Data Analytics focuses on processing and maintaining statistical analysis of existing datasets. Data Analytics depends on the methods and techniques that drive the raw data to the primary goals of the business and drill this information to convert metrics, facts, and statistics into improvement initiatives
There are various methods for data Analytics, most commonly there are two: quantitative data Analytics and qualitative data Analytics.
Quantitative data refers to information collected from each individual of a large group about the opinions of a particular group of services. This type of data is the result of conducting surveys. Quantitative data analytics is all about analyzing number-based data. There are two methods of quantitative Data Analytics descriptive statistics and inferential statistics.
Quantitative data analysis may include the calculation of the frequencies of variables and the differences between the variables
Qualitative Data Analytics involves the identification, testing, and interpretation of patterns and topics in textual data. Data is collected from a variety of sources, including social media platforms, customer feedback, and various solutions to various problems. There are several methods to analyze data obtained through exploration. The most commonly used methods are content analysis, narrative analysis, discourse analysis, etc.
- Preparing and organizing the data.
- Review and explore data.
- Creating initial codes.
- Revising codes and incorporating them into themes.
- Present themes in a cohesive manner.
- Descriptive Data Analytics
- Diagnostic Data Analytics
- Predictive Data Analytics
- Prescriptive Data Analytics
- Data Requirement Specification
- Data Collection
- Data Processing
- Data cleaning
- Data Analysis
- Modeling and algorithms
- Infer and Interpret Results