

  • Matrices and vectors
  • Matrix operations (e.g., addition, multiplication)
  • Applications in graphics, machine learning, and cryptography

Lesson 5: Discrete Mathematics

  • Sets, relations, and functions
  • Combinatorics and probability
  • Graph theory and its applications

Lesson 6: Numerical Methods

  • Approximation and interpolation
  • Root finding and optimization techniques
  • Error analysis and convergence

Lesson 7: Differential Equations

  • Introduction to differential equations
  • Numerical methods for solving ordinary differential equations
  • Applications in physics, engineering, and biology

Lesson 8: Fourier Analysis

  • Fourier series and Fourier transforms
  • Discrete Fourier transform (DFT) and Fast Fourier Transform (FFT)
  • Applications in signal processing and data compression

Lesson 9: Computational Geometry

  • Basics of computational geometry
  • Algorithms for geometric problems (e.g., convex hull, closest pair)
  • Applications in computer graphics, robotics, and geographic information systems (GIS)

Lesson 10: Introduction to Computational Statistics

  • Descriptive statistics and data visualization
  • Probability distributions and statistical inference
  • Applications in data analysis, machine learning, and decision making

    Each lesson can include theoretical explanations, practical examples, and hands-on exercises to reinforce learning.

      • Prerequisite(s): MATH 115*
      • Corequisite(s): CCM 150L
      Free
      • Skill Beginner
      • Available Seats 10
      • Last Update May 21, 2024