![]() ![]() Flexibility: It helps the organization to react better to unforeseen changes in fluctuating markets.Business Growth: It helps the organization to reduce maintenance costs and therefore increase the overall profits.Risk Assessment: It helps the organization to gauge unforeseen risks better thus enabling them to implement preventive/corrective measures beforehand.Thus, it helps in fostering long-term business relationships with them. Requirements: It helps the organization to understand the end customer needs/requirements better.Decision Making: It helps the prospective user/organization to make faster, better-informed business decisions backed up by facts.It also helps the user to develop machine learning models and data pipelines. It thus helps the user to run data on a distributed server platform which helps in increasing the productivity and thus the efficiency of the applications. This tool helps the user to process large-scale data by running the data on Hadoop cluster platforms. ![]() It helps the user to get rid of the unprocessed data which comes up from various sources like websites etc. This tool, better known as Google Refine performs data cleaning for analyzing the data. It follows the modular pipelining concept which helps in data reporting and integration. This tool is open-source which helps the user in data analysis and statistical modeling. send their processed data through this tool. Many ETL (Extraction Transformation Loading) tools like Pentaho, Informatica, etc. This tool comes under an open-source license which helps in connecting to any data source like a Data Warehouse or Excel. It can be integrated with multiple platforms like MongoDB, SQL server, or JSON. It also comes up with data visualization libraries like Matplotlib, Seaborn, etc. along with machine learning libraries like scikit-learn, Keras, TensorFlow, etc. It provides various data analytics libraries/packages like NumPy, Pandas, etc. It comes up with CLI (Command Line Interface) tools that help the developer in installing packages as per his/her requirement. It follows the OOP (Object Oriented Programming) directive. This tool comes under MIT license and is open-source and available on Github. It allows the developers to install packages as per his/her requirement through the command line. It comes as a bundled-up package that provides the user with CLI (Command Line Interface) as well as the GUI that is RStudio. It is platform-independent as can be run on multiple platforms like UNIX, Windows, macOS, etc. for performing statistical analysis of data and generating models for the same. This tool is popular among researchers, students, scientists, professional organizations, etc. Let us discuss some of the most popular ones used. To achieve the above, many tools are available in the market. The data analysis can be performed in five stages that is Data Requirement and Gathering, Data Collection, Data Cleaning, Analyzing Data, Data Interpretation, and Data Visualization. setup cost, technological stack, business ideology, customers, etc. The methodology discussed above depends on the requirement of the organization i.e. But all of these can be categorized under either Quantitative or Qualitative Analysis techniques which is more generic. Other types of data analysis techniques are used by developers like Descriptive Analysis, Inferential Analysis, Text Analysis, Statistical Analysis, Diagnostic Analysis, Predictive Analytics, and Prescriptive Analytics. It can numerically aggregate the data and the output categories can be clustered. It follows the test hypothesis statistical technique to gain insights and patterns from the data. It requires independent data from a large statistical sample. It involves predefined output categories. Quantitative Analysis: It helps in measuring the magnitude from a particular practice.It can create patterns from insights and concepts. It can also perform an in-depth analysis of the data coming from a small sample. It does not have any predefined output categories. Qualitative Analysis: It can be used to gain insights into a particular practice.Many techniques have been devised but eventually, it boils down to two categories: The demand for data analytics has increased over the years. ![]()
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