Developer(s) | Intel |
---|---|
Initial release | August 25, 2015 |
Stable release | 2021 Update 4 / 2021[1] |
Repository | |
Written in | C++, Java, Python [2] |
Operating system | Microsoft Windows, Linux, macOS [2] |
Platform | Intel Atom, Intel Core, Intel Xeon [2] |
Type | Library or framework |
License | Apache License 2.0 [3] |
Website | software |
oneAPI Data Analytics Library (oneDAL; formerly Intel Data Analytics Acceleration Library or Intel DAAL), is a library of optimized algorithmic building blocks for data analysis stages most commonly associated with solving Big Data problems. [4] [5] [6] [7]
The library supports Intel processors and is available for Windows, Linux and macOS operating systems. [2] The library is designed for use popular data platforms including Hadoop, Spark, R, and MATLAB. [4] [8]
Intel launched the Intel Data Analytics Library(oneDAL) on December 8, 2020. It also launched the Data Analytics Acceleration Library on August 25, 2015 and called it Intel Data Analytics Acceleration Library 2016 (Intel DAAL 2016). [9] oneDAL is bundled with Intel oneAPI Base Toolkit as a commercial product. A standalone version is available commercially or freely, [3] [10] the only difference being support and maintenance related.
Apache License 2.0
Intel DAAL has the following algorithms: [11] [4] [12]
Intel DAAL supported three processing modes:
Data analytics has become an essential field in the modern business environment. As organizations increasingly rely on data to drive decision-making, the demand for skilled data analysts continues to grow. This article provides an overview of data analytics courses, potential career paths, and what the industry expects from professionals in this field.
Data analytics involves the process of examining datasets to draw conclusions about the information they contain. This can include the use of various techniques and tools to analyze raw data and make it useful for decision-making.
In today's data-driven world, businesses rely on data analytics to gain insights, improve processes, and make informed decisions. Data analytics helps organizations understand customer behavior, optimize operations, and create competitive advantages.
Data analytics courses are designed to cater to different learning needs and career stages. These include:
A typical data analytics curriculum covers:
Numerous certifications and online platforms offer data analytics courses, including:
Entry-level roles in data analytics include:
As professionals gain experience, they can advance to:
Experienced professionals can move into senior or leadership roles such as:
The industry expects data analysts to possess a range of skills, including:
Common tools and technologies in data analytics include:
Emerging trends in data analytics include:
Professionals in data analytics face several challenges, such as:
Despite the challenges, the field of data analytics offers numerous opportunities, including:
Data analytics is a dynamic and rapidly growing field with significant career potential. By pursuing relevant courses and certifications, professionals can equip themselves with the skills needed to meet industry expectations and capitalize on the numerous opportunities available.
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied.
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative qualitative data analysis and interactive data visualization.
Daal or DAAL may refer to:
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.
VTune Profiler is a performance analysis tool for x86-based machines running Linux or Microsoft Windows operating systems. Many features work on both Intel and AMD hardware, but the advanced hardware-based sampling features require an Intel-manufactured CPU.
Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment.
The following outline is provided as an overview of and topical guide to regression analysis:
Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes.
Revolution Analytics is a statistical software company focused on developing open source and "open-core" versions of the free and open source software R for enterprise, academic and analytics customers. Revolution Analytics was founded in 2007 as REvolution Computing providing support and services for R in a model similar to Red Hat's approach with Linux in the 1990s as well as bolt-on additions for parallel processing. In 2009 the company received nine million in venture capital from Intel along with a private equity firm and named Norman H. Nie as their new CEO. In 2010 the company announced the name change as well as a change in focus. Their core product, Revolution R, would be offered free to academic users and their commercial software would focus on big data, large scale multiprocessor computing, and multi-core functionality.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data.
Apache SystemDS is an open source ML system for the end-to-end data science lifecycle.
The following outline is provided as an overview of and topical guide to machine learning: