5 days





Course features:


Lab Requirements:

Learning Path

  • Overview of Python programming language
  • Setting up Python environment (interpreter, IDE)
  • Writing and executing Python scripts
  • Data types: integers, floats, strings, lists, tuples, dictionaries
  • Variables and assignments
  • Basic arithmetic and string operations
  • Control flow: if statements, loops (for and while), conditional expressions
  • Functions: defining functions, parameters, return values
  • Input/output operations: reading from/writing to files
  • Exception handling: try-except blocks
  • Hands-on exercises and practice problems
  • Lists: list comprehensions, slicing, common list methods
  • Tuples: immutability, packing/unpacking
  • Dictionaries: key-value pairs, dictionary comprehensions
  • Nested loops and conditionals
  • Break, continue, and pass statements
  • Enumerate and zip functions
  • Lambda functions and anonymous functions
  • Higher-order functions: map, filter, reduce
  • Function decorators
  • Classes and objects
  • Inheritance, encapsulation, and polymorphism
  • Hands-on OOP exercises and projects
  • Overview of built-in modules and functions
  • Commonly used modules: os, sys, math, random
  • Installing and managing external libraries using pip
  • Exploring popular Python libraries: NumPy, pandas, matplotlib
  • Setting up Flask environment
  • Creating routes and handling requests
  • Templating with Jinja2
  • Creating a CRUD (Create, Read, Update, Delete) application
  • Integrating Flask with databases (SQLite)
  • Multithreading vs. multiprocessing
  • Thread synchronization and locks
  • Asynchronous programming with async/await
  • Handling exceptions effectively
  • Logging and debugging techniques
  • Unit testing with unittest module
  • •Writing clean and maintainable code
  • Performance optimization techniques
  • Code profiling and optimization tools
  • Data manipulation with pandas
  • Data visualization with matplotlib and seaborn
  • Hands-on data analysis projects
  • Overview of data science lifecycle
  • Introduction to machine learning concepts
  • Exploring popular machine learning libraries: scikit-learn, TensorFlow, Keras
  • Data preprocessing and feature engineering
  • Model training and evaluation
  • Model deployment and inference
  • Overview of Ansible and its architecture
  • Writing Ansible playbooks for automation tasks
  • Hands-on Ansible exercises
  • Participants work on a final project that integrates various Python concepts learned throughout the bootcamp
  • Project presentations and peer feedback