Libraries tagged by data-analytics

# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Data manipulaton tool

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Datakit is a library that assists with data analysis and research. It includes classes for working with tables of data and deriving statistical information, importing those tables from file formats such as CSV, a class wrapper with statistical methods for PHP arrays, as well as memory efficient packed arrays.

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Using AI to analyze data and receive insights to improve specific areas of your business

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Work out all the data objects and associated table data that has recently changed

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DAP API

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Archon: PHP Data Analysis Library

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PHP SDK for programmatically connecting to Apache Drill.

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Table helper for big data analysis

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A PHP Library for Data Analysis and Data Cleaning

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WebParser is a PHP library that allows developers to parse and query webpages using an ORM-like syntax. It facilitates the extraction of HTML elements by chaining operations such as filtering by ID or class, ordering results, and limiting output. WebParser offers a flexible interface for exploring and extracting data from the web, making it ideal for web scraping and data analysis tasks.

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A Laravel package for seamless integration with DeepSeek AI services, enabling efficient and scalable AI-powered functionalities such as natural language processing, data analysis, and automation through a simple and intuitive API wrapper.

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Search Package OverviewThe Search package is a powerful tool designed to facilitate efficient and effective search operations within various datasets or databases. It provides a set of functions and classes that enable users to perform complex search queries, filter results, and retrieve relevant data with ease. The package is highly customizable, allowing users to define their own search criteria, implement sorting mechanisms, and handle large volumes of data seamlessly.Key Features: Customizable Search Queries: Users can create tailored search queries using various operators and conditions, making it possible to perform both simple and advanced searches. Sorting and Filtering: The package includes built-in methods to sort and filter search results, enabling users to organize data based on specific parameters such as date, relevance, or custom fields. Scalability: Designed to handle large datasets, the Search package is optimized for performance, ensuring quick response times even with millions of records. Integration: The package is compatible with a variety of databases and data sources, making it a versatile solution for different types of projects. User-Friendly Interface: It offers a straightforward API that is easy to use, even for those who are not experts in programming. This allows a broader audience to leverage the power of advanced search capabilities.Use Cases: Data Analysis: Quickly find and retrieve specific information from large datasets for analysis. Content Management Systems: Implement efficient search functionality in content-heavy websites or applications. E-commerce: Enhance product search features in online stores, improving the user experience by providing relevant results swiftly. Knowledge Bases: Help users find relevant articles or documentation based on keyword searches.Overall, the Search package is an essential tool for anyone needing to implement or enhance search functionality in their applications, providing both power and flexibility in managing and retrieving data.

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Enhances Easy Digital Downloads (EDD) database querying with SQL aggregate functions (SUM, AVG, MAX, etc.) and arithmetic operations, enabling sophisticated data analysis and manipulation in WordPress and PHP projects.

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