## Secrets of successful data analysis - Sykalo Eugene 2023

# Data Visualization - Principles of data visualization and popular tools

Data Analysis Tools and Techniques

## Introduction to Data Visualization

Data visualization is the graphical representation of data and information. It is an essential tool for data analysis, as it allows for the communication of complex information in a clear and concise manner. Data visualization is used in various industries, including finance, healthcare, marketing, and education. It helps organizations to understand their data, identify trends and patterns, and make informed decisions.

The history of data visualization dates back to the 17th century when charts and graphs were used to represent data. William Playfair was one of the pioneers of modern data visualization, and he invented several types of charts, including the bar chart and line graph. In recent years, advances in technology have made it easier to create and manipulate data visualizations.

Data visualization can be used to represent different types of data, including quantitative and qualitative data. It can also be used to show relationships between data points, compare data sets, and highlight trends over time. Some common types of data visualizations include bar charts, line graphs, pie charts, scatter plots, and maps.

When creating data visualizations, it is important to consider the target audience and their needs. Different audiences may require different types of visualizations to effectively communicate the information. It is also important to select the appropriate charts and graphs to represent the data accurately. The use of color, typography, and layout can also impact the effectiveness of a data visualization.

## Principles of Data Visualization

Data visualization principles are a set of guidelines that help to create effective and visually appealing data visualizations. The goal of data visualization is to communicate complex information in a clear and concise manner. The following are some of the principles of data visualization:

### Understanding the Target Audience and Their Needs

One of the most important principles of data visualization is understanding the target audience and their needs. Different audiences may require different types of visualizations to effectively communicate the information. For example, a business audience may prefer a bar chart or line graph, while a scientific audience may prefer a scatter plot. Understanding the audience's needs can help to create a more effective data visualization.

### Selecting the Appropriate Charts and Graphs

Another key principle of data visualization is selecting the appropriate charts and graphs to represent the data accurately. Different types of data may require different types of visualizations. For example, time-based data may be best represented using a line graph, while categorical data may be best represented using a bar chart. It is important to choose the appropriate visualization to accurately represent the data.

### Importance of Color, Typography, and Layout

The use of color, typography, and layout can impact the effectiveness of a data visualization. The use of color can help to highlight important information or to differentiate between different data sets. Typography, such as font size and style, can help to make the visualization more readable. The layout of the visualization can also impact its effectiveness. For example, a cluttered visualization may be difficult to read and may not effectively communicate the information.

### Consistency

Consistency is another important principle of data visualization. Consistent use of color, typography, and layout can help to create a cohesive and effective visualization. It is important to maintain consistency throughout the visualization to avoid confusion and to increase readability.

### Simplicity

Simplicity is an important principle of data visualization. A simple visualization can be more effective than a cluttered or complicated one. It is important to focus on the most important information and to avoid unnecessary details.

## Types of Charts and Graphs

There are several types of charts and graphs that are commonly used in data visualization. Each type of chart or graph has its own strengths and weaknesses and is best suited for different types of data. The following are some of the most commonly used types of charts and graphs:

### Bar Charts

A bar chart is a type of chart that uses bars to represent the data. Bar charts are useful for comparing data sets and for showing changes in data over time. They are often used to represent categorical data, such as survey results or sales data. Bar charts can be vertical or horizontal, depending on the orientation of the data.

### Line Graphs

A line graph is a type of chart that uses lines to represent data points. Line graphs are useful for showing trends in data over time. They are often used to represent quantitative data, such as stock prices or temperature changes. Line graphs can be useful for identifying patterns and for making predictions about future trends.

### Scatter Plots

A scatter plot is a type of chart that uses dots to represent data points. Scatter plots are useful for showing the relationship between two variables. They are often used to represent quantitative data, such as the relationship between height and weight or the relationship between temperature and humidity. Scatter plots can be useful for identifying outliers and for determining whether there is a correlation between two variables.

### Pie Charts

A pie chart is a type of chart that uses slices of a circle to represent the data. Pie charts are useful for showing the proportion of different parts of a whole. They are often used to represent categorical data, such as the distribution of different types of products sold. Pie charts can be useful for making comparisons between different categories.

### Histograms

A histogram is a type of chart that uses bars to represent the frequency distribution of a data set. Histograms are useful for showing the distribution of data and for identifying patterns. They are often used to represent quantitative data, such as test scores or income levels. Histograms can be useful for identifying outliers and for determining whether a data set is normally distributed.

### Heat Maps

A heat map is a type of chart that uses colors to represent data. Heat maps are useful for showing patterns in large data sets. They are often used to represent quantitative data, such as the distribution of temperatures across a region. Heat maps can be useful for identifying hot spots or areas of high activity.

Chart/Graph Type | Use |

Area Chart | Showing the relationship between two variables for quantitative data |

Bar Chart | Comparing data sets and showing changes in data over time for categorical data |

Box Plot | Showing the distribution of data and identifying outliers for quantitative data |

Bubble Chart | Showing the relationship between three variables for quantitative data |

Candlestick Chart | Showing the price movement of an asset over time |

Choropleth Map | Showing the variation in a variable across a geographic area |

Dot Plot | Showing the distribution of data and identifying outliers for quantitative data |

Gantt Chart | Showing the timeline of a project |

Heat Map | Showing patterns in large data sets for quantitative data |

Histogram | Showing the distribution of data and identifying patterns for quantitative data |

Line Graph | Showing trends in data over time for quantitative data |

Pie Chart | Showing the proportion of different parts of a whole for categorical data |

Radar Chart | Comparing multiple variables for a single data point |

Sankey Diagram | Showing the flow of data or resources through a system |

Scatter Plot | Showing the relationship between two variables for quantitative data |

Stacked Bar Chart | Comparing parts of a whole for categorical data |

Tree Map | Showing the proportion of different parts of a whole for categorical data in a hierarchical structure |

Waterfall Chart | Showing the cumulative effect of positive and negative values on a total |

## Best Practices for Data Visualization

Creating effective and visually appealing data visualizations requires following best practices that ensure the information is presented in a clear, concise, and accurate manner. The following are some best practices for data visualization:

### Keep it Simple

One of the most important best practices for data visualization is to keep it simple. A simple visualization is more effective than a cluttered or complicated one. The visualization should focus on the most important information and avoid unnecessary details. A minimalist approach can help to create a more visually appealing visualization that is easier to understand.

### Use Clear and Descriptive Titles

Titles help to provide context for the visualization and should be clear and descriptive. They should accurately reflect the data being presented and provide a concise summary of the information. The title should be placed prominently and be easy to read.

### Choose the Right Type of Chart or Graph

Selecting the appropriate chart or graph to represent the data is crucial for creating an effective visualization. Different types of data may require different types of visualizations. For example, time-based data may be best represented using a line graph, while categorical data may be best represented using a bar chart. A scatter plot may be best suited for showing the relationship between two variables, while a heat map may be best suited for showing patterns in large data sets.

### Use Appropriate Scales and Axes

The use of appropriate scales and axes is important for creating accurate and effective visualizations. The scales and axes should be labeled clearly and accurately to reflect the data being presented. The scales should also be chosen to make the visualization easy to read and interpret.

### Use Color Effectively

The use of color can help to highlight important information or to differentiate between different data sets. However, it is important to use color effectively and avoid using too many colors or colors that are difficult to distinguish. The colors should be chosen carefully to enhance the readability and effectiveness of the visualization.

### Provide Context and Interpretation

Providing context and interpretation can help to make the visualization more meaningful and informative. The visualization should be accompanied by a clear and concise explanation that provides context for the data being presented. Interpretation can help to make the visualization more actionable and can provide insights into the data and its implications.

### Test and Refine

Testing and refining the visualization is an important best practice for data visualization. The visualization should be tested with the target audience to ensure that it effectively communicates the information. Feedback should be used to refine the visualization and make it more effective and useful.