Principle of Data Analysis & Interpretation

How to Interpret Data

Qualitative Analysis

Qualitative Analysis

Scale of Measurement

Statistical Techniques

Visualization Techniques

Data

Analysis

Interpretation

There are 2 main methods in which this can be done, namely;

1.Univariate /Qualitative Analysis

2.Bivariate/Quantitative Analysis

Observation

Documents

Interviews

Mean

Standard Deviation

Frequency Distribution

Nominal Scale (Qualitative)

Ordinal Scale (Qualitative)

Interval (Quantitative)

Ratio (Quantitative)

Descriptive Analysis

Dispersion Analysis

Regression Analysis

Factor Analysis

Time Series Analysis

1

• Data Dashboards

2

• Visualizations (Graphs & Charts)

3

• Maps

4

• Tables

5

• Machine Learning

6

• Artificial Intelligence

Data interpretation and analysis is an important aspect of working with data sets in any field or research and statistics. They both go hand in hand, as the process of data interpretation involves the analysis of data.

The process of data interpretation is usually cumbersome, and should naturally become more difficult with the best amount of data that is being churned out daily. However, with the accessibility of data analysis tools and machine learning techniques, analysts are gradually finding it easier to interpret data.

Data interpretation is very important, as it helps to acquire useful information from a pool of irrelevant ones while making informed decisions. It is found useful for individuals, businesses, and researchers.