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List of data science software

List of data science software is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand List of data science software rather than just read about it. In short: 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 Development environments These interactive notebooks, IDEs, and platforms provide specialised development environments.

Key takeaways

  • List of data science software belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect List of data science software to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of List of data science software from memory before moving on to harder problems.

Reference excerpt

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

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

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

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

Data engineering Examples of Data engineering tools.

Apache Airflow Apache Flink Apache Hadoop Apache Kafka Apache NiFi Apache Spark Dask Data build tool (dbt)

Data mining Examples of Data mining tools.

Data mining

Proprietary

Database management

List of RDBMS

Proprietary

Data warehouses Data warehouse environments include:

Amazon Redshift Snowflake Google BigQuery Microsoft Azure Synapse Teradata Vertica

Data lakes Data lake 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 (KNN) – 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 (SVM) – algorithm for finding a hyperplane to separate classes

Statistical software

Open-source

Public domain CSPro Dataplot Epi Map X-13ARIMA-SEATS

Freeware BV4.1 MINUIT WinBUGS Winpepi

Proprietary

Data processing Tools for Data processing and analysis:

Data and information visualization Software for Data visualization:

Plotting software Software for plotting data to support processing and visualise results.

Maps and geospatial visualization

ArcGIS Carto Epi Map GeoDA Google Earth Engine Leaflet Mapbox MountainsMap QGIS

Machine learning MLOps and model deployment:

BentoML Data Version Control (DVC) Kubeflow MLflow Seldon Core Streamlit TensorFlow Serving Weights & Biases

Data repositories

Kaggle – platform for data science competitions, datasets, and notebooks. OpenML – collaborative platform for sharing datasets, algorithms, and experiments. University of California, Irvine Machine Learning Repository Zenodo – open-access repository supported by CERN and the EU.

Educational data science software

Kaggle – online platform for data science education, competitions, datasets, and collaborative learning. KNIME – open-source data analytics platform used for teaching data science, machine learning, and workflow-based analysis. RapidMiner – used in academic research and education for data mining and machine learning. Statistics Online Computational Resource (SOCR) – online tools and instructional resources for statistics education. Tanagra (machine learning) – data mining software developed for research and teaching purposes. TinkerPlots – explore and analyze data through visual modeling.

See also

Business intelligence software List of data science journals List of R software and tools Lists of mathematical software and List of open-source software for mathematics List of numerical-analysis software List of numerical analysis topics List of numerical libraries List of open-source data science software Common Crawl – nonprofit that crawls the web and freely provides its archives and datasets to the public under an MIT License

References

External links 20 Tools for Data Scientists | Pragmatic Institute igorbarinov/awesome-data-engineering

Worked examples

Example 1 — a first encounter with List of data science software

Start with the simplest possible case. Write down what List of data science software claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to List of data science software before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about List of data science software ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of List of data science software

In research
List of data science software appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses List of data science software in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
List of data science software is common in secondary-school and first-year university syllabi. It links to neighbouring topics Big data, Data analysis software, Data engineering, so understanding it makes those chapters shorter.
In everyday life
Look for List of data science software outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study List of data science software in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what List of data science software means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain List of data science software out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is List of data science software in simple terms?

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 Development…

Why does List of data science software matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study List of data science software?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on List of data science software.

Tags

  • Big data
  • Data analysis software
  • Data engineering
  • Data mining and machine learning software
  • Data science
  • Database management systems
  • Free and open-source software
  • Lists of software
  • Programming tools
  • Science software
  • Statistical software
  • Visualization software

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