The Most Common Scholarly Data Analysis Frameworks and Models
Students use a collection of tools and affiliated libraries to help them discover, gather, arrange, clean, and interpret scientific information. Data analysis frameworks and the student’s approach to handling research findings are an important scholarly skill. Because it’s hard to pick this art in four short academic years, we offer project-specific data analysis help at student-friendly rates.
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Major Data Analysis Frameworks used by PhD Students in Data Analysis
The 4 major data analysis frameworks utilized by PhD student include:
Descriptive Analytics
Under descriptive analytics, students first collect and aggregate raw data from various sources. Next, convert the information into a common analysis format using data intelligence tools before forming conclusions and action plans. Some common descriptive data analysis techniques include:
- Frequency tables
- Summary statistics
- Measures of dispersion
- Measures of central tendency
- Charts & graphs, And
- Pivot tables
Predictive Analytics
Predictive analytics uses research findings to forecast future outcomes using machine learning, data analysis, AI, and statistical models. A student can find intricate data patterns that might be useful in predicting future organizational behavior.
Diagnostic Analytics
Diagnostic data analytics students examine data to understand causes of events, outcomes, or behaviors. An analyst student uses diverse tools and techniques to identify trends, patterns, and connections to explain why certain phenomena occur.
Prescriptive Analytics
Here, a business student will suggest optional, data-based decisions. They’ll demonstrate how organizations can take advantage of future opportunities (or to mitigate future risks), showing the implications of each option with authoritative data.
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