Python-for-Data-Science-AI-Development

🔢Module1: Python Basic

In this module, I was introduced to the foundational elements of Python programming, focusing on data types, expressions, and string manipulation. I learned how to distinguish and convert between key types such as integers, floats, and strings, and how to apply mathematical operations within expressions. I also explored how to store and modify data using variables and mastered the use of string methods to format and analyze text.

Key skills acquired:

Converting between Python data types (e.g., int(), str(), float())

Writing and evaluating expressions using arithmetic operators

Declaring and using variables in code

Manipulating strings using built-in methods like .lower(), .replace(), and slicing

Building a simple interactive Python program using JupyterLab

📚Module 2: Python Data Structures

I explored essential Python data structures used to organize and manage collections of data. I began with lists and tuples, learning how to store sequences of elements and perform operations like indexing, slicing, and iteration. I then moved on to dictionaries, which store key-value pairs for structured, efficient data access. Finally, I studied sets, which enforce uniqueness and allow for fast membership tests and set operations.

Key skills acquired:

Creating and manipulating lists (mutable sequences)

Executing operations on tuples (immutable sequences)

Using dictionary methods to store and retrieve values by key

Applying set theory through Python sets, including union, intersection, and difference

Differentiating the use cases for lists, tuples, dictionaries, and sets

These core data structures are critical to writing efficient, organized, and maintainable Python code across all domains—from web development to data science.

🧠⚙️Module 3: Python Programming Fundamentals

This module deepened my understanding of Python programming by covering foundational concepts necessary for building robust applications. I began by learning conditional statements and branching to guide the program’s flow. I then explored looping structures such as for and while loops to automate repetitive tasks. Next, I worked with functions to modularize code and make it reusable. I also learned how to implement exception handling to gracefully catch and handle runtime errors. The module concluded with an introduction to object-oriented programming (OOP) through the creation and use of classes and objects.

Key skills acquired:

Writing if/elif/else statements for logical branching

Creating for and while loops to iterate over data

Defining and calling functions with parameters and return values

Handling errors with try/except blocks to ensure stability

Understanding and building classes and objects to support modular, scalable code

Applying OOP principles like encapsulation through Python syntax

This module laid the groundwork for writing clean, maintainable, and logical Python code suited for both scripting and application development.

📊📁Module 4: Python for Data Handling

This module introduced essential skills for working with data in Python. I began by learning how to read from and write to files using built-in Python functions like open() and context managers (with), which are crucial for handling data stored in external sources. I then explored NumPy for efficient numerical operations and the creation of 1D and 2D arrays. The module concluded with hands-on practice using Pandas, a powerful library for data analysis and manipulation through DataFrames.

Key skills acquired:

Reading and writing text files using Python’s file I/O methods

Creating and manipulating NumPy arrays for mathematical computation

Using Pandas DataFrames to load, explore, and analyze structured data

Saving processed data to files for future use or sharing

Laying the foundation for advanced data science workflows in Python

This module gave me a strong starting point for working with data programmatically and efficiently using Python’s powerful libraries.

🌐📥Module 5: Data Collection in Python

This module explored the fundamental techniques used to collect data programmatically. I learned how to access and retrieve data using APIs (Application Programming Interfaces) and the HTTP protocol via the Python requests library. It also covered web scraping basics to extract information from websites and explained how to work with various file formats, making it possible to handle diverse data sources effectively.

Key skills acquired:

Understanding and using the HTTP request-response cycle

Sending and receiving data with REST APIs

Using Python’s requests library to call open-source APIs

Performing basic web scraping with libraries like BeautifulSoup

Reading and processing data in multiple file formats (e.g., CSV, JSON, XML)

Differentiating between standard APIs and RESTful APIs

By completing this module, I now understand how to gather real-world data from both structured APIs and unstructured websites, enabling deeper data analysis projects.