IBM Research Publications | IBM Research
This is our catalog of recent publications authored by IBM researchers, in collaboration with the global research community. We're currently adding our back catalog of more than 110,000 publications. It's an ever-growing body of work that shows why IBM is one of the most important contributors to modern computing.
With analytics, an organization is better able to be descriptive, predictive, and prescriptive-but only if there's a firm connection between what analytics can deliver and what the business is trying to accomplish. How can your organization use analytics to help deliver deeper insights to enable more effective decision making?
Read more of Databricks' resources that include customer stories, ebooks, newsletters, product videos and webinars.
Understanding Traceback in Python - Machine Learning Mastery
When an exception occurs in a Python program, often a traceback will be printed. Knowing how to read the traceback can help you easily identify the error and make a fix. In this tutorial we are going see what the traceback can tell you.
Blog - Analytics Vidhya
Data Science Dojo Video Tutorials
So we created free data science tutorials just for you! Our teaching team consists of leading data scientists and practitioners who are also passionate about teaching. If you like these data science tutorials, why not come and meet them at our data science bootcamp!
Spreadsheets are powerful tools with many applications: collecting data, sharing data, visualizing data, analyzing data, reporting on data. Sometimes, the temptation to do all of these things in a single workbook is irresistible. But if your goal is to provide data to others for analysis, then features that are useful for, say, reporting are downright detrimental to the task of data analysis.
District Data Labs
This is the third post in our Data Exploration with Python series. Before reading this post, make sure to check out Part 1 and Part 2! Preparing yourself and your data like we have done thus far in this series is essential to analyzing your data well.
Pete Warden's blog
Joanne and I got engaged two years ago in Paris, and were planning on getting married in the summer, before the pandemic intervened. Once it became clear that it might be years until everybody could meet up in person, especially older members of our families who were overseas, we started looking into how we could have our ceremony online, with no physical contact at all.
Towards Data Science
Your home for data science. A Medium publication sharing concepts, ideas and codes.
Research Blog | Accenture
Accenture's thought leadership from our researchers uncover industry trends and shape data-driven insights to create innovative solutions. Read more.
International Journal of Data Science and Analytics
Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, ...
Harvard Data Science Review
As an open access platform of the Harvard Data Science Initiative, the Harvard Data Science Review features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability.
Papers with Code - The latest in Machine Learning
Papers With Code highlights trending Machine Learning research and the code to implement it.
Nature Machine Intelligence
Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.
HackerRank is the market-leading technical assessment and remote interview solution for hiring developers. Learn how to hire technical talent from anywhere!
Stack Overflow - Where Developers Learn, Share, & Build Careers
Stack Overflow is the largest, most trusted online community for developers to learn, share their programming knowledge, and build their careers.
Welcome to the MongoDB Documentation
Find the guides, samples, and references you need to use the database, visualize data, and build applications on the MongoDB data platform. Zoe works at a university that uses MongoDB to store student records. Since her background is in SQL, Zoe reads the MongoDB Manual to learn how to build queries using the mongo shell.
Find resources and documentation for new and previous releases of SAS technology.
Beautiful Soup Documentation - Beautiful Soup 4.9.0 documentation
Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. These instructions illustrate all major features of Beautiful Soup 4, with examples.
BigQuery documentation | Google Cloud
Migrating data warehouses to BigQuery Learn patterns and recommendations for transitioning your on-premises data warehouse to BigQuery. Try BigQuery for yourself Create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads. Try BigQuery free
Documentação do Microsoft SQL - SQL Server
Avançar para o conteúdo principal Não há mais suporte para esse navegador. Atualize o Microsoft Edge para aproveitar os recursos, o suporte técnico e as atualizações de segurança mais recentes. Saiba como usar o SQL Server e o SQL do Azure, tanto localmente quanto na nuvem.
Dask: Scalable analytics in Python
Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. You don't have to completely rewrite your code or retrain to scale up.
SageMath Mathematical Software System - Sage
SageMath is a free and open-source mathematical software system.
Introduction to TensorFlow
TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud.
Here is a description of a few of the popular use cases for Apache Kafka®. For an overview of a number of these areas in action, see this blog post. Kafka works well as a replacement for a more traditional message broker.
Home | Qlik Community
New to Qlik Sense I always have to enter a verification code from my passwords that are on my phone. I have Qlik exam soon and would like to have this solved by then. Qlik NPrinting Discussions We used to have NPrinting and QlikView user-based licenses which are expires on 31 Dec 2021.
The Selenium Browser Automation Project
Selenium is an umbrella project for a range of tools and libraries that enable and support the automation of web browsers. It provides extensions to emulate user interaction with browsers, a distribution server for scaling browser allocation, and the infrastructure for implementations of the W3C WebDriver specification that lets you write interchangeable code for all major web browsers.
Altair: Declarative Visualization in Python - Altair 4.2.0rc1 documentation
With Altair, you can spend more time understanding your data and its meaning. Altair's API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.
SymPy is... Free: Licensed under BSD, SymPy is free both as in speech and as in beer. Python-based: SymPy is written entirely in Python and uses Python for its language. Lightweight: SymPy only depends onmpmath, a pure Python library for arbitrary floating point arithmetic, making it easy to use.
"We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]."
Power BI documentation - Power BI
Power BI amplifies your insights and the value of your data. With Power BI documentation, you get expert information and answers to address your needs, no matter how you use Power BI.
A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a projection and mapping toolkit (Cartopy). More Domain-Specific Tools
In addition to the manuals, FAQs, the R Journal and its predecessor R News, the following sites may be of interest to R users: R guides and documentation not contained in the contributed documentation section of CRAN: Translations of the R manuals (An Introduction to R , R Data Import/Export, The R language definition, Writing R Extensions, R Internals) and "R for Beginners" to Chinese by Dr. Guohui Ding.
PyPI · The Python Package Index
The Python Package Index (PyPI) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community. Learn about installing packages. Package authors use PyPI to distribute their software. Learn how to package your Python code for PyPI.
Plotly Python Graphing Library
Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts. Plotly.py is free and open source and you can view the source, report issues or contribute on GitHub.
Apache Spark™ - Unified Engine for large-scale data analytics
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Welcome to Apache Cassandra's documentation!
This is the official documentation for Apache Cassandra. If you would like to contribute to this documentation, you are welcome to do so by submitting your contribution like any other patch following these instructions.
Keras: the Python deep learning API
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.
O Pylab é um módulo da linguagem Python que permite gerar gráficos de duas dimensões de excelente qualidade, permitindo edição interativa, animações, inúmeros tipos de gráficos diferentes, anotações em sintaxe Latex e salvamento das imagens geradas em diversos formatos diferentes.
Use the resources in our Tableau Knowledge Base to learn about new features, explore the Tableau Community, find product-specific answers, and get in-depth product training, from elearning to demo videos and live webinars.
Home · d3/d3 Wiki
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Requests: HTTP for Humans™ - Requests 2.26.0 documentation
Release v2.26.0. ( Installation ) Requests is an elegant and simple HTTP library for Python, built for human beings. Behold, the power of Requests: >>> r = requests . get ( 'https://api.github.com/user' , auth = ( 'user' , 'pass' )) >>> r . status_code 200 >>> r .
json - JSON encoder and decoder - Python 3.10.1 documentation
Source code: Lib/json/__init__.py Compact encoding: Pretty printing: Decoding JSON: Specializing JSON object decoding: Note JSON is a subset of YAML 1.2. The JSON produced by this module's default settings (in particular, the default separators value) is also a subset of YAML 1.0 and 1.1. This module can thus also be used as a YAML serializer.
pandas documentation - pandas 1.3.5 documentation
The reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.
random - Generate pseudo-random numbers - Python 3.10.1 documentation
Source code: Lib/random.py This module implements pseudo-random number generators for various distributions. For integers, there is uniform selection from a range. For sequences, there is uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.
SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.
Introduction - statsmodels
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct.
This documentation is for an unsupported version of PostgreSQL. You may want to view the same page for the current version, or one of the other supported versions listed above instead.
zlib - Compression compatible with gzip - Python 3.10.1 documentation
For applications that require data compression, the functions in this module allow compression and decompression, using the zlib library. The zlib library has its own home page at https://www.zlib.net. There are known incompatibilities between the Python module and versions of the zlib library earlier than 1.1.3; 1.1.3 has a security vulnerability, so we recommend using 1.1.4 or later.
os - Miscellaneous operating system interfaces - Python 3.10.1 documentation
This module provides a portable way of using operating system dependent functionality. If you just want to read or write a file see , if you want to manipulate paths, see the module, and if you want to read all the lines in all the files on the command line see the module.
datetime - Basic date and time types - Python 3.10.1 documentation
Only one concrete class, the class, is supplied by the module. The class can represent simple timezones with fixed offsets from UTC, such as UTC itself or North American EST and EDT timezones. Supporting timezones at deeper levels of detail is up to the application.
cmath - Mathematical functions for complex numbers - Python 3.10.1 documentation
This module provides access to mathematical functions for complex numbers. The functions in this module accept integers, floating-point numbers or complex numbers as arguments. They will also accept any Python object that has either a or a method: these methods are used to convert the object to a complex or floating-point number, respectively, and the function is then applied to the result of the conversion.
XGBoost Documentation - xgboost 1.5.1 documentation
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
torch - PyTorch 1.10.1 documentation
The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities.
Getting Started - PySpark 3.2.0 documentation
This page summarizes the basic steps required to setup and get started with PySpark. There are more guides shared with other languages such as Quick Start in Programming Guides at the Spark documentation.