CO2412: Computational Thinking
Lecture 15
Organizing and Presenting Data Graphically
1. Importance of Data Organization
o Raw data is often difficult to interpret and analyze directly,
necessitating the need for proper organization into tables or graphs.
o Techniques covered include bar charts, pie charts, ordered arrays,
stem-and-leaf displays, frequency distributions, histograms,
polygons, contingency tables, and scatter diagrams.
Variables in Data
1. Definition of a Variable
o A variable is a characteristic that can change over time or vary
between individuals or objects.
o Examples: Hair color, white blood cell count, or the time to failure
of a computer component.
2. Types of Variables
o Quantitative: Variables that are numeric and can be measured.
o Qualitative: Variables that describe qualities or categories.
o Discrete: Variables that take specific values.
o Continuous: Variables that can take any value within a range.
Tables and Charts for Categorical Data
1. Categorical Data
o Categorical data represents categories or groups.
o Common graphical representations include pie charts and bar
charts.
2. Graphing Categorical Data
o Pie Charts: Show the percentage distribution of categories.
o Bar Charts: Represent the frequency or percentage of categories
through the height of bars.
Lecture 15
Organizing and Presenting Data Graphically
1. Importance of Data Organization
o Raw data is often difficult to interpret and analyze directly,
necessitating the need for proper organization into tables or graphs.
o Techniques covered include bar charts, pie charts, ordered arrays,
stem-and-leaf displays, frequency distributions, histograms,
polygons, contingency tables, and scatter diagrams.
Variables in Data
1. Definition of a Variable
o A variable is a characteristic that can change over time or vary
between individuals or objects.
o Examples: Hair color, white blood cell count, or the time to failure
of a computer component.
2. Types of Variables
o Quantitative: Variables that are numeric and can be measured.
o Qualitative: Variables that describe qualities or categories.
o Discrete: Variables that take specific values.
o Continuous: Variables that can take any value within a range.
Tables and Charts for Categorical Data
1. Categorical Data
o Categorical data represents categories or groups.
o Common graphical representations include pie charts and bar
charts.
2. Graphing Categorical Data
o Pie Charts: Show the percentage distribution of categories.
o Bar Charts: Represent the frequency or percentage of categories
through the height of bars.
