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Technology supporting big data

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                                                        Technology supporting big data Introduction : Big data is defined as data that is more diverse, arrives in higher volumes, and moves at a faster rate. Simply said, big data refers to data sets that are larger and more complicated, especially when they come from new sources. Traditional data processing software simply cannot handle these massive data sets. fig-1: Big Data Big data Technology: Big data is a term that is used to indicate a large collection of data that is rapidly growing in size. It simply describes the vast amount of data that is difficult to store, analyse, and convert using traditional management systems.  In reality, Big Data Technologies is the software that combines data mining, data storage, data sharing, and data visualisation. The broad word encompasses data, data frameworks, and tools and techniques used to study and convert data. Machine Learning, Deep Learning, Artificial Intelligence, and the Internet of
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Time Series for Actuaries

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INTRODUTION OF TIME SERIES A time series is just a collection of data points that occur in a logical order across time. On the other hand, cross-sectional data represents a single point in time. A time-series graph shows the evolution of a set of data points over time, such as the price of an asset, with data points recorded at regular intervals. There is no need that the data be gathered for a specific period of time, allowing the data to be collected in a fashion that delivers the information that the investor or analyst investigating the activity requires. Time-series data may be used to anticipate or predict future data based on past data. Aim Of Time Series Describe the data Fit a model Forecast future values Look for connection with other time series Decide if process is out of control Time Series Analysis Time-series analysis is a technique for examining a set of data points across time. Instead of randomly recording data points, time-series analyzers record data points at regul
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Machine learning using python

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Python :   Python is a dynamic, high-level, and interpreted programming language with many uses. It makes utilizing an Object-Oriented programming technique to create apps easier. It's easy to learn and has a variety of high-level data structures. •Python is a powerful and flexible programming language that is simple to learn. It is ideal for application development. •Python's syntax, dynamic type, and interpretability make it an excellent language for scripting and rapid application development. • Python supports object-oriented, imperative, functional, and procedural programming methods. • Python isn't designed for certain jobs, such as web development. Because it can be used with online, enterprise, 3D CAD, and other applications, it is known as a versatile High level language :   • A high-level language is any programming language that enables for the creation of programming in a much more user-friendly programming environment that is unaffected by the system's hard
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