What is Organisation of Data?
Imagine a room full of files and documents kept together in an unorganised manner and you’ve been asked to find one particular file as an urgency. How would it be if you go to a library to find a particular book of a genre you have in your mind and the librarian asks you to find it yourself from 50,000 books kept on 500 different shelves. Anything messier makes the work harder and waste most of your time. Data organization is a tool which helps to organize and classify data sets to make them more useful. With the help of this method many IT experts have been able to apply this primarily to physical records, although there is no doubt that some types of data organization can also be applied to digital records. In fact, there are still many other ways through which IT professionals work on the principle of data organization which under the more general heading are classified as "data management."
Another essential component of enterprise data organization is the analysis of both relatively structured and unstructured data. Structured data holds data in tables that can be easily integrated into a database and can be further fed into analytics software or any other particular applications. Unstructured datas are considered raw and unformatted, just like in a simple text document, where information is scattered throughout random paragraphs. Thanks to few experts who have developed tech tools and resources to handle relatively unstructured data. These datas are integrated into a holistic data environment.
To make better use of the data assets, businesses adopt data organization strategies. Data assets hold a very valuable position in the world as it is held by enterprises across many different industries. Data organization is considered as a component of a comprehensive strategy which helps to streamline business processes whether it be getting better business intelligence or generally improving a business model.
Organisation Of Data In Statistics?
Organisation of data in statistics is a tool or you can say, a process, that organizes the collected factual materials which are considered necessary in the scientific community to validate research findings. Research datas are the ones that are collected, observed or created for the purpose of analysis to produce original research results. But why are they so important that one who acknowledges it is always benefited?
Here is why They are Important:
Datas are intended to showcase facts which will become meaningless if not properly preserved and interpreted.
The collection of data and it’s analysis acts as a right hand for the researcher. It helps them to discover answers to their research questions and hypothesis. In some cases, it even predicts future outcomes.
Ways of Organisation of Data in Statistics In Research
There are three ways to organize data in a research
Frequency Distribution Table
Stem And Leaf Diagram
Chart
Frequency Distribution Table:
In order to construct a frequency table, we need to follow few steps, they are:
Step 1: Construct a table of three columns. In the first column, all the data values are to be written in ascending order.
Step 2: In the second column, we need to go through the list of data values and and place one tally mark at an appropriate place for every data value. Once 5 tally marks are reached, draw a diagonal line through the first four tally marks. This process will be continued until all the datas values in the list are tallied.
Step 3: The number of tally marks are to be counted for each data value and write it in the third column.
Types Of Frequency Distribution
A. Categorical / ungroup: This helps to determine the order to list the categories. The total number of occurrences of each category is listed thereafter.
Example: The following data represents the score of 10 students: 8, 6, 4, 5, 8, 9, 10, 10, 6.
Now, construct a table with three columns. The 1st column represents what is being arranged in ascending order. The lowest mark is 4. So, we have to start from the 1st column as shown below. The second column is tally and the third column is frequency.
B. Group: A group can be defined as data being organized into groups known as classes. There are few things to remember before constructing a table. They are,
Classes between 5 - 20 are to be used
Classes are mutually exclusive
All the classes are to be included even if the frequency is zero.
Width for all the classes would be same
Convenient numbers for the class limit are to be used
The sum of the frequency is the total data set
There should be enough classes for all the datas
If the class has no data, use zero rather than leaving it blank.
Example: The following data represents the ages of 20 respondents
21, 26, 18, 45, 32, 41, 42, 22, 28, 26,
33, 20, 26, 44, 46, 21, 24, 36, 39, 30.
1. Determine the highest and lowest value and then compute the range:
Range = Highest value - Lowest Value
I.e., range = 46 - 18 = 28.
2. Decide the number of classes you want to have.
3. Compute the class width or class interval.
Class Interval = Range/ # of classes
I.e., class interval = 28/5 = 5.6 or 6.
4. Lower class limit (smallest number of each class) and upper class limit (largest number of each class) needs to be mentioned.
Example: LCL = 18, 24, 30, 36, 42.
UCL = 23, 29, 35, 41, 47.
5. Class boundaries - these are the number that separates the classes from one another by subtracting .5 to lower limit and add .5 to upper limit of each class.
Example: (LL) 18 - .5 = 1.5 (class boundary) and (UP) 23 + .5 = 23.5 (class boundary)
And thus plot the table as this:
Stem And Leaf Diagram:
This method is used to organize statistical data to help us to see values according to their size and order them accordingly. Here, each data value is split into a stem and a leaf. The leaf is the digit to the right while the stem is the remaining digits to the left. For example, in the number 243, the stem is 24 and 3 is the leaf.
Graph or Chart:
A graph or a chart condense large amounts of information into easy-to-understand formats that clearly and effectively communicate important points
FAQs on Data Organization
1. What is data organization in Maths as per the CBSE syllabus?
Data organization is the method of systematically arranging raw, collected information into a meaningful and understandable format. For students, this usually involves arranging data in tables, using tally marks, or grouping it to make it easier to interpret and analyse. The primary goal is to see patterns and draw conclusions from the data, such as finding the most frequent item or the range of values.
2. What are the common types of charts used to represent organized data?
According to the NCERT curriculum, the most common visual tools for representing organized data include:
- Pictographs: Using images or symbols to represent data quantities.
- Bar Graphs: Using rectangular bars of uniform width, where the height of the bar represents the frequency of the data.
- Double Bar Graphs: Used to compare two sets of related data.
- Histograms: Similar to bar graphs, but used to show the frequency of data over continuous intervals.
- Circle Graphs or Pie Charts: A circular chart divided into slices to illustrate numerical proportion.
3. Can you give a simple example of organizing raw data?
Certainly. Imagine a teacher collects the marks of 15 students in a science test. The raw data might look like this: 21, 15, 23, 23, 18, 15, 25, 10, 18, 23, 10, 15, 21, 18, 25. To organize this, you could create a frequency distribution table. You would list the unique marks (10, 15, 18, 21, 23, 25) and then count how many times each mark appears. This organized table instantly shows that the mark of '15' and '23' appeared most frequently (3 times each).
4. Why is organizing data considered an essential first step before calculating mean, median, or mode?
Organizing data is crucial because it structures the information, which is necessary for accurate calculations. For example, to find the median, you must first arrange the data in ascending or descending order. To find the mode, having a frequency table makes it easy to spot the most frequent value. Without proper organization, you are likely to make errors, miss values, or find it very time-consuming to calculate these measures of central tendency correctly.
5. What is a frequency distribution table and what is its main importance?
A frequency distribution table is a table that displays the number of times (frequency) each unique value or group of values occurs in a dataset. Its main importance lies in its ability to condense large amounts of raw data into a simple, easy-to-read summary. This helps in quickly identifying the distribution of data, finding the most and least common values, and preparing the data for further analysis like creating a bar graph or histogram.
6. What is the main difference between a bar graph and a histogram?
The main difference lies in the type of data they represent. A bar graph is used to display discrete, separate categories (like favourite colours or types of fruits). There are clear gaps between the bars to show they are distinct. A histogram, on the other hand, is used to represent continuous data that is grouped into intervals or 'bins' (like height ranges or marks grouped in 0-10, 10-20, etc.). In a histogram, there are no gaps between the bars, indicating a continuous range of data.
7. If you have data on the favourite sport of all students in a class, which is a better way to show the data: a pie chart or a line graph? Why?
A pie chart would be the better choice. This is because a pie chart is specifically designed to show the proportion of different categories that make up a whole. In this case, each 'slice' of the pie would represent the percentage of students who chose a particular sport. A line graph is unsuitable because it is used to show trends or changes in data over a continuous period, like temperature changes throughout the day, which doesn't apply to categorical data like favourite sports.
8. How do tally marks help in the process of organizing raw data?
Tally marks provide a quick and efficient method for counting the frequency of data points as you go through a raw list, especially a long one. Instead of writing down numbers and erasing them to update a count, you simply make a mark (a vertical line) for each occurrence. Grouping these marks in sets of five (four vertical lines and one diagonal line across them) makes the final counting process much faster and less prone to errors. It is a fundamental tool for creating a frequency distribution table directly from raw data.
