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Population Modeling and Simulations in Python
In this project, we’ll learn to use concepts of mathematics and business to create simulations and aid the process of decision-making.
You will learn to:
Understand modeling and simulations.
Create John Conway's Game of Life.
Run the Lotka-Volterra model.
Make epidemiology modeling programs.
Create interactive data visualizations.
Serve simulations in the browser.
Modeling and Simulation
Intermediate proficiency in Python
Basic knowledge of calculus
Basic knowledge of data visualization
One of the main limitations of the current state of machine learning and deep learning is the constant need for new data. How can we make predictions when we don’t have any data available? This lack of data is more common than we would normally think.
In this project, we’ll learn about modeling and simulations and how they can be applied in various population analysis scenarios, such as understanding how diseases can spread within a community, prey/predator dynamics, and Conway’s Game of Life. Throughout the project, we will make sure that everything is interactive and shareable on the web. To do so, we will learn to use libraries such as SciPy, Matplotlib, Plotly, Streamlit, Datapane, and others.
Task 0: Getting Started
Game of Life
Task 1: Creating John Conway’s Game of Life
Task 2: Comparing and Animating Game of Life
Task 3: Introduction to Lotka-Volterra Model
Task 4: Creating a Phase-Space Plot
Task 5: Making Visualizations Interactive
Task 6: Adding Logistic Growth
Task 7: Creating a LG Summary Interactive Plot
Task 8: Finding LG Steady State
Task 9: Implementing Parameter Fitting
Task 10: Exploring Euler's Method
Task 11: Making an SIR Model
Task 12: Deriving SIR Phase-Space Plot
Task 13: Adding the Time-Limited Immunity
Task 14: Making SIR Stochastic
Task 15: Implementing Agent Based Modeling
Task 16: Adding Space Plotting in Agent Based Modeling
Task 17: Exploring Streamlit
Task 18: Exploring Datapane
Task 19: Exploring PyScript