🐍 MemoLearning Python for Data Science

Master Python programming fundamentals and essential libraries for data science

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Python for Data Science Curriculum

12
Core Units
~150
Python Concepts
8+
Essential Libraries
50+
Hands-on Examples
1

Python Fundamentals

Learn Python basics including syntax, variables, operators, and data types essential for data science.

  • Python installation and setup
  • Variables and data types
  • Basic operators and expressions
  • Comments and documentation
  • Input and output operations
  • Python syntax and indentation
  • Interactive Python and REPL
  • First data science script
2

Data Structures

Master Python's built-in data structures for organizing and manipulating data effectively.

  • Lists: creation, indexing, slicing
  • List methods and operations
  • Tuples and immutability
  • Dictionaries and key-value pairs
  • Sets and unique collections
  • Nested data structures
  • Data structure comparison
  • Choosing the right structure
3

Control Flow

Learn conditional statements, loops, and control structures for program logic and data processing.

  • Conditional statements (if, elif, else)
  • Comparison and logical operators
  • For loops and iteration
  • While loops and conditions
  • Loop control (break, continue)
  • Nested loops and conditions
  • List comprehensions
  • Data filtering and transformation
4

Functions and Modules

Create reusable code with functions and organize code using modules for data science workflows.

  • Function definition and calling
  • Parameters and arguments
  • Return statements and values
  • Local vs global scope
  • Lambda functions
  • Built-in functions for data
  • Importing modules and packages
  • Creating custom modules
5

File Handling and I/O

Read and write data files, handle different file formats, and manage data input/output operations.

  • Opening and closing files
  • Reading text files
  • Writing to files
  • File modes and operations
  • CSV file handling
  • JSON data processing
  • File paths and directories
  • Error handling in file operations
6

Error Handling and Debugging

Handle errors gracefully and debug Python code effectively for robust data science applications.

  • Understanding error types
  • Try-except blocks
  • Catching specific exceptions
  • Finally and else clauses
  • Raising custom exceptions
  • Debugging techniques
  • Using print statements
  • IDE debugging tools
7

Object-Oriented Programming

Learn OOP concepts including classes, objects, and inheritance for organizing data science code.

  • Classes and objects
  • Instance and class attributes
  • Methods and self parameter
  • Constructor method (__init__)
  • Inheritance and polymorphism
  • Encapsulation and data hiding
  • Special methods (dunder methods)
  • OOP in data science contexts
8

NumPy Fundamentals

Master NumPy arrays, mathematical operations, and numerical computing for data science.

  • NumPy installation and import
  • Creating NumPy arrays
  • Array attributes and properties
  • Array indexing and slicing
  • Mathematical operations
  • Broadcasting and vectorization
  • Array reshaping and manipulation
  • Statistical functions
9

Pandas for Data Manipulation

Learn Pandas library for data manipulation, analysis, and preprocessing tasks.

  • Pandas installation and import
  • Series and DataFrame creation
  • Data loading (CSV, Excel, JSON)
  • Data selection and filtering
  • Data cleaning and preprocessing
  • Handling missing values
  • Data transformation and aggregation
  • Merging and joining datasets
10

Data Visualization with Matplotlib

Create compelling data visualizations using Matplotlib for exploratory data analysis.

  • Matplotlib installation and setup
  • Basic plotting with pyplot
  • Line plots and scatter plots
  • Bar charts and histograms
  • Customizing plots (colors, labels)
  • Subplots and figure management
  • Saving and exporting plots
  • Interactive plotting basics
11

Development Environment Setup

Set up efficient Python development environments for data science projects.

  • Python installation methods
  • Virtual environments with venv
  • Conda and Anaconda setup
  • Jupyter Notebook installation
  • IDE selection (VS Code, PyCharm)
  • Package management with pip
  • Environment configuration
  • Best practices and workflows
12

Practical Data Science Project

Apply all learned Python concepts in a comprehensive data science project from start to finish.

  • Project planning and setup
  • Data collection and loading
  • Exploratory data analysis
  • Data cleaning and preprocessing
  • Statistical analysis
  • Data visualization
  • Results interpretation
  • Project documentation

Unit 1: Python Fundamentals

Learn Python basics including syntax, variables, operators, and data types essential for data science.

Python Installation and Setup

Learn how to install Python on different operating systems and set up your development environment for data science work.

# Check Python version
python --version
python3 --version

Variables and Data Types

Understand how to create variables and work with different data types including integers, floats, strings, and booleans.

name = "Data Science"
age = 25
height = 5.9
is_student = True

Basic Operators and Expressions

Master arithmetic, comparison, and logical operators for performing calculations and making decisions in your code.

# Arithmetic operators
result = 10 + 5 * 2
# Comparison operators
is_greater = 10 > 5

Comments and Documentation

Learn to write clear comments and documentation to make your code readable and maintainable for data science projects.

# This is a single-line comment
"""
This is a multi-line comment
for longer explanations
"""

Input and Output Operations

Handle user input and display output effectively for interactive data science applications and scripts.

name = input("Enter your name: ")
print(f"Hello, {name}!")

Python Syntax and Indentation

Understand Python's unique syntax rules, especially indentation, which is crucial for writing correct Python code.

if True:
    print("Properly indented")
    print("Still in the block")

Interactive Python and REPL

Learn to use Python's interactive shell (REPL) for quick testing and exploration of data science concepts.

>>> 2 + 3
5
>>> print("Hello World")
Hello World

First Data Science Script

Create your first complete Python script that demonstrates basic data science concepts and operations.

# Calculate average of numbers
numbers = [1, 2, 3, 4, 5]
average = sum(numbers) / len(numbers)
print(f"Average: {average}")

Unit 2: Data Structures

Master Python's built-in data structures for organizing and manipulating data effectively.

Lists: Creation, Indexing, Slicing

Learn to create lists, access elements using indexing, and extract portions using slicing techniques.

data = [1, 2, 3, 4, 5]
first_item = data[0]
last_three = data[-3:]

List Methods and Operations

Master essential list methods for adding, removing, and modifying elements in your data collections.

numbers = [1, 2, 3]
numbers.append(4)
numbers.extend([5, 6])
numbers.remove(2)

Tuples and Immutability

Understand tuples as immutable sequences and when to use them instead of lists in data science applications.

coordinates = (10.5, 20.3)
x, y = coordinates # Unpacking
# coordinates[0] = 15 # Error!

Dictionaries and Key-Value Pairs

Learn to work with dictionaries for storing and retrieving data using meaningful keys instead of numeric indices.

student = {
  "name": "Alice",
  "age": 22,
  "grades": [85, 90, 92]
}

Sets and Unique Collections

Use sets to work with unique collections of items and perform mathematical set operations on your data.

unique_values = {1, 2, 3, 3, 4}
# Result: {1, 2, 3, 4}
set1 & set2 # Intersection

Nested Data Structures

Handle complex data by nesting lists, dictionaries, and other structures to represent real-world datasets.

dataset = [
  {"name": "Alice", "scores": [85, 90]},
  {"name": "Bob", "scores": [78, 85]}
]

Data Structure Comparison

Compare the characteristics, performance, and use cases of different Python data structures for data science.

# Lists: ordered, mutable
# Tuples: ordered, immutable
# Dicts: key-value, mutable
# Sets: unique, mutable

Choosing the Right Structure

Learn decision-making criteria for selecting the most appropriate data structure for different data science tasks.

# Use list for ordered sequences
# Use dict for labeled data
# Use set for unique items
# Use tuple for immutable data

Unit 3: Control Flow

Learn conditional statements, loops, and control structures for program logic and data processing.