This course is your gateway to mastering Python for data analysis, whether you are just getting started or looking to expand your skills. We begin with the basics, ensuring you build a solid foundation, then gradually move into data science applications.
I would like to stress that we do not assume a programming background and no background in Python is required.
Python Foundations: Grasp the essentials of Python, including data types, strings, slicing, f strings, and more, laying a solid base for data manipulation.
Control and Conditional Statements: Master decision making in Python using if else statements and logical operators.
Loops: Automate repetitive tasks with for and while loops, enhancing your coding efficiency.
Capstone Project Turtle Graphics: Apply your foundational knowledge in a fun, creative project using Pythons turtle graphics.
Functions: Build reusable code with functions, understanding arguments, return values, and scope.
Lists: Manage and manipulate collections of data with Python lists, including list comprehension.
Equality vs. Identity: Dive deep into how Python handles data with topics like shallow vs. deep copy, and understanding type vs. isinstance.
Error Handling: Write robust code by mastering exception handling and error management.
Recursive Programming: Solve complex problems elegantly with recursion and understand how it contrasts with iteration.
Searching and Sorting Algorithms: Learn fundamental algorithms to optimize data processing.
Advanced Data Structures: Explore data structures beyond lists, such as dictionaries, sets, and tuples, crucial for efficient data management.
Object Oriented Programming: Build scalable and maintainable code with classes, inheritance, polymorphism, and more, including an in depth look at dunder methods.
Unit Testing with pytest: Ensure your codes reliability with automated tests using pytest, a critical skill for any developer.
Files and Modules: Handle file input/output and organize your code effectively with modules.
NumPy: Dive into numerical computing with NumPy, the backbone of data science in Python.
Pandas: Master data manipulation and analysis with pandas, a must know tool for data science.
Matplotlib Graphing and Statistics: Visualize data and perform statistical analysis using Matplotlib.
Matplotlib Image Processing: Explore basic image processing techniques using Matplotlib.
Seaborn: Enhance your data visualization skills with Seaborn, creating more informative and attractive statistical graphics.
Plotly: Learn interactive data visualization with Plotly, producing interactive plots that engage users.
PyTorch Fundamentals: Get started with deep learning using PyTorch, understanding tensors and neural networks.
