Welcome to a GPT-Generated course designed to revolutionize the teaching of Applied Mathematics through the integration of Jupyter Notebooks, Markdown, and Python.
This repository showcases a compelling model for educational institutions, demonstrating how teachers and students can leverage custom-built GPTs to enhance Applied Mathematics education using Jupyter Notebooks.
This course covers a broad spectrum of Applied Mathematics, equipping students with the skills and knowledge needed in the modern world:
- Data Analysis Tools: Gain proficiency in software like Excel, Python, R, and various data visualization tools.
- Research Methodologies: Master both qualitative and quantitative research techniques.
- Applied Financial Mathematics: Delve into Time Series Data Gathering and Analysis, Options Pricing, Risk Models, and Forecast Evaluation.
- Financial Literacy & Financial Planning: Understand the basics and complexities of financial management.
- Mathematical Modeling: Learn to construct models to simulate real-world scenarios.
- Data Literacy: Become adept at interpreting and analyzing data.
- Basic Algorithmic Understanding: Grasp the fundamentals of algorithm development.
- Logic and Critical Thinking: Sharpen your reasoning and problem-solving skills.
- Discrete Mathematics: Explore the mathematics of countable spaces.
- Statistics: Understand the collection, analysis, interpretation, and presentation of data.
- Probability Theory: Learn about the mathematics of probability and randomness.
- Calculus: Dive into the study of change within mathematics.
- Linear Algebra: Understand the mathematics of vectors and matrices.
- Differential Equations: Explore equations involving derivatives and their applications.
- Numerical Methods: Learn about algorithms for solving mathematical problems numerically.
- Optimization Theory: Study the methodology for finding the best possible solution to a problem.
- Stochastic Processes: Delve into processes that involve randomness.
- Statistical Methods for Time Series: Explore methods for analyzing time series data.
- Machine Learning for Time Series: Apply machine learning techniques to time series data.
If you are an educator or administrator interested in integrating GPT or other AI/ML tools to enhance teaching effectiveness and curriculum quality, or if you aim to improve student learning outcomes while navigating the challenges associated with AI tools, I invite you to reach out for a consultation.
Whether you're looking to prevent the misuse of AI for "cheating" or to simply elevate the educational experience, connect with me on Linkedin. I offer a flat rate of $250 for a full day, remote or on-site consultation (plus any travel costs) or $8,000 for a comprehensive course design, deliverable within 4 weeks.