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Pixel_Pioneer_Tutorial_2_From_NumPy_to_JAX_using_Caloric_Counting.ipynb

### Course Title: **" Python Programming Through Genetic Circuits: From Basics to Advanced (2024) "** ### Course Duration: Three Weeks (15 Days, 5 Days a Week) ### Target Audience: Individuals with little to no prior programming experience, interested in exploring Python in the context of genetics and bioinformatics. ### Course Outline with Assignments: #### Week 1: Python Basics and Introduction to Genetic Circuits - **Day 1: Introduction to Python** - **Topics**: What Python is, its importance in scientific computing, basic syntax, variables, data types. - **Assignment**: Install Python, write a script to print a personalized greeting. - **Tool**: Python & IDLE - Python is a versatile programming language, and IDLE is its integrated development environment, perfect for beginners to write and test simple programs. - **Day 2: Control Structures and Functions** - **Topics**: Conditional statements, loops, writing simple functions. - **Assignment**: Create a program that categorizes DNA nucleotides into purines and pyrimidines. - **Tool**: Python in-built editor - Useful for writing and executing small to medium-sized Python programs, allowing learners to focus on coding without overwhelming features. - **Day 3: Data Structures** - **Topics**: Lists, tuples, sets, dictionaries, string manipulation. - **Assignment**: Write a script to store and manipulate a DNA sequence. - **Tool**: Jupyter Notebook - An open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. - **Day 4: Introduction to Genetic Circuits** - **Topics**: Basics of genetics, molecular biology, and an overview of genetic circuits. - **Assignment**: Research and summarize a specific genetic circuit from recent studies. - **Tool**: Online academic databases (like PubMed) - Essential for accessing recent scientific literature and understanding current research in genetics. - **Day 5: Project - Basic DNA Sequence Analysis** - **Topics**: Reading DNA sequences, calculating nucleotide frequencies. - **Assignment**: Analyze a given DNA sequence for its nucleotide composition. - **Tool**: Biopython - A set of freely available tools for biological computation written in Python, great for handling biological data. #### Week 2: Intermediate Python and Genetic Circuit Simulation - **Day 6: Object-Oriented Programming (OOP)** - **Topics**: Introduction to classes and objects, basic OOP principles. - **Assignment**: Design a basic class structure for a genetic element (like a gene or promoter). - **Tool**: PyCharm - An integrated development environment used in computer programming, specifically for Python, offering code analysis, a graphical debugger, and a unit tester. - **Day 7: Working with Files** - **Topics**: File input/output, parsing genetic data files. - **Assignment**: Write a script to read and parse a genetic data file. - **Tool**: Excel/CSV files - Common formats for storing genetic data, easily readable and writable in Python for data manipulation. - **Day 8: Introduction to NumPy and Matplotlib** - **Topics**: Basic array operations in NumPy, data visualization with Matplotlib. - **Assignment**: Use NumPy to perform calculations on genetic data and visualize the results with Matplotlib. - **Tool**: NumPy & Matplotlib - NumPy is essential for numerical computations, and Matplotlib is a plotting library, both are fundamental for data analysis in Python. - **Day 9: Genetic Circuit Simulation Basics** - **Topics**: Modeling genetic circuits, basic simulation techniques using OOP. - **Assignment**: Create a simple model of a genetic toggle switch. - **Tool**: Python simulation libraries (like SciPy) - Useful for more complex computations and simulations in Python. - **Day 10: Project - Genetic Circuit Simulator** - **Topics**: Building a more advanced genetic circuit simulator. - **Assignment**: Extend the toggle switch model to include additional genetic elements. - **Tool**: GitHub - A platform for version control and collaboration, allowing you to manage your code projects and collaborate with others. #### Week 3: Advanced Python and Complex Genetic Circuit Analysis - **Day 11: Advanced Python Concepts** - **Topics**: List comprehensions, lambda functions, map/filter. - **Assignment**: Use advanced concepts to optimize the genetic circuit code. - **Tool**: Advanced Python documentation - Essential for in-depth understanding of Python's more complex features. - **Day 12: Introduction to Pandas and Data Analysis** - **Topics**: Data handling with Pandas, basic data analysis techniques. - **Assignment**: Analyze a dataset of genetic sequences using Pandas. - **Tool**: Pandas - A powerful Python data analysis toolkit, indispensable for manipulating and analyzing complex datasets. - **Day 13: Advanced Genetic Circuit Modeling** - **Topics**: Complex circuit design, data integration in modeling. - **Assignment**: Design a complex genetic circuit and simulate its behavior. - **Tool**: Simpy - A process-based discrete-event simulation framework based on standard Python, useful for complex system simulations in genetics. - **Day 14: Introduction to Machine Learning with Python** - **Topics**: Basics of machine learning, applying ML to genetic data. - **Assignment**: Implement a basic machine learning algorithm to identify patterns in genetic data. - **Tool**: scikit-learn - A free software machine learning library for Python, it features various classification, regression, and clustering algorithms. - **Day 15: Capstone Project** - **Topics**: Implementing a comprehensive project combining all learned concepts. - **Assignment**: Develop a Machine Learning-Enhanced Genetic Circuit Design Tool. - **Tool**: Python and all learned libraries - Utilizing the full spectrum of Python capabilities and libraries to create a comprehensive and practical application. ### Course Outcome: Participants will become proficient in Python programming, with a deep understanding of its applications in the field of genetic circuit analysis and bioinformatics. This course will equip them with practical skills to address complex biological problems using computational methods.

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