Data Visualization in Analytics and Healthcare

The process of analyzing data to reveal patterns, trends, and insights that can be used to make informed decisions.

The practice of representing data visually through charts, graphs, and other visual aids to make it easier to understand and interpret.

Data visualization helps make data analytics useful and effective by presenting findings in a simple and clear way, making it easier for people to understand the meaning behind the data.

Data analytics helps businesses learn from past experiences and make informed decisions for the future. It can also be used to improve healthcare, manage risks in finance, and detect fraud.

The process of using data visualization to create compelling and engaging stories that help convey the insights derived from data analysis.

Data visualization is the process of representing data with visuals like charts, graphs, and maps. It helps you understand patterns and insights in data more easily than just looking at numbers.

You should visualize your data when you need to explore a new dataset, understand trends and patterns, spot outliers, or communicate your findings to others.

Exploratory visualization is used to understand data through initial analysis. It helps you get a sense of the data and discover potential patterns and trends.

Explanatory visualization is used to communicate insights and findings from data analysis. It helps you explain the data story in a clear and concise way.

Exploratory visualization is about finding patterns and trends in data, while explanatory visualization is about sharing these insights with others.

Exploratory visualization helps you understand your data, while explanatory visualization helps you communicate what you've found to others.

Choosing the right type of visualization depends on the data you have and the message you want to communicate. For example, you might use a bar chart to compare categories, a line chart to show trends over time, or a scatter plot to explore relationships between variables.

The relationship between two variables that change together.

When two variables increase or decrease together.

When one variable increases while the other decreases.

Examples of positive correlation: More time spent studying, higher grades.

Examples of negative correlation: More hours spent watching TV, lower grades.

A type of data visualization that shows relationships between multiple pieces of data.

A way to analyze and compare data based on its value and risk, often using color coding to quickly identify different categories.

A type of graph that shows the relationship between two variables, where each data point is marked as a single 'dot' on the graph.

Scatterplots are typically used for large datasets that don't involve time as a factor. They help visualize the relationship between variables without suggesting cause and effect.

Bar charts show how numerical data is related to categories, where each category is represented by a separate bar.

Categories in a bar chart represent things that are not numbers (like educational level) and the numbers themselves have set values (like number of people).

Categorical data in a bar chart means data that is not a number, like the name of a subject. Discrete values mean there are separate, countable numbers, without fractions.

Maps are a powerful way to visualize data related to locations. They can show distribution, trends, and comparisons across different geographic areas.

Dot distribution maps use dots to represent data points on a map, visualizing the density and distribution of a specific variable. For example, you could use dots to represent the distribution of population across a region.

Cartograms distort the size of geographic areas to emphasize a specific variable, like population density. They make it visually clear how certain areas are relatively larger in comparison to others based on the selected variable.

Plotly is a data visualization tool built on Python. It allows you to create highly customizable visualizations with some coding knowledge.

D3.js is a free and open-source JavaScript library for creating interactive and dynamic data visualizations. It requires programming knowledge to use effectively.

Tableau is a popular data analytics tool known for its user-friendly interface. It allows users to create visually appealing visualizations without coding, and is efficient in handling large datasets.

Data visualizations should have a clear purpose, target the right audience, be simple and easy to understand, avoid distorting the data, and ensure inclusivity.

Before creating a visualization, consider the key message you want to communicate. What specific insights are you aiming to highlight with your data visualization?

A type of chart that uses bars to represent the magnitude of different categories or values. The height of each bar is proportional to the value it represents, making it easy to compare different values at a glance.

A type of chart that represents data as slices of a circle. Each slice represents a proportion or percentage of the whole, with the size of the slice corresponding to its value.

A type of chart that uses nodes (circles) to represent different entities or elements within a network, and lines to show the connections or relationships between those nodes.

A type of chart that uses maps to represent the distribution of data across a geographic area. This allows for visualizing data based on its location.

A type of chart that uses a series of horizontal or vertical bars to represent data, where length or height of the bar indicates the magnitude of the value.

A type of chart that shows trends and patterns in data over a period of time, usually by connecting points on a graph with a line.

A type of chart that shows the relationship between two different variables by plotting data points on a graph. The position of each point on the graph indicates its value for both variables.

A type of chart that uses bars to represent both positive and negative values, where bars extending above the zero line represent positive values and bars extending below the zero line represent negative values.