Python Curriculum Summary for Wastewater Surveillance and Bioinformatics

This curriculum is designed to equip you with fundamental and practical Python skills tailored for wastewater surveillance, bioinformatics, and public health applications. Below is an index of modules followed by a detailed description of each module.

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Index

1. Module 001: Introduction to Python
2. Module 002: Input Handling in Python
3. Module 003: Conditional Statements in Python
4. Module 004: Loops in Python
5. Module 005: File Input and Output in Python
6. Module 006: Data Analytics Introduction in Python
7. Module 007: Data Visualizations in Python
8. Module 008: Bonus Materials

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Module 001: Introduction to Python

• Overview:
  An introduction to Python as a powerful programming language for bioinformatics and public health.
• Key Topics:
  • Basic Python syntax and data types
  • Variables and simple operations
  • A narrative approach using a character (Tekin) to illustrate real-life applications in wastewater surveillance
• Objective:
  Build a solid foundation in Python by understanding its importance and learning basic programming concepts.

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Module 002: Input Handling in Python

• Overview:
  Learn to interact with users and process real-world data dynamically using Python’s input() function.
• Key Topics:
  • Collecting user input and converting it into appropriate data types (e.g., strings, integers, floats)
  • Validating inputs for wastewater sample IDs, viral loads, and DNA sequences
• Objective:
  Ensure your programs can robustly handle and validate data entered by users in a lab or field setting.

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Module 003: Conditional Statements in Python

• Overview:
  Understand how to make decisions in your code using conditional statements such as if, if-else, and if-elif-else.
• Key Topics:
  • Using conditionals to validate DNA sequences and detect genetic patterns (e.g., start codons)
  • Flagging high viral loads and differentiating between RNA and DNA samples
• Objective:
  Learn to implement logic that filters, classifies, and responds dynamically to varying data conditions in bioinformatics.

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Module 004: Loops in Python

• Overview:
  Explore iterative structures in Python to automate repetitive tasks, a must when dealing with large datasets.
• Key Topics:
  • For loops: Iterating over strings, lists, and tuples
  • While loops and loop control statements (break, continue)
  • Practical examples such as iterating through DNA sequences (in triplets) and processing multiple wastewater samples
• Objective:
  Master looping techniques to efficiently analyze large-scale environmental and genomic data.

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Module 005: File Input and Output in Python

• Overview:
  Learn how to read from and write to files using Python, a crucial skill for handling data logs and experimental outputs.
• Key Topics:
  • Opening, reading, and writing files (e.g., CSV, TXT)
  • Automating file processing tasks such as data extraction and report generation
• Objective:
  Enable the automation of data management tasks in public health laboratories and surveillance projects.

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Module 006: Data Analytics Introduction in Python

• Overview:
  Introduces basic data analytics concepts using Python to derive insights from public health data.
• Key Topics:
  • Data manipulation and summarization using libraries like Pandas
  • Basic statistical analysis and data cleaning techniques
• Objective:
  Equip you with the skills to analyze and interpret environmental and genomic data, setting the stage for more advanced analytics.

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Module 007: Data Visualizations in Python

• Overview:
  Focus on visualizing data to communicate complex information clearly and effectively.
• Key Topics:
  • Creating plots and charts using visualization libraries (e.g., Matplotlib)
  • Designing dashboards and graphical representations of wastewater surveillance data
• Objective:
  Learn to convert data into visual insights that can support decision-making in public health.

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Conclusion

This curriculum bridges essential Python programming concepts with practical applications in wastewater surveillance and bioinformatics. Each module builds on the previous one, providing you with the skills to:

• Process and analyze complex environmental and genomic datasets.
• Develop automated tools for public health data analysis.
• Visualize data effectively for informed decision-making.

By the end of this course, you will have a robust foundation in Python and the ability to apply these skills to real-world challenges in public health and environmental surveillance.

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