Python for Data Science — Digital Notes & Practice

These are MUST

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📚 What I Covered

Topics in Krish Naik’s Complete Python Playlist:

• Anaconda installation & Python basics
• Why Python is best for Machine Learning
• Lists & Boolean variables
• Sets, Dictionaries, Tuples
• NumPy and built-in functions
• Pandas: DataFrames & Series (part 1)
• Pandas: Reading CSVs (part 2)
• Pandas: Reading JSON, HTML, Pickle, Excel (part 3)
• Matplotlib basics (simple visualizations)
• Seaborn: Distplot, Joinplot, Pairplot (part 1)
• Seaborn: Countplot, Violinplot, Boxplot (part 2)
• Exploratory Data Analysis (conceptual)
• EDA practical example: Titanic dataset
• Functions, positional & keyword arguments
• Lambda functions
• Map, Filter functions
• List comprehensions
• Advanced string formatting
• Iterables vs. Iterators
• Pyforest — importing DS libraries
• Object-Oriented Programming (classes, methods, objects)
• Exception handling & Custom exceptions
• Inheritance, Magic methods, Assert statement
• Iterators vs. Generators
• Decorators in depth
• is vs. == nuances
• 60+ solved Python projects
• Shallow vs. Deep Copy
• Speeding up Pandas with Modin
• Class variables, Class & Static methods
• Multiple inheritance
• eval() function — dynamic evaluation
• Asynchronous execution (threads)
• Vulture — dead code detection
• zip() function
• PDF password protection
• requirements.txt via pip freeze
• Logging in Python
• Secure Hash Algorithms (SHA256, etc.)
• Numba — performance optimization
• Public, Private & Protected modifiers
• House Price Prediction project (EDA & Feature Engineering)
• Productivity tips & VSCode extensions
• Importing modules from different directories

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Credits: Krish Naik Sir

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🙋‍♂️ Feedback & Collaboration

If you find any mistake or want to suggest improvements, feel free to:

• Raise an issue
• Submit a pull request
• Connect with me on LinkedIn

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“Learning Python is the first step toward mastering the world of Data Science.”