Data Visualization is an important part of the CBSE Class 9 Skill Education – Data Science (Subject Code 419) curriculum. It helps students understand how raw data can be represented visually using graphs, charts, and plots to make it easier to analyze and interpret.
Data Visualization Class 9 Notes
Importance of data visualisation
Data visualisation means showing raw data using graphs, charts, maps and other visuals. Data visualisation helps to understand the data. Suppose you have a chart paper containing the daily attendance of the last three years; now you have to figure out which month had the highest attendance. It will be difficult to identify, but if you have the same data in a bar graph, then the answer becomes clear and you can easily identify it. So, data visualisation helps to understand the complex data, helps to make smart decisions, shows the patterns and trends quickly, and explains ideas clearly.
Let us now look at a few real-life uses of data visualisations.
- Tracking student progress with scorecards: Student scorecards help to understand the student’s progress, strengths and weaknesses. It helps the teachers and parents to give better support.
- Identifying usage trends of a website: Suppose you are a website administrator and you want to identify how many people are visiting your website. To do this, you have to track, and for quick identification, you have to display the tracked data in a graph.
- Monitoring goals and results of a sales executive: A salesperson has a sales target every day or month. If, suppose, the salesperson has to identify whether the target is achieved or not, in that condition they can take the help of a graph.
- Visualising the spread and impact of pandemics: Pandemics like COVID-19have impacted the entire world. Data visualisation techniques help to identify the most affected countries or regions. They also show a trend if the spread increases or decreases.
Plotting data
There are several different ways to visualise data depending on the data being modelled and its purpose. Several other graphs and tables can be used to visualise the data.
1. Dot Plot
A dot plot is a simple graph that uses dots to show data. In a dot plot, each dot stands for a value, and we place the dots above categories to show how many times each value appears. The first time a dot plot is used is in 1884, even before computers.
Why use dot plots?
- Easy to understand the data
- Good for a small set of data
- Helps spot patterns and differences quickly
2. Bar Graph
A bar graph is a graphical display of data using bars of different heights. It is possible to plot the bars vertically or horizontally. A vertical bar graph is called a column chart or graph.
Minimum and Maximum in a Bar Chart?
In a bar chart, each bar shows a value like marks, sales, or number of books read. ‘Minimum’ means the shortest bar in the chart; it shows the smallest value in the data, and ‘maximum’ means the tallest bar in the chart; it shows the largest value in the data.
Frequency
The frequency of a data value is the number of times the data value occurs/repeats. For example, if five students have a score of 85 in English, the score of 85 is said to have a frequency of 5. It is written as f(value), meaning f(5).
Why Use a Bar Graph?
- It’s easy to understand the data.
- Quick comparisons
- Shows patterns
- Helpful for many types of data like attendance, test scores, sales, etc.
3. Histograms
A histogram is a special type of bar graph that shows how often data falls into certain ranges called ‘bins’. In histogram grouped the individual values. For example, if you measure student heights, the histogram groups the students into height ranges like 100-102 cm, 103-105 cm, etc.
Why Use a Histogram?
- Groups data into bins
- Great for measurements
- Finds the mode easily
- Shows patterns clearly
Shapes of a Histogram and What They Mean
Histograms have different shapes, and each and every shape tells us something important about the data. For example,
- Normal Distribution: Looks like a bell curve. Most of the data are around the average. For example, heights of students in a class.
- Right-Skewed Distribution: Many data points occur on the left side, with fewer data points on the right side in a right-skewed distribution.
- Left-Skewed Distribution: Many data points in a left-skewed distribution occur on the right side with a scarcer number of data points on the left side.
- Bimodal Distribution: A bimodal distribution has two peaks. In a bimodal distribution, the data should be separated and analysed as separate normal distributions.
- Random Distribution: A random distribution lacks an apparent pattern and has several peaks.
Use of Single- and Multi-Variable Plots
1. Single-variable plots
To visualise one variable, the type of graph to use depends on the type of the variable:For categorical variables (or grouping variables). You can visualise the count of categories using a bar plot or a pie chart to show each type’s proportion. You can visualise the variable’s distribution for a continuous variable using density plots, histograms, etc.
2. Multi-variable plots
Multi-variable plots are used to display relationships among several variables. Example: We have a survey of how many students enrolled in schools 1 and 2 from 1995 to 2006.
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