Data Preparation is the method for pre-processing unstructured or raw data to make it suitable for analysis. It consists of collecting, combining, editing, cleaning of data in the machine learning, data mining, and data science community. Some other terms used for it is Data Wrangling, Data Munging or Data Cleaning. Components of Data Preparation There are different components […]
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Technology is constantly evolving. Apparently only a handful of big players lead the industry by offering blockchain-related tools. But the reality is a bit different. Today, many industries recognize blockchain technology and its potential. During its launch, blockchain was related with negative sentiments due to its relation with cryptocurrencies and bitcoin. Lack of knowlege about the technology
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Industrial revolution took off somewhat like these. Initially, it was mechanization, then mass production on the assembly line then came computers. Now, it’s time for the fourth industrial revolution with IoT, cloud and cognitive computing. Popularly known as Industry 4.0 or I4.0 What Is Industry 4.0 Advancement in Artificial intelligence (AI) and Information Technologies along
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Python has become an integral tool used by data analysts nowadays. Every tool has some packages and libraries’ add-ins, which helps in different analysis. One such vital library in Python is the Pandas. It provides relational data in a labeled and easy-to-comprehend format. It helps in manipulating and analyzing data. Origin of Pandas The name
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Data visualization is basically an effort that helps people understand and analyze data with the help of proper visuals, say graphs, patterns, or correlations. In simple, it is the extraction of information from a text format and presenting it in a pictorial format. This makes the final outcome much more appealing, easy to understand, in
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Nowadays, we have become so dependent on Big Data Analytics that it is difficult to think of almost any industry where it has not shown its presence. Be it in urban development to control traffic, using sensors to detect heavy traffic; or in healthcare department where deadly diseases can be detected early. Even in the
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Error-free and easy coding is a prerequisite for a data analyst.Thus, apart from business sense, a bit of coding with any of the available tools is mandatory. Be it SAS, R or Python. Again, if we compare among these three, SAS has comprehensive documentation and comes with a cost. R is a low-level programming language,
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With sky-rocketing digitization, smartphones becoming a major source of media entertainment, there has been a revolution in the entertainment industry on the usage of data analytics. Media sector has always generated data, be it in its customer databases, log files or so. Media data analytics can prove real business if captured and put to good
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Sports are an organized business nowadays, facilitated by analytics. The sports analytics market may be classified on the basis of application, deployment and the type of sports. Analytics in sports is necessary for team composition, performance optimization and injury prevention. Investment in sports analytics is a long-term plan rather than an immediate solution. Baseball had
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