What is Data: Types of Data, and How To Analyze Data?

Since the invention of computers, people have used the term data to refer to computer information, and this information was either transmitted or stored. But that is not the only data definition; there exist other types of data as well. So, what is the data? Data can be texts or numbers written on papers, or it can be bytes and bits inside the memory of electronic devices, or it could be facts that are stored inside a person’s mind. And in this article, we will cover the following topics in detail:

- What is data?

- Types and uses of data

- Two ways to analyze data

- Reasons to become a data scientist 

- Top 5 data-related jobs

What is Data?

Now, if we talk about data mainly in the field of science, then the answer to “what is data” will be that data is different types of information that usually is formatted in a particular manner. All the software is divided into two major categories, and those are programs and data. Programs are the collection made of instructions that are used to manipulate data. So, now after thoroughly understanding what is data and data science, let us learn some fantastic facts.

Types and Uses of Data

Growth in the field of technology, specifically in smartphones has led to text, video, and audio is included under data plus the web and log activity records as well. Most of this data is unstructured.

The term Big Data is used in the data definition to describe the data that is in the petabyte range or higher. Big Data is also described as 5Vs: variety, volume, value, veracity, and velocity. Nowadays, web-based eCommerce has spread vastly, business models based on Big Data have evolved, and they treat data as an asset itself. And there are many benefits of Big Data as well, such as reduced costs, enhanced efficiency, enhanced sales, etc.

The meaning of data expands beyond the processing of data in computing applications. When it comes to what data science is, a body made of facts is called data science. Accordingly, finance, demographics, health, and marketing also have different meanings of data, which ultimately make up different answers for what is data.

How To Analyze Data?

Ideally, there are two ways to analyze the data:

1. Data Analysis in Qualitative Research
2. Data Analysis in Quantitative Research

1. Data Analysis in Qualitative Research

Data analysis and research in subjective information work somewhat better than numerical information as the quality information consists of words, portrayals, pictures, objects, and sometimes images. Getting knowledge from such entangled data is a confounded procedure; thus, it is usually utilized for exploratory research as well as data analysis.

Finding Patterns in the Qualitative Data

Although there are a few different ways to discover patterns in the printed data, a word-based strategy is the most depended and broadly utilized global method for research and analysis of data. Prominently, the process of data analysis in qualitative research is manual. Here the specialists, as a rule, read the accessible information and find monotonous or frequently utilized words.

2. Data Analysis in Quantitative Research

Preparing Data for Analysis

The primary stage in research and analysis of data is to do it for the examination with the goal that the nominal information can be changed over into something important. The preparation of data comprises the following. 

1. Data Validation
2. Data Editing
3. Data Coding

For quantitative statistical research, the utilization of descriptive analysis regularly gives supreme numbers. However, the analysis is never adequate to show the justification behind those numbers. Still, it is important to think about the best technique to be utilized for research and analysis of data fitting your review survey and what story specialists need to tell.

Consequently, enterprises ready to make due in the hypercompetitive world must have a remarkable capacity to investigate complex research information, infer noteworthy bits of knowledge, and adjust to new market needs.

Top Reasons to Become a Data Scientist: Jobs in Data

Mentioned below are the uses of Data that explain how becoming a data scientist is the right choice to make. 

1. Data Science is used to detect Risks and Frauds. Initially, Data science was used in the Finance sector and the same continues to be the most significant application of Data Science.
2. Next is the Healthcare Sector. Here, data science is used for analyzing medical images, Genetics, and Genomics. It is also applicable to the development of drugs as well. And lastly, it is of great advantage for becoming a virtual assistant for patients.
3. Another application of data science is an internet search. All the search engines make use of data science algorithms to show the desired result.
4. Many other applications of data science or artificial intelligence alike include targeted advertising, advanced recognition of images, recognition of speed, planning of airline route, augmented reality, and gaming, etc.

Take a Data Science course with Simplilearn to satiate your needs to become a successful data scientist.

Top 5 Jobs in Data

Mentioned below are the names of a few job titles that are high in-demand.

1. Data Scientist

This is one of the most in-demand jobs right now, as evident from the previous section.

2. BIA

Business Intelligence Analysts help the companies to make fruitful decisions with the help of using data and making the required recommendations.

3. Database Developer

Third, in the list of the top 5 jobs in data is “database developer.” They are mainly focused on improving the databases and developing new applications for better use of data.

4. Database Administrator

The job of a Database administrator is to set up the databases then maintain and secure them at all times.

5. Data Analytics Manager

Nowadays, more and more companies are starting to rely on data managers to extract out the most useful information from massive amounts of data.

Therefore, the scope in the field of data is immense. These were just five that we listed; there are many others like the Data Engineer certification course, Data security administrator. This shows that a career in any field of Data Science and Analytics is a promising one.