Getting started with Python

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Data visualisation is the practise of making data and information more easily understood and used for decision-making via the creation of visual representations of data and information. It's an effective method for discovering, analysing, and sharing information with others.

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Data augmentation, in the context of machine learning, is the process of extending a training dataset in terms of both size and variety via the use of transformations and alterations. Since it can boost the efficiency and generalisation of machine learning models, this method has gained a lot of attention in recent years.

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Grouping data points that are similar into clusters or segments is a common unsupervised machine learning strategy in data science. Similarities and patterns within the data are discovered by the algorithm in this method without the requirement for manual labelling or categorization.

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The huge amounts of data that are now being created, kept, and analysed by companies and other types of organisations are referred to collectively as "big data." The term "big data" refers to the collection of both organised and unstructured data from a wide range of sources, such as consumer transactions, sensors, and social media platforms. 

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Companies and other institutions in the modern digital age produce vast volumes of data daily. In order to be used for analysis, however, this data must be cleaned and changed from its raw form. Data wrangling refers to the method described here. We will define data wrangling, talk about its numerous forms, problems, and best practises, and explore its many uses across various sectors in this blog.

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A dependent variable and one or more independent variables may be modelled with the use of regression analysis, a statistical technique. Finding the optimum line (or curve) to reflect the connection between the variables is the purpose of regression analysis.

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Information and insight may be mined from geographical data via a process known as "spatial data mining." Location-based data, such as satellite images, maps, and GPS coordinates, are the focus of this subfield of data mining.

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By a technique known as "data mining," valuable information may be extracted from massive databases. Businesses in the present age of technology collect vast amounts of data from a wide range of sources, such as social media, customer databases, and site analytics. 

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Data visualisation is the practise of making data and information more easily understood and used for decision-making via the creation of visual representations of data and information. It's an effective method for discovering, analysing, and sharing information with others.

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Data Mesh Architecture (DMA) is a novel strategy for creating and managing large-scale data systems that seeks to address some of the limitations of more conventional, centralised approaches. Instead of having a single group responsible for managing all of the organization's data, Data Mesh Architecture distributes ownership of the data across the many product teams. In this method, data is divided into smaller, more manageable pieces, each of which is the responsibility of a separate product team.

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Data augmentation, in the context of machine learning, is the process of extending a training dataset in terms of both size and variety via the use of transformations and alterations. Since it can boost the efficiency and generalisation of machine learning models, this method has gained a lot of attention in recent years.

Article · · By  

By a technique known as "data mining," valuable information may be extracted from massive databases. Businesses in the present age of technology collect vast amounts of data from a wide range of sources, such as social media, customer databases, and site analytics. 

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Grouping data points that are similar into clusters or segments is a common unsupervised machine learning strategy in data science. Similarities and patterns within the data are discovered by the algorithm in this method without the requirement for manual labelling or categorization.

Article · · By  

The huge amounts of data that are now being created, kept, and analysed by companies and other types of organisations are referred to collectively as "big data." The term "big data" refers to the collection of both organised and unstructured data from a wide range of sources, such as consumer transactions, sensors, and social media platforms. 

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Companies and other institutions in the modern digital age produce vast volumes of data daily. In order to be used for analysis, however, this data must be cleaned and changed from its raw form. Data wrangling refers to the method described here. We will define data wrangling, talk about its numerous forms, problems, and best practises, and explore its many uses across various sectors in this blog.

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Information and insight may be mined from geographical data via a process known as "spatial data mining." Location-based data, such as satellite images, maps, and GPS coordinates, are the focus of this subfield of data mining.

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Regression analysis is a statistical method that is used to study the relationship between a dependent variable and one or more independent variables. It is a powerful tool in data analysis and helps to understand the underlying patterns and trends in the data. Regression analysis can be used in various fields such as economics, engineering, social sciences, and health sciences. 

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Data Science is a multidisciplinary field that combines techniques and methodologies from computer science, statistics, and mathematics to extract insights and knowledge from large amounts of data. In finance, data science plays a crucial role in decision-making, risk management, and financial analysis.

Table Of Contents

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This project can be one to impress recruiters and be the highlight of your resume.

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Acquiring the right expertise on the front end and backend technologies is vital to becoming a viable prospect for recruiters.

Career Corner. 6 minutes.By  NIIT Editorial

A binary tree is one of the most extensively used tree data structures. It is a hierarchical data structure wherein each node has two children, the left child and the right child

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Need help with Linux servers? This deployment guide is at your service

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Ever wondered how audio gets integrated into games?

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