Useful Links
Statistics
Statistical Computing
1. Introduction to Statistical Computing
2. Programming Fundamentals for Statistics
3. Data Management and Manipulation
4. Numerical Methods for Statistics
5. Simulation and Resampling Methods
6. Advanced Computational Methods
7. Statistical Model Implementation
8. Visualization and Communication
9. Software Engineering for Statistics
Programming Fundamentals for Statistics
Core Programming Languages
R Programming Language
Syntax and Structure
Data Types and Objects
Statistical Packages and Libraries
CRAN Ecosystem
Python for Statistics
Syntax and Structure
Scientific Computing Stack
NumPy
SciPy
pandas
Matplotlib
Scikit-learn
Julia for High-Performance Computing
Syntax and Structure
Performance Features
Multiple Dispatch
Interoperability with Other Languages
Other Languages
C and C++ for Performance
SQL for Database Operations
JavaScript for Web Applications
Programming Fundamentals
Variables and Data Types
Numeric Types
Integers
Floating-Point Numbers
Complex Numbers
Character and String Types
Logical and Boolean Types
Data Type Conversion and Coercion
Operators
Arithmetic Operators
Comparison Operators
Logical Operators
Assignment Operators
Control Structures
Conditional Statements
if Statements
else and elif Statements
Nested Conditionals
Switch Statements
Loops
for Loops
while Loops
Nested Loops
Loop Control
break Statements
next/continue Statements
Functions and Procedures
Defining Functions
Function Parameters
Required Parameters
Optional Parameters
Default Values
Variable-Length Arguments
Return Values
Function Documentation
Scope and Environments
Local vs. Global Scope
Lexical Scoping
Closures
Error Handling
Types of Errors
Exception Handling
Debugging Strategies
Data Structures for Statistics
Basic Data Structures
Vectors and Arrays
Creation and Initialization
Indexing and Slicing
Element-wise Operations
Matrices
Matrix Construction
Matrix Indexing
Matrix Operations
Lists
List Creation
Accessing and Modifying Elements
List Comprehensions
Statistical Data Structures
Data Frames
Structure and Properties
Creating Data Frames
Indexing and Subsetting
Adding and Removing Columns
Factors and Categorical Data
Time Series Objects
Sparse Data Structures
Advanced Data Structures
Multidimensional Arrays
Nested Data Structures
Custom Data Types
Advanced Programming Concepts
Object-Oriented Programming
Classes and Objects
Defining Classes
Instantiating Objects
Attributes and Methods
Inheritance
Polymorphism
Method Dispatch
S3 and S4 Systems in R
Functional Programming
Higher-Order Functions
Map, Filter, and Reduce
Lambda Functions
Recursion
Vectorization and Broadcasting
Vectorized Operations
Benefits of Vectorization
Broadcasting Rules
Avoiding Loops
Memory Management
Memory Allocation
Garbage Collection
Memory Profiling
Memory-Efficient Programming
Previous
1. Introduction to Statistical Computing
Go to top
Next
3. Data Management and Manipulation