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Une application 100% anti-gaspillage de fiches de cours, devoirs, etc tapés sur word ★★★★★ Parce que chaque année des milliers de fichiers sont saisis sur word et ne sont plus jamais ouverts et ne servent plus à rien sur votre ordinateur. Plutôt que de gaspiller ces ressources, partageons-les... Des ressources pédagogiques à portée de clics pour apprendre toutes sortes de disciplines et sciences.




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Data Mining Science College University Notes Tests


Go to the Data Mining Courses Notes Tests



Data mining is used to discover patterns and relationships in the data in order to help make better business decisions


Data Mining is is defined as a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Data mining has applications in multiple fields, like science and research. As an application of data mining, businesses can learn more about their customers and develop more effective strategies related to various business functions and in turn leverage resources in a more optimal and insightful manner. This helps businesses be closer to their objective and make better decisions. Data mining involves effective data collection and warehousing as well as computer processing. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms. Data mining is also known as Knowledge Discovery in Data (KDD).

Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. Data mining depends on effective data collection and warehousing as well as computer processing.

Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools (such as cluster analysis) to reveal trends, patterns, and relationships, which might otherwise have remained undetected. In contrast to an expert system (which draws inferences from the given data on the basis of a given set of rules) data mining attempts to discover hidden rules underlying the data. Also called data surfing.

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn’t coined until the 1990s. But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions). What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power.

Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.

"When [data mining and] predictive analytics are done right, the analyses aren’t a means to a predictive end; rather, the desired predictions become a means to analytical insight and discovery. We do a better job of analyzing what we really need to analyze and predicting what we really want to predict."

Predictive Modeling : This modeling goes deeper to classify events in the future or estimate unknown outcomes – for example, using credit scoring to determine an individual's likelihood of repaying a loan. Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaults

Prescriptive Modeling : With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.





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Parce que chaque année des milliers de fichiers word sont tapés sur des ordinateurs portables d'étudiants de toutes discplines confondues (médicales, juridique, sociale, économique,...). Tous ces fichiers qui ont des durées de vie courte ne servent plus à leurs rédacteurs au bout d'un an, mais ils peuvent encore servir aux générations futures qui souhaitent s'instruire. Car l'éducation de transforme en bien de consommation qu'on jette dès qu'on a terminé de consommer un cours. Il est important de pérenniser ses savoirs et de les transmettre aux générations futures et à ceux qui en ont besoin. Avec cette application, vous pourrez continuer à vous instruire grâce au recyclage de fiches de cours et au partage.

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