MATLAB
What it is: Commercial numerical computing platform. Matrix operations, algorithm development, engineering simulations.
What It Does Best
Engineering focus. Built for engineers. Signal processing, control systems, image processing all native.
Simulink integration. Visual programming for systems modeling. Industry standard for simulations.
Toolboxes. Pre-built packages for every domain: optimization, computer vision, statistics, finance.
Key Features
Matrix operations: Native matrix language, optimized linear algebra
Simulink: Block diagram modeling and simulation
Toolboxes: 90+ domain-specific add-on packages
Live Editor: Interactive notebooks with equations and graphics
Code generation: Convert MATLAB to C/C++ for deployment
Pricing
Standard: $2,150 one-time + $860/year maintenance
Academic: $99/year for students
Toolboxes: $29-1,000+ each per year
When to Use It
✅ Engineering and research (industry standard)
✅ Need Simulink for system modeling
✅ Team already uses MATLAB
✅ Domain-specific toolboxes critical
✅ Budget for commercial software
When NOT to Use It
❌ Cost is concern (Python/NumPy free)
❌ General data science (Python ecosystem richer)
❌ Machine learning focus (Python/R better)
❌ Web integration needed
❌ Open source preference
Common Use Cases
Control systems: Design and simulate control algorithms
Signal processing: Audio, communications, sensor data
Image processing: Computer vision, medical imaging
Financial modeling: Quantitative analysis, risk management
Academic research: Algorithm development, simulations
MATLAB vs Alternatives
vs Python/NumPy: MATLAB better for engineering, Python more versatile
vs R: MATLAB better for engineering, R better for statistics
vs Octave: Octave free MATLAB clone, MATLAB more polished
Unique Strengths
Simulink: Visual system modeling, no equivalent
Toolboxes: Comprehensive domain-specific libraries
Code generation: Deploy to embedded systems
Industry standard: Aerospace, automotive, electronics all use it
Bottom line: If you're an engineer or researcher in a MATLAB-heavy field, it's worth the cost. For general data science, Python is better value. Students get great academic pricing.