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Data lake
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Data stored in a lake can be anything, from completely unstructured data like text documents or images, to semistructured data such as hierarchical web content, to the rigidly structured rows and columns of relational databases. This flexibility means that enterprises can upload anything from raw data to the fully aggregated analytical results.
Database
A database is an information that is set up for easy access, management and updating. Computer databases typically store aggregations of data records or files that contain information, such as sales transactions, customer data, financials, and product information. There are many different types of databases such as Relational databases, Object-oriented databases, Distributed databases, NoSQL databases, Graph databases, OLTP databases, Open-source databases, Cloud databases, Multimodel databases, Document/JSON databases, Self-driving databases, etc.
Decision Tree
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label. A decision tree is a machine learning algorithm used for both regression and classification problems but mostly it is preferred for solving classification problems. In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches.
Deep Learning
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks. It has various applications like self-driving cars, Natural Language Processing and Speech Recognition, Computer Vision, Machine Translation, etc.
Descriptive Analysis
Descriptive Analysis is the type of analysis of data that helps describe, show or summarize data points in a constructive way such that patterns might emerge that fulfill every condition of the data. It can be categorized into four types which are measures of frequency, central tendency, dispersion or variation, and position. It is considered to be more vast than other quantitative methods and provides a broader picture of an event or phenomenon. It can use any number of variables or even a single number of variables to conduct descriptive research.
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