Python is one of the most in-demand programming languages for data science, artificial intelligence, and software development. This course provides a structured learning path for mastering Python from the ground up, starting with fundamental programming logic and progressing to real-world applications.
Learners will explore key programming concepts, including variables, data structures, loops, functions, and object-oriented programming. The course also introduces essential Python libraries such as Pandas and NumPy for data manipulation and analysis. Additionally, participants will gain hands-on experience working with Jupyter Notebooks, accessing APIs, and web scraping using Beautiful Soup.
By the end of this course, learners will have a solid foundation in Python programming and its applications in data-driven fields, enabling them to develop analytical solutions, automate workflows, and contribute to AI projects.
Sections Included:
- Introduction to Python Programming – Understanding Python syntax, variables, data types, and basic operations.
- Control Flow and Data Structures – Implementing branching, loops, lists, tuples, dictionaries, and sets.
- Functions and Object-Oriented Programming – Creating reusable code with functions, classes, and objects.
- Data Handling with Pandas and NumPy – Performing data manipulation, analysis, and numerical computing.
- Working in Jupyter Notebooks – Writing, testing, and documenting code effectively.
- Web Scraping and API Integration – Extracting data from websites and APIs using Python libraries.
- Real-World Applications in Data Science – Applying Python skills to practical data analysis and AI development projects.
Benefits Upon Completion:
- Develop proficiency in Python programming for data science and AI.
- Gain hands-on experience with Pandas, NumPy, and Jupyter Notebooks.
- Learn to extract and process data from web sources and APIs.
- Build a strong foundation in object-oriented programming.
- Enhance career prospects in data science, AI, and software development.
