First of all, what is data? Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things. Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. There are two methods of data analysis.
- Quantitative Analysis
- Qualitative Analysis Quantitative Analysis is done through surveys and experiments. While qualitative analysis is through interviews and observation.
Data Analysis Process
- Data Requirement Gathering
- Data Collection
- Data Cleaning
- Data Analysis
- Data Interpretation
- Data Visualization
Data Requirement Gathering: This answers the reason why. Why do you need that particular data. This will help you know they type of data you would need and how to go about it.
Data Collection: In this stage, data is collected based on the requirements.
Data Cleaning: Not all the data collected will be useful. Here you sort the useful from the one that is not necessary.
Data Analysis:
The cleaned data is analysed using data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements. Other data analysis tools include, python, R, SQL, DataPine, Tableau, Sas e.t.c
Data Interpretation: Here the analysed data is interpreted according to the results of the analysis.
Data Visualization: Here the data is shown in visual format so that the human brain will easily interpret the results. They say a picture is worth a thousand words.