Search Results for "data analysis"
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Showing 21 open source projects for "data analysis"
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
ThinkStats2 is the code and text companion for the second edition of Think Stats, an introduction to statistics and data science for Python programmers. It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. Case studies draw from public sources, including health-related datasets, to connect abstract concepts with realistic analysis. ... -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
Book5_Essentials-of-Probability-and-Statistics is a Visualize-ML educational volume that introduces the statistical and probabilistic concepts underpinning modern data analysis and machine learning. The repository explains topics such as distributions, sampling, inference, and uncertainty using visual demonstrations and intuitive narratives. Its teaching philosophy prioritizes conceptual clarity over heavy formalism, making statistical thinking more approachable for beginners. The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. ... -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. ... -
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xrayutilities
a package with useful scripts for X-ray diffraction physicists
xrayutilities is a python package used to analyze x-ray diffraction data. It can support with performing diffraction experiments and used for common steps in the data analysis. It can read experimental data from several data formats (spec, edf, xrdml, ...); convert them to reciprocal space for arbitrary goniometer geometries and different detector systems (point, linear as well as area detectors); for further processing the data can be gridded (transformed to a regular grid). ... -
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openSkyMatch
Matches OpenScience Observatories images with astronomical catalogs
openSkyMatch is a collection of Linux shell and Python scripts designed for the OpenScience Observatories program. It automates the identification and matching of detected celestial objects in locally captured FITS images with entries in large-scale sky catalogs, notably Pan-STARRS1 DR2 (II/389/ps1_dr2). The toolkit supports data preprocessing, coordinate correlation, and catalog-based validation of astronomical detections. All tools are open-source and optimized for reproducibility and... -
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Python Zero to Hero for DevOps Engineers
Learn Python from DevOps Engineer point of you
...The repository is organized into Day-01 through Day-19 folders plus a small sample app, which makes it very easy to follow in sequence like a bootcamp. The curriculum starts with Python installation, environment setup, and writing your first script, then quickly moves into data types, strings, regular expressions, variables, and functions. It places a strong emphasis on DevOps-specific use cases: environment variables, command-line arguments, configuration handling, and automating log analysis or user management tasks are all explicitly woven into the exercises. As you progress, you encounter increasingly rich Python features such as lists (with list comprehensions), dictionaries, sets, operators, and control flow, always tied back to practical automation or infrastructure examples. -
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Digital Forensics Guide
Learn all about Digital Forensics and Computer Forensics
The Digital Forensics Guide repository is a comprehensive, structured reference for investigators, analysts, students, and cybersecurity professionals interested in digital forensics principles, tools, methodologies, and workflows. It organizes foundational topics such as evidence acquisition, disk and memory analysis, file system structures, network forensics, artifact extraction, timeline generation, and reporting into digestible modules that help build core competency. Alongside conceptual explanations, the guide includes practical examples with widely used tools (like Autopsy, Volatility, Sleuth Kit, and network analysis suites), illustrating how investigations proceed from initial data capture to final analysis. -
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ACORBA
Automated approach to measure root tip angles of Arabidopsis thaliana
Gravitropic response is studied in most of the laboratories working with Arabidopsis thaliana, for example, to detect new phenotypes in mutants. However, manual analysis of images and microscopy data are known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the angle is set subjectively. In this context, it is essential to develop and use automated or semi-automated image analysis to produce faster, reproducible, and unbiased data. In this context, we developped ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time. -
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training, and deployment. ... -
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EduData
Datasets in Education and convenient interface for dataset
Datasets in Education and convenient interface for downloading and preprocessing dataset in education. The CLI tools to quickly convert the "raw" data of the dataset into "mature" data for knowledge tracing task. The "mature" data is in json sequence format and can be modeled by XKT and TKT(TBA) The analysis dataset tool only supports the json sequence format. To check the following statical indexes of the dataset. In order to better verify the effectiveness of the model, the dataset is usually divided into train/valid/test or using kfold method. ... -
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NiftyNet
An open-source convolutional neural networks platform for research
...Adapt existing networks to your imaging data. Quickly build new solutions to your own image analysis problems. NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. -
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Matplotlib tutorial
Matplotlib tutorial for beginner
...It provides a sequence of example scripts and notebooks that cover fundamental plotting tasks: line graphs, histograms, scatter plots, bar charts, customizing axes, labels, legends, and styling. This makes it ideal for someone learning data analysis or exploratory data visualization for the first time and needing concrete, runnable examples rather than abstract explanations. As the tutorial builds progressively, learners can gradually advance from simple static plots to more complex visualizations, learning how to control figure size, add multiple subplots, adjust plot aesthetics, and handle different data types. ... -
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Collective Mind Technology
plugin-based framework for systematic and reproducible experimentation
New version moved to http://github.com/ctuning/ck Collective Mind framework (cM) is an open-source plugin-based schema-free repository and infrastructure for collaborative, systematic and reproducible research and experimentation. This 3rd version (started in 2006) helps to implement, preserve, share and reproduce the whole experimental setup as connected modules and data. cM uses crowdsourcing to leverage knowledge and computational resources of multiple users. For example, it includes multi-objective GCC, LLVM and ICC auto-tuning scenarios using shared benchmarks, codelets, data sets, tools, and combined with classification and predictive models. cM includes OpenME interactive interface to open up and expose internals of various third-party tools such as GCC, LLVM, run-time systems, etc. and connect them to cM through dynamic plugins that allows online analysis and tuning of programs and architectures. ... -
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This project goal is to provide a way to get screenshot and channels data from digital storage oscilloscope.
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ConDEnSE (Confidential Data Enabled Statistical Exploration) will be a web-based environment for statistical analysis of confidential data from various database sources, based on Plone and R, and using the Jackknife method of confidentiality protection.
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MBDyn_sim_suite is a collection of free pre&post-processing tools and simulation models for the open-source multi-body analysis software MBDyn forming a general purpose simulation environment for structural dynamics with an emphasis on wind turbines.
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File-Spector is a small, fast and easy to use binary file analyzer and Inspector. It allows the users to format a complete binary file structure and then use it to read any binary file that matches the specified format.
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pyCatalog is a Python, MySQL, wxPython, Reportlab application specifically usable in library and information centers. It simply produces book catalog and card catalog in pdf format rendered using reportlab. The program takes MARC file as its source data.
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Rocksnake takes localities from geological mapping projects and plots them in 2D, over an optional basemap. Each locality has all the standard geology notebook contents and photos attached to it which can then be viewed from Rocksnake.
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ExpLab is a set of tools that supports the running, documentation and evaluation of computational experiments.
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