Data analytics is a process that converts a large amount of unstructured information into information that can be used for marketing decisions.
It is a collection of mathematical techniques for handling the “items” in a table. Several variables are used to describe these items.
Processing information of various types is the goal of data analytics. What are the similar or different items, for instance? What are the connections between the objects and the related variables, too?
Numerous marketing contexts make use of data analytics.
The outcomes of these analyses frequently enable businesses to modify and improve their commercial policies or strategies.
Python: Python is a versatile programming language widely used in data science and analytics. Its libraries, such as NumPy, Pandas, and Matplotlib, provide powerful tools for data manipulation, analysis, and visualization.
R: R is another popular programming language specifically designed for statistical computing and graphics. It has a vast ecosystem of packages like dplyr, ggplot2, and tidy that support data manipulation, visualization, and statistical analysis.
SQL: Structured Query Language (SQL) is a software design language for managing and querying relational databases. It allows you to extract, manipulate, and analyze data stored in databases using commands such as SELECT, JOIN, and GROUP BY.
Tableau: Tableau is a powerful data visualization tool that allows users to make interactive dashboards, reports, and visualizations. It supports various data sources and provides drag-and-drop functionality to build visually appealing visualizations.
Power BI: Power BI is a business intelligence tool by Microsoft that enables users to analyze data and share insights through interactive dashboards, reports, and visualizations. It integrates with various data sources and offers advanced analytics capabilities.
Excel: Microsoft Excel is a widely use spreadsheet program offering basic data analytics capabilities. It provides functions, formulas, and features like pivot tables, charts, and data analysis tools to perform descriptive statistics and basic analysis.
Apache Spark: Apache Spark is an open-source distributed computing system with a unified analytics platform. It supports big data processing, machine learning, and graph processing, making it suitable for large-scale data analytics.
KNIME: KNIME is an open-source data analytics platform that allows users to design data workflows and execute them visually. It provides a wide range of data preprocessing, modeling, and visualization tools.
SAS: SAS (Statistical Analysis System) is a comprehensive analytics software suite offering various tools for data management, analytics, and reporting. It is widely used in healthcare, finance, and government industries.
MATLAB: MATLAB is a software design language and environment specifically designed for numerical computing. It offers built-in functions and toolboxes for data analysis, visualization, and machine learning.
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Data Analysis
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