MATLAB vs Python for Data Science: An Honest Comparison

I've used MATLAB extensively in academic research and Python in personal/open-source projects. Both are excellent tools, but they have very different strengths. Here's my honest take after working with both for years.

Where MATLAB Wins

1. Signal Processing and Control Systems

MATLAB was built for this. The Signal Processing Toolbox, Control System Toolbox, and Simulink are unmatched:

% Design a Butterworth lowpass filter — clean, intuitive API
[b, a] = butter(6, 0.3);
filtered = filtfilt(b, a, noisy_signal);

% Frequency analysis
[pxx, f] = pwelch(signal, [], [], [], fs);

The equivalent in Python (scipy.signal) works, but MATLAB's documentation, interactive tools (like filterDesigner), and Simulink integration make it the better choice for serious DSP work.

2. Matrix Operations and Linear Algebra

MATLAB's syntax was designed around matrices. Operations are concise and readable:

% MATLAB
A = [1 2; 3 4];
b = [5; 6];
x = A \ b;           % Solve Ax = b
[U, S, V] = svd(A);  % SVD
eig_vals = eig(A);   % Eigenvalues

Python requires explicit imports and more verbose syntax:

import numpy as np
A = np.array([[1, 2], [3, 4]])
b = np.array([[5], [6]])
x = np.linalg.solve(A, b)
U, S, V = np.linalg.svd(A)
eig_vals = np.linalg.eigvals(A)

Not a huge difference, but MATLAB feels more natural for linear algebra-heavy work.

3. Plotting and Visualization (for quick exploration)

MATLAB's figure system is excellent for rapid exploration:

subplot(2,1,1); plot(t, signal); title('Time Domain');
subplot(2,1,2); spectrogram(signal, 256, 250, 256, fs, 'yaxis');

Getting interactive, publication-quality plots with minimal code is where MATLAB shines.

4. Toolboxes

Specialized toolboxes (Image Processing, Statistics, Optimization, Curve Fitting) are polished, well-documented, and integrated. They represent decades of domain-expert development.

Where Python Wins

1. Machine Learning and Deep Learning

Python's ML ecosystem is simply larger and more active:

• scikit-learn: Comprehensive, consistent API for classical ML
• TensorFlow/PyTorch: The frameworks for deep learning
• Hugging Face: Pre-trained models and datasets
• XGBoost, LightGBM: Best gradient boosting implementations

MATLAB has the Deep Learning Toolbox, but the community, tutorials, and pre-trained model availability in Python are overwhelming.

2. Data Manipulation

Pandas is exceptional for tabular data:

import pandas as pd

df = pd.read_csv("data.csv")
result = (df
    .groupby("category")
    .agg({"value": ["mean", "std"], "count": "sum"})
    .sort_values(("value", "mean"), ascending=False)
)

MATLAB's table type has improved, but Pandas is more flexible and ergonomic for complex data wrangling.

3. Cost and Accessibility

This is the elephant in the room. MATLAB requires expensive licenses ($2,150+ per year for commercial use, each toolbox extra). Python is free. For personal projects, open-source work, and startups, this matters enormously.

4. Deployment and Integration

Python runs everywhere. You can:

• Build web APIs with Flask/FastAPI
• Create CLI tools with Click/Typer
• Deploy ML models with Docker
• Integrate with virtually any system

MATLAB Compiler exists but adds complexity and cost.

5. Package Ecosystem

pip has 400,000+ packages. MATLAB's File Exchange is useful but smaller by orders of magnitude.

My Workflow

I use both, depending on the task:

Task	My Choice
Signal processing (GPR, EMG)	MATLAB
Quick matrix prototyping	MATLAB
Machine learning projects	Python
Data pipeline / ETL	Python
Academic research (in my department)	MATLAB
Open-source / deployment	Python
Deep learning	Python
Control systems	MATLAB

The Verdict

There is no universal winner. MATLAB excels in engineering domains — signal processing, control systems, and simulation. Python excels in software engineering contexts — ML deployment, web integration, and general-purpose programming.

If I had to pick one for a career in data science, I'd pick Python for its breadth and accessibility. But for specific engineering disciplines, knowing MATLAB is still a significant advantage. Ideally, learn both and use each where it shines.