Data wrangling has become the primary process to remain competitive for organizations. Data is the backbone of the digital age, and with growing volume leading to data explosion, the need for effective data handling becomes paramount. Among the essential processes in the realm of data science is Data Wrangling. This article delves into the intricacies […]
Data Wrangling : Understanding, Why its Important Read More »
The process of extracting, transforming and loading (ETL) is a fundamental aspect of modern data integration. ETL is used to consolidate data from multiple sources, transform it into a format that can be used for analysis and load it into a target system. However, the ETL process can be time-consuming, complex and error-prone. In recent
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Data democratization is the process of making data accessible to everyone in an organization, irrespective of their technical know-how as a result, make data-informed decisions and build customer experiences powered by data. Inaccessibility of data to business users can lead to loss of opportunities for many organizations. Purpose of Data Democratization Before data democratization became
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Data Quality Management, first step is to measure of how fit a data is to serve specific needs of business. Quality data is always useful. Business leaders are gradually becoming aware of the importance of the quality of data and its impact. However, if data is not complete or consistent, then it can lead to
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Hive is a data warehousing engine to facilitate interaction between user and Hadoop Distributed File System (HDFS). It helps to process and analyze huge amounts of data built on top of Hadoop. It also helps non-programmers to understand and manage large datasets inside distributed Hadoop storage by using SQL-like queries. Earlier we used MapReduce for
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Time Series is a statistical technique that deals with time series data, or trend analysis. Time series data implies the data which is in a series of particular time periods or intervals. Whereas, sequence data mining signifies finding statistically relevant patterns between data examples where the values are delivered in a sequence. Study of time series
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RapidMiner is an open-source, data science software platform that provides environment for data mining, predictive analytics, clustering and machine learning. To make the data mining process smooth, it has a set of predefined operators. It consists an array of tools that provides centralized solutions for advanced data analytics. It ensures smooth transformation from modelling to
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In modern business environment, to leverage data for making more informed decisions, there needs to be a cultural realignment. Data if put in the centre of the decision-making process, elevates its value to a higher level. Organizations who can adjust to data culture at the earliest will gain a competitive advantage resulting from being early
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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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