Startup companyA startup or start-up is a company or project undertaken by an entrepreneur to seek, develop, and validate a scalable business model. While entrepreneurship includes all new businesses, including self-employment and businesses that do not intend to go public, startups are new businesses that intend to grow large beyond the solo founder. At the beginning, startups face high uncertainty and have high rates of failure, but a minority of them do go on to become successful and influential.
Venture capitalVenture capital (commonly abbreviated as VC) is a form of private equity financing that is provided by venture capital firms or funds to startups, early-stage, and emerging companies that have been deemed to have high growth potential or which have demonstrated high growth (in terms of number of employees, annual revenue, scale of operations, etc). Venture capital firms or funds invest in these early-stage companies in exchange for equity, or an ownership stake.
Business incubatorA business incubator is an organization that helps startup companies and individual entrepreneurs to develop their businesses by providing a fullscale range of services starting with management training and office space and ending with venture capital financing. The National Business Incubation Association (NBIA) defines business incubators as a catalyst tool for either regional or national economic development.
TechCrunchTechCrunch is an American global online newspaper focusing on high tech and startup companies. It was founded in June 2005 by Archimedes Ventures, led by partners Michael Arrington and Keith Teare. In 2010, AOL acquired the company for approximately $25 million. Following the 2015 acquisition of AOL and Yahoo by Verizon, the site was owned by Verizon Media from 2015 through 2021. In 2021 Verizon sold its media assets, including AOL, Yahoo, and TechCrunch, to the private equity firm Apollo Global Management, and Apollo integrated them into a new entity called Yahoo! Inc.
InnovationInnovation is the practical implementation of ideas that result in the introduction of new goods or services or improvement in offering goods or services. ISO TC 279 in the standard ISO 56000:2020 defines innovation as "a new or changed entity realizing or redistributing value". Others have different definitions; a common element in the definitions is a focus on newness, improvement, and spread of ideas or technologies.
Cross-validation (statistics)Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.
Statistical model validationIn statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data.
Training, validation, and test data setsIn machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.
Open innovationOpen innovation is a term used to promote an information age mindset toward innovation that runs counter to the secrecy and silo mentality of traditional corporate research labs. The benefits and driving forces behind increased openness have been noted and discussed as far back as the 1960s, especially as it pertains to interfirm cooperation in R&D.
Fundamental analysisFundamental analysis, in accounting and finance, is the analysis of a business's financial statements (usually to analyze the business's assets, liabilities, and earnings); health; and competitors and markets. It also considers the overall state of the economy and factors including interest rates, production, earnings, employment, GDP, housing, manufacturing and management. There are two basic approaches that can be used: bottom up analysis and top down analysis.
Technical analysisIn finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. As a type of active management, it stands in contradiction to much of modern portfolio theory. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results.
Market timingMarket timing is the strategy of making buying or selling decisions of financial assets (often stocks) by attempting to predict future market price movements. The prediction may be based on an outlook of market or economic conditions resulting from technical or fundamental analysis. This is an investment strategy based on the outlook for an aggregate market rather than for a particular financial asset. The efficient-market hypothesis is an assumption that asset prices reflect all available information, meaning that it is theoretically impossible to systematically "beat the market.
Character encodingCharacter encoding is the process of assigning numbers to graphical characters, especially the written characters of human language, allowing them to be stored, transmitted, and transformed using digital computers. The numerical values that make up a character encoding are known as "code points" and collectively comprise a "code space", a "code page", or a "character map". Early character codes associated with the optical or electrical telegraph could only represent a subset of the characters used in written languages, sometimes restricted to upper case letters, numerals and some punctuation only.
Control characterIn computing and telecommunication, a control character or non-printing character (NPC) is a code point in a character set that does not represent a written character or symbol. They are used as in-band signaling to cause effects other than the addition of a symbol to the text. All other characters are mainly graphic characters, also known as printing characters (or printable characters), except perhaps for "space" characters. In the ASCII standard there are 33 control characters, such as code 7, , which rings a terminal bell.
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Data modelA data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The corresponding professional activity is called generally data modeling or, more specifically, database design.
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
