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Python Masterclass

This document shows an hypotetical programme for a longer masterclass. You can use it to study python at your pace, using online resources.

  1. Introduction to Python

We covered part of the setup during the masterclass, and you should be ready to study the basic syntax (see example )

  • Overview of Python: History, versions, and uses
  • Setting Up the Environment: Installing Python, using IDEs (PyCharm, VS Code)
  • Basic Syntax: Variables, data types, and basic operations
  1. Control Flow

Try examples with simple variables

  • Conditional Statements: if, elif, else
  • Loops: for, while, nested loops
  • Comprehensions: List, dictionary, and set comprehensions
  1. Functions

Create simple functions performing simple tasks, like summing numbers

  • Defining Functions: Parameters, return values, and scope
  • Lambda Functions: Anonymous functions
  • Built-in Functions: map, filter, reduce

  1. Data Structures

    • Lists: Methods, slicing, and comprehensions
    • Tuples: Immutability, packing, and unpacking
    • Dictionaries: Key-value pairs, methods, and comprehensions
    • Sets: Unique elements, methods, and operations

  2. Modules and Packages

    • Importing Modules: Standard library and third-party modules
    • Creating Packages: Structuring and importing packages

  3. File Handling

In bioinformatics several files are very large and we need to read them line by line, rather than loading the whle content to memory

  • Reading and Writing Files: Text and binary files
  • Context Managers: Using with statement
  • JSON Handling: Reading from and writing to JSON files
  1. Exception Handling

Here we go into more advanced topics, but useful when your scripts start to require debugging

  • Try-Except Blocks: Catching and handling exceptions
  • Raising Exceptions: Custom exceptions
  • Finally Clause: Cleaning up resources
  1. Object-Oriented Programming (OOP)

See our example

  • Classes and Objects: Defining classes, creating objects
  • Methods: Instance, class, and static methods
  • Inheritance: Single and multiple inheritance
  • Polymorphism: Method overriding and magic methods
  • Encapsulation: Private and protected members
  1. Advanced Topics

You can probably skip this section at first and go straigh to data analysis

  • Decorators: Function decorators and class decorators
  • Generators: Yield statement, generator expressions
  • Context Managers: Custom context managers using enter and exit
  • Concurrency: Threads, multiprocessing, and async programming
  1. Working with Libraries

See our example

  • NumPy: Arrays and mathematical functions
  • Pandas: DataFrames, series, and data manipulation
  • Matplotlib: Plotting and visualization
  • BioPython: Working with biological data
  • Requests: HTTP requests for API calls
  1. Testing and Debugging
  • Unit Testing: unittest module, writing test cases
  • Debugging: Using pdb, logging, and debugging tools in IDEs
  1. Best Practices
  • Code Style: PEP 8 guidelines
  • Documentation: docstrings
  • Version Control: Using Git and GitHub
  1. Real-World Projects in Bioinformatics

It's great to assign yourself some tasks. To begin with try to replicate existing programs, so that you can compare your output with theirs. See our example

  • Project 1: Analyzing DNA Sequences with BioPython
  • Project 2: Data Analysis of Genomic Data with Pandas
  • Project 3: Creating a Bioinformatics Pipeline
  • Project 4: Visualizing Biological Data with Matplotlib