The Python Programming and Automation is designed to equip learners with in-depth knowledge of Python, one of the most versatile and widely used programming languages in the world. This comprehensive course covers the fundamentals of Python programming, including data types, control structures, functions, and object-oriented programming, while also diving into advanced topics such as file handling, exception management, and module creation. In addition, the course emphasizes automation techniques, enabling students to streamline repetitive tasks, manipulate data efficiently, and integrate Python with various applications and tools. Through hands-on projects and real-world examples, participants will gain practical experience in scripting, automating workflows, and solving complex problems, making them industry-ready for roles in software development, data analysis, and IT automation. Upon successful completion, learners receive a certification validating their expertise in Python programming and automation.
Module 1: Introduction to Python Overview of Python and its applications Setting up Python environment (Anaconda, IDEs) Writing first Python program Python syntax, keywords, and indentation.
Module 2: Data Types and Operators Variables and data types (int, float, string, boolean) Operators (arithmetic, logical, comparison, bitwise) Type conversion and typecasting.
Module 3: Control Structures Conditional statements (if, elif, else) Loops (for, while) Loop control statements (break, continue, pass).
Module 4: Functions and Modules Defining and calling functions Function arguments and return values Python modules and packages Using standard libraries.
Module 5: Object-Oriented Programming (OOP) Classes and objects Attributes and methods Inheritance, polymorphism, encapsulation Constructors and destructors.
Module 6: Data Handling Lists, tuples, sets, dictionaries List comprehension and dictionary comprehension File handling (read, write, append) Exception handling and debugging.
Module 7: Python Libraries for Data & Automation NumPy for numerical computing Pandas for data manipulation Matplotlib & Seaborn for data visualization Automation libraries: OS, Shutil, Scheduler.
Module 8: Web Scraping & API Integration Web scraping with BeautifulSoup and Requests Browser automation with Selenium Consuming APIs and automating data extraction.
Module 9: Database and Excel Automation Connecting Python with SQLite and MySQL CRUD operations in databases Automating Excel operations using OpenPyXL and xlrd/xlwt.
Module 10: Projects & Advanced Applications Real-time automation projects Data analysis and visualization projects Introduction to machine learning using Python Best practices, code optimization, and version control.
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