python
Why?
Python has become the primary programming language for data science due to its readability, versatility, and extensive ecosystem of data & ai focused libraries. It enables data scientists to perform data manipulation, analysis, visualization, & implement machine learning algorithms. Python's simplicity allows for rapid development, in addition to its web apps frameworks which make integrating and deploying data-driven applications more accessible for data scientists without needing deep software engineering skills.
What?
This course introduces Python programming with a focus on data science applications. You will learn Python syntax, data structures, control flow, functions, object-oriented programming, and error handling, in addition to NumPy for numerical computing.
Curriculum:
Python Basics
Introduction to Python programming, including syntax, basic operations, variables, and the Python development environment. Overview of Python's role in data science and its key advantages.
Python Data Types
Exploring Python's built-in data types including numbers, strings, booleans, and understanding type conversion, operations, and methods available for each type.
Python Lists
Working with lists as a fundamental data structure in Python, including creation, manipulation, iteration, list comprehensions, and common list operations for data processing.
Conditional Statements
Implementing decision-making in Python programs using if, elif, and else statements, comparison operators, logical operators, and nested conditionals.
Functions
Defining and using functions, understanding parameters, return values, scope, lambda functions, and applying functional programming concepts in Python.
Python OOP
Object-oriented programming in Python, including classes, objects, inheritance, polymorphism, encapsulation, and how OOP facilitates data science workflows.
Error Handling
Implementing exception handling with try-except blocks, raising exceptions, defining custom exceptions, and writing robust code that gracefully handles errors.
NumPy
Introduction to the NumPy library for numerical computing, working with arrays, vectorized operations, mathematical functions, and efficient data manipulation techniques.
Python Methods
Advanced Python functionality including built-in methods, higher-order functions, generators, decorators, and techniques for writing efficient and maintainable Python code.
Notes
Python will be your companion throughout your data science career. This course offers a starting point, but truly mastering Python requires dedication beyond classroom learning. Many advanced courses focus primarily on theoretical concepts, so take initiative to explore essential libraries like Pandas, Numpy, Matplotlib, Scikit-learn, and many more. Complement your studies by building small personal projects and experimenting with tools like Jupyter notebooks or Google Colab. These practical skills will distinguish you as a data scientist far more than theoretical knowledge alone.
