Data analytics backed by methods and algorithms - a combination for success

Data analytics backed by methods and algorithms - a combination for success

About discipline: Even a sleeper in a dream collects and data collection and processing, information. Dreams themselves are the product of such process for data scientist. Otherwise, in an age of dynamically complicated information is impossible. Although a person, his brain abilities and resources are enormous (when structuring information in a personal data analysis system, for example), the amount of information being extracted (processed) and the processing speed are small. Until recently, databases have helped. Now, the exponential growth of data is helped by Big Data, and extraction from data of hidden links, dependencies or Mining Data - Data Mining.

Big Data - large, poorly structured data stored on digital media (in digital streams). They should be processed according to a special methodology, using specialized tools, tools (methods and programmes). For example, storage and organization of personal data in Big Data.

In information streams that "serve" Big Data, it is important to highlight hidden, but very relevant connections that affect the big data toolkit. There are multilateral capabilities of Data Mining, the extraction of data links for analytical research.

The key property of the discipline: gives a set of competencies that form concepts about Big Data, their use, about Data Mining, its capabilities. The goals of the test are to interest the user in processing, collecting large amounts of data about each user, including himself. Check how much the user distributes and transmits about himself data in different systems and services, whether he knows about the rules for processing personal data and give advice on competent handling of personal data.

Key verifiable concepts: big data, processing unstructured information, searching for hidden (non-surface) links in data.

Required level: school course in informatics and ICT.
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