List of data science software

This is a list of data science software and platforms used in data science, which includes programming languages, programming environments, machine learning frameworks, data engineering tools, statistical software, data analysis, plotting, MLOps systems, and more.

Programming languages

Data Analysis Expressions
FreeMat
GAUSS
GNU Octave
IDL
Java
JavaScript
Julia
MATLAB
O-Matrix
PV-WAVE
Python
R
S
Scilab language
SAS language
Scala
SPARQL
Speakeasy
Swift
Wolfram Language
Development environments

These interactive notebooks, IDEs, and platforms provide specialised development environments.

Apache Zeppelin
Architect — Eclipse
CoCalc
Dataiku Data Science Studio
FreeMat
GNU Octave
Google Colab
DataSpell
Jupyter Notebook / JupyterLab
Kaggle Notebooks
MATLAB
O-Matrix
PyCharm
RStudio
SAS and SAS Studio
Spyder
Visual Studio Code
Machine and deep learning software

The Machine learning / deep learning tools support development in those fields.

Examples of Data engineering tools.

Examples of Data mining tools.

Free and open-source

Data warehouses

environments include:

environments include:

Apache Hadoop
Cloudera
Databricks
Delta Lake
Amazon S3
Google Cloud Storage
Azure Data Lake
Algorithms
Apriori algorithm – frequent itemset mining and association rule learning in market basket analysis
Backpropagation – algorithm for training artificial neural networks using gradient descent
Decision Trees – tree-based algorithm for classification and regression
Expectation–maximization algorithm – iterative procedure for maximum likelihood estimation with latent variables
Gradient descent – iterative optimization algorithm for minimizing a loss function
ID3 algorithm – used to generate a decision tree from a dataset
K-Means – clustering algorithm based on minimizing within-cluster distances
K-Nearest Neighbors – instance-based learning and classification method
Linear regression – estimation method for predicting a dependent variable based on independent variables
Logistic regression – classification algorithm for predicting a binary outcome
Naive Bayes – probabilistic classifier based on Bayes' theorem
Ordinary least squares – estimation method for parameters in linear regression
PageRank – graph-based algorithm for link analysis and search ranking
Principal component analysis – technique to reduce high-dimensional data while preserving variance
Q-learning – reinforcement learning algorithm for learning optimal actions
Random forest – ensemble of decision trees for improved classification or regression
Sequential minimal optimization – solver for training support vector machines
Stochastic gradient descent – randomized variant of gradient descent for large-scale machine learning
Support Vector Machines – algorithm for finding a hyperplane to separate classes
Statistical software

Open-source

Tools for Data processing and analysis:

Software for Data visualization:

Software for plotting data to support processing and visualise resuls.