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Machine Learning & Data Science · head to head

Minitab vs scikit-learn

Minitab logo

Minitab

Machine Learning & Data Science

Statistical software for quality improvement

From
Free
Rated
-
Full reviewWebsite
S

scikit-learn

Machine Learning & Data Science

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Minitab pricing is by quote only: the pricing page is an inquiry form and publishes no rate, no seat price and no minimum; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: Minitab covers Statistical analysis, scikit-learn covers Classification algorithms.

Where they differ

Only the attributes on which Minitab and scikit-learn actually diverge.

Attributes where Minitab and scikit-learn differ
AttributeMinitabscikit-learn
Pricing modelsubscriptionUnknown
PlatformsMac, Windows, WebPython, Linux, macOS, Windows
Founded19722007

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Minitab

  • Statistical analysis
  • Quality tools
  • Regression analysis
  • Control charts
  • Design of experiments
  • Excel
  • Python
  • R

Only in scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Pandas

Both cover

  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

Minitab

  • Statistical analysis and hypothesis testing for quality engineeringnot scikit-learn
  • Six Sigma and process improvement studies with control chartsnot scikit-learn
  • Design of experiments and capability analysisnot scikit-learn

scikit-learn

  • Machine learningnot Minitab
  • Data analysisnot Minitab
  • Model trainingnot Minitab
  • Predictive analyticsnot Minitab

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Minitab

  • Pricing is by quote only: the pricing page is an inquiry form and publishes no rate, no seat price and no minimum
  • Obtaining a price requires submitting contact details and waiting for a sales representative

scikit-learn

  • No GPU acceleration by default; limited optional GPU support requires external arrays
  • Single-machine only; no built-in distributed computing across clusters
  • All datasets must fit entirely in RAM; no out-of-core learning
  • No production-grade deep learning; neural network support limited to basic multilayer perceptron
  • No reinforcement learning algorithms

Pricing, plan by plan

Minitab

Free
  • TrialFree
    • 7-day trial
    • Full features
  • Single User$29/month
    • Full Minitab
    • All features

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

Which should you pick?

Choose Minitab if

  • You need statistical analysis.
  • You want to start without paying.
  • You work on Mac, Windows, Web.
  • You also want quality tools.
Minitab review

Choose scikit-learn if

  • You need classification algorithms.
  • You want to start without paying.
  • You work on Python, Linux, macOS, Windows.
  • You also want regression models.
scikit-learn review

Questions people ask

Is Minitab or scikit-learn better?
Neither clearly leads. Minitab starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Minitab or scikit-learn?
Minitab starts at Free and scikit-learn at Free.
Does Minitab or scikit-learn run on more platforms?
Minitab runs on Mac, Windows, Web. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use Minitab for free?
Both have a free tier, so you can try either at no cost before committing.
What is Minitab best used for?
Minitab is most often used for statistical analysis and hypothesis testing for quality engineering, six sigma and process improvement studies with control charts, design of experiments and capability analysis. Of those, statistical analysis and hypothesis testing for quality engineering and six sigma and process improvement studies with control charts are not what scikit-learn is typically brought in for.
What can Minitab do that scikit-learn cannot?
Minitab covers Statistical analysis, Quality tools, Regression analysis, Control charts. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Both handle Mac support, Windows support.

Answered from the vendors’ own pages

scikit-learn: Does scikit-learn support GPU acceleration?

Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.

Source
scikit-learn: Can scikit-learn handle datasets larger than RAM?

No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.

Source
scikit-learn: Is scikit-learn free to use commercially?

Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.

Source
scikit-learn: What neural network capabilities does scikit-learn have?

Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.

Source
scikit-learn: Does scikit-learn include natural language processing?

Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.

Source
scikit-learn: When was scikit-learn first released?

Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.

Source
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