Page 47 - CT_AI_Class-7

For example, here’s some raw data for the bowlers:

Bowler Balls Bowled Wickets Taken Runs Conceded
DATA VISUALISATION Player A 6 1 25

2
15
AND ANALYSIS Player B 6 0 30
Player C
6
From this list, you can see that Player B bowled the most successful over, taking 2 wickets, while
Player C gave away the most runs.

Now, to make this data useful, you need to organise it. Organising means sorting the data in a
way that helps you understand it better. For example, if you want to know which bowler was most
effective, you can arrange the data based on wickets taken. After organising, the data might look
like this:

Bowler Balls Bowled Wickets Taken Runs Conceded

Player B 6 2 15

Player A 6 1 25

Player C 6 0 30

Now it’s clear that Player B was the most successful bowler, taking 2 wickets, while Player C had

no wickets but gave away more runs.
After organising the data, you can visualise it. Visualisation means presenting the data in a way
that makes it easier to understand, like with charts or graphs. For example, you could create a

bar chart to show how many runs were scored in each over. This helps you quickly see which
overs were the most expensive (i.e., where the most runs were scored). Here’s how the chart
might look for runs scored in each over.

Over Runs Scored
Over 1 10
Over 2 5
Over 3 12
Over 4 8

Over 5 14
Over 6 6

Data Visualisation and Analysis 45