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Data science

Data scientists rely on Stata because of its strong programming capabilities, reproducibility, extensibility, and interoperability. From data wrangling to reporting, Stata provides the tools you need to accomplish your analyses. Permissive licensing allows you to easily integrate it into your proprietary workflow.

Features for data scientists

Data wrangling
Scrape data from the web, import it from standard formats, or pull it in via SQL with JDBC or ODBC. Match-merge, link, append, reshape, transpose, sort, filter. Stata handles Unicode, frames (multiple datasets in memory), BLOBs, regular expressions, and more, whether working with hundreds of thousands or even billions of data points.

Automated reporting and customizable tables
Use Markdown to create Word documents and HTML files with embedded Stata code, output, and graphs. Automate Word, PDF, or Excel reports with both high-level export capabilities and low-level fine-grained programmatic access to automate production of the documents your team needs. Customize tables to clearly communicate results, and export your tables to Word, PDF, HTML, LaTeX, Excel, or Markdown. Read more about Markdown, about Word Documents, about PDF documents, about Excel, or about customizable tables.

Create graphs and customize them programmatically or interactively with the Graph Editor. Edits can even be recorded and "replayed" on other graphs for reproducibility. Export to industry standard formats suitable for web (SVG, PNG) or print (PDF, TIFF, EPS, PS).

Automate your entire workflow with both scripts and full-blown programming features like classes, structures, and pointers. A unique feature of Stata's programming environment is Mata, a fast and compiled matrix programming language. Of course, it has all the advanced matrix operations you need. It also has access to the power of LAPACK. What's more, it has built-in solvers and optimizers to make implementing your own estimator easier. And you can leverage all of Stata's estimation features and other features from within Mata.

PyStata—Python integration
Interact Stata code with Python code. You can seamlessly pass data and results between Stata and Python. You can use Stata within Jupyter Notebook and other IPython environments. You can call Python libraries such as NumPy, matplotlib, Scrapy, scikit-learn, and more from Stata. You can use Stata analyses from within Python.

Connect to external code via Python, Java, and C++ plugins. Write Python or Java code directly within your Stata code. Control Stata via Jupyter Notebook, OLE Automation, or call it in batch mode. Write custom SQL statements with JDBC and ODBC to extract from or populate databases. Access H2O clusters. Read more about PyStata—Python integration, Jupyter Notebook, Java integration, Java plugins, C/C++ plugins, H2O integration, and OLE Automation.

Statistics and modeling
Incorporate state-of-the-art statistical models and results in your workflow. Find groups in your data using unsupervised techniques including cluster analysis, principal components, factor analysis, multidimensional scaling, and correspondence analysis. Understand your groups even better using latent class analysis. When your analysis calls for supervised techniques, Stata has flexible nonparametric methods and an array of regression models from linear and logistic models to mixture models. Stata keeps up when your data call for special techniques. You have access to methods that understand and take advantage of the structure in time series, panel data, survival data, complex survey data, spatial data, and multilevel data. Stata provides the most approachable implementations of Bayesian methods and structural equation modeling available anywhere. You can request bootstrap methods for virtually any estimator. When your analysis calls for it, Stata automates other replication methods and simulations.

Stata is the only software for data science and statistical analysis featuring a comprehensive version control system that ensures your code continues to run, unaltered, even after updates or new versions are released. No need to keep around multiple legacy installations to avoid breaking your system; Stata code from 25 years ago can still be run without modification. Datasets, graphs, scripts, programs, and more are 100% cross-platform and backward compatible.

Use lasso and elastic net for model selection and prediction. And when you want to estimate effects and test coefficients for a few variables of interest, inferential methods provide estimates for these variables while using lassos to select from among a potentially large number of control variables. You can even account for endogenous covariates. Whether your goal is model selection, prediction, or inference, you can use Stata's lasso features with your continuous, binary, count, or time-to-event outcomes.

Check out Stata's full list of features, or see what's new in Stata 18.

Why Stata?

Intuitive and easy to use.
Once you learn the syntax of one estimator, graphics command, or data management tool, you will effortlessly understand the rest.

Accuracy and reliability.
Stata is extensively and continually tested. Stata's tests produce approximately 5.8 million lines of output. Each of those lines is compared against known-to-be-accurate results across editions of Stata and every operating system Stata supports to ensure accuracy and reproducibility.

One package. No modules.
When you buy Stata, you obtain everything for your statistical, graphical, and data analysis needs. You do not need to buy separate modules or import your data to specialized software.

Write your own Stata programs.
You can easily write your own Stata programs and commands. Share them with others or use them to simplify your work. Utilize Stata's do-files, ado-files, and Mata: Stata's own advanced programming language that adds direct support for matrix programming. You can also access and benefit from the thousands of existing Stata community-contributed programs.

Extensive documentation.
Stata offers 35 manuals with more than 18,000 pages of PDF documentation containing detailed examples, in-depth discussions, references to relevant literature, and methods and formulas. Stata's documentation is a great place to learn about Stata and the statistics, graphics, data management, and data science tools you are using for your research.

Top-notch technical support.
Stata's technical support is known for their prompt, accurate, detailed, and clear responses. People answering your questions have master's and PhD degrees in relevant areas of research.

Learn more

Would you like to see Stata in action?

Join us for one of our free live webinars. Ready. Set. Go Stata shows you how to quickly get started manipulating, graphing, and analyzing your data. Or, go deeper in one of our special-topics webinars.

Would you like to see more?

Stata's YouTube has over 300 videos with playlists for a variety of methodologies important to data scientists. And they are a convenient teaching aid in the classroom.

Visit our channel

NetCourses: Online training made simple

Get started quickly at using Stata effectively, or even learn how to perform rigorous time-series, panel-data, or survival analysis, all from the comfort of you home or office. NetCourses make it easy.

For Stata users, by Stata users

Stata Press offers books with clear, step-by-step examples that make teaching easier and that enable students to learn and data scientists to implement the latest best practices in analysis.

Alan C. Acock

Alan C. Acock

Nicholas J. Cox

James W. Hardin and Joseph M. Hilbe

Ulrich Kohler and Frauke Kreuter

J. Scott Long and Jeremy Freese

Michael N. Mitchell

Michael N. Mitchell

Michael N. Mitchell

Sophia Rabe-Hesketh and Anders Skrondal

Michael N. Mitchell