Data is the new GOLD; unarguably so.
Data is information; in its raw form. It can be translated into forms efficient for processing or analysis. Tools like Ms. Excel, Python, SQL, and PowerBI are data analytics tools.
The major focus of this write-up will be data in statistics.
Understanding the various types of data allows you to choose the data that most fits your desired results and goals; mainly, decision-making.
The types of data that exist are Quantitative data and Qualitative data.
Quantitative data can be measured by arithmetic variables. For example; the weight of an object, the temperature of a human, and trouser size.
We have two types of quantitative data namely: discrete data, and continuous data.
- Discrete data is a numerical type of data that includes whole numbers that can be counted.
- E.g. The number of pupils in a classroom.
- Continuous data on the other hand cannot be counted but has values that can be measured.
- E.g. The speed of a motorbike.
Refer to the table below:
Qualitative data, on the other hand, represents data that cannot be measured or expressed as a number. They consist of words, figures, pictures, videos et al.
Examples of Qualitative data are nominal data and ordinal data.
Nominal data is used to denote variables without any quantitative value. Examples are colors, names, nationalities et al.
Ordinal data is used for categorization, they are listed in an orderly manner of position, for example, first, second, good, better, best, bad et al.
Data analytics is the process of analyzing these raw data and coming to a conclusion with the information. Businesses use it to optimize their performance, for better efficiency, and make better decisions.
Data analytics users are vast: banking, communications, healthcare providers, education, manufacturing, government, insurance, retail and wholesale store amongst others make use of data analytics to make their businesses more efficient. This analysis helps reduce costs by identifying better and more efficient ways to optimize, analyze customer trends, and to make better decisions.
Data Analytics steps.
- The first step is to determine the type of data required, qualitative or quantitative.
- The second step is the collection, how we collect our data. Data can be collected via various means such as computers, questionnaires et al.
- Once the data is collected, we have to organize it so it can be analyzed. This is where software like Ms. Excel comes in.
- Clean up the data, and make it as complete as possible. When all this is done efficiently, a data analyst can then analyze the data.
The data analyst is only able to analyze complete data; complete data is one without any gaps or missing information. Using incomplete data can lead to costly mistakes and lead to missed opportunities, as it gives partial information.
Data is the present and the future, those that are able to pick a particular vocation in the vast data analytics space, and master it, live to reap high rewards, it is one of the most demanded professions in the current day and age.