Coursera.org defines data analysis as extracting meaning from data to make informed decisions. The process mainly entails understanding and analyzing data to suit the desired objective. These data are often subject to analysis in tables, bar charts, graphs, etc.
Data analysis is a vital component of Data Mining and Business Intelligence. When drawing graphs and charts to illustrate data, one must factor in the dependent and independent variables used in research. Data analysis knowledge is essential today due to its applicability in crucial areas, such as corporate business, social, and applied science.
|1.||Data Mining||The data mining technique uses algorithms to extract valuable patterns or knowledge from large datasets. Anyone in the business sector can learn about their client's behaviors through data mining. As a result, this data can be valuable in developing effective marketing strategies, increasing sales volume, and minimizing operational costs.|
|2.||Data Warehousec||A data warehouse involves collecting and managing diverse data sources into a central repository for easy access and analysis. Our Data scientists and engineers use this technique to make informed decisions. Besides, relational databases, CRM, or integrated information systems are helpful when inserting data in the data warehouse.|
|3.||Data Visualization||Data visualization is representations like charts, graphs, and dashboards. It helps in simplifying complex information by presenting it visually. This method primarily utilizes systematic mapping between graphic markers and data values to establish the best visualizations.|
|4.||Data Integration||Data integration combines different datasets from multiple sources to create a comprehensive dataset for analysis. The process usually involves several steps. These include cleansing, ETL mapping, and transformation.|
|5.||Casual Inference||Casual inference entails identifying cause-and-effect relationships between variables based on observed patterns from data. Typically, this approach is practical when concluding the causal connection based on an effect's occurrence status.|
|6.||Main Assignment Body||
Graph summarization entails summarizing large networks or graphs to identify critical nodes or clusters within the network structure. One can obtain summarization from the entire data set to help relay information about the dataset.
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