What is Data Handling? Understanding The Basics

  • Different Data Types
  • Significance of collecting accurate and suitable data
  • Data Handling Steps
  • How to represent data?

Different Data Types

Qualitative Data Type

  • Nominal: These are the values that don’t follow any kind of natural order. Let’s have a look at a few examples to assist you to understand. The color of a smartphone can be considered as a hypothetical data type because we can’t compare one hue to another.
  • Ordinal: These values have a natural ordering while remaining within their class of values. When it comes to clothing brands, we can easily classify them according to their name tag in small, medium, and big order. The grading method used to assess candidates in an exam may also be thought of as an ordinal data type, with A+ clearly superior to B grade.

Quantitative Data Type

  • Discrete: This category includes numerical values that are either integers or whole numbers. The amount of speakers in a phone, cameras, processing cores, and the number of SIM cards supported are all instances of discrete data.
  • Continuous: Fractional numbers are treated as continuous values. These might include the processors’ working frequency, the phone’s Android version, Wi Fi frequency, core temperature, and so on.

Significance of collecting accurate and suitable data

  1. Incapacity to appropriately answer research questions.
  2. The failure to replicate and confirm the study leading to skewed findings, wasting resources and prompting other academics to follow useless areas of exploration, jeopardizing public policy choices and causing harm to human participants and animal subjects.
  3. While the extent of the impact of inaccurate data collecting may vary depending on the field and type of the inquiry, when these research findings are used to support public policy recommendations, there is a risk of creating disproportionate harm.

Data Handling Steps

  1. Data Collection: Data pertinent to the issue statement is gathered.
  2. Data Presentation: The obtained data should be reported in a logical and understandable manner. It is possible to accomplish this by organizing the obtained data in tally marks, table shapes, and so on.
  3. Graphical Representation: Because visual or graphical representation of data facilitates analysis and comprehension, provided data can be displayed in graphs, charts such as bar graphs, pie charts, and so on.
  4. Data Analysis: The data should be analyzed so that the essential information can be derived from the data, allowing for subsequent actions to be taken.

How to represent data?

  • Grouped Bar Graph: A grouped bar graph is a visual representation of data from sub-categories of the primary categories.
  • Stacked Bar Chart: A stacked bar chart also shows sub-groups, but the sub-groups are stacked on the same bar.
  • Segmented Bar Graph: A type of stacked bar chart where each bar shows 100% of the discrete value. They should represent 100% on each of the bars or else it’s going to be an ordinary stacked bar chart.
  • Double Bar Graph: When comparing two data sets, a double bar graph might be utilized. In a double bar graph, there are two axes. The x-axis of a double-bar graph represents the comparison categories, while the y-axis shows the scale. A scale is a collection of integers that represent data at equal intervals. It is critical to understand that all double bar graphs must have a title. The title of the double- bar graph gives the viewer a basic summary of what is being measured and compared. A key will be included with a double bar graph. The key to a double bar graph is to use two different colors to depict the groups being compared.
  • Portrays the number’s earliest digits (thousands, hundreds, or tens) as the stem and the last digit (ones) as the leaf.
  • Usually, entire numerals are used. Any number with a decimal point is rounded to the nearest whole number.
  • When test results, speeds, heights, and weights are flipped on their side, they resemble a bar graph. It displays how the data is distributed — that is, the highest number, the lowest value, the most common number, and the outliers (a number that lies outside the main group of numbers). This provides valuable insights.

Summing up

  1. What Is The Difference Between GUI And CUI?
  2. Understanding Data Mining Architecture In Detail
  3. Key Constraints In RDBMS: Difference Between Primary Key And Foreign Key
  4. What Is A Data Model In DBMS? What Are Its Types?

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Unstop (formerly Dare2Compete) enables companies to engage with candidates in the most interactive way to discover, assess, and hire the best talent.