When running a project and you need to deliver regular reports to your manager, you will automatically need to find the simplest way to present your report. Data visualization is among the most popular and reliable means to deliver complex data reports. Visualization is all about delivering your data in the form of visuals.
The application of this methodology is supported by the research report that revealed that approximately 90% of the information channeled to the brain is visual. When you want your readers to grasp the point you are trying to deliver, make proper use of visuals throughout your data presentation. The brain has powerful features that process visuals faster compared to text content.
In addition, the human brain can process visuals 60,000 times faster compared to text. However, when we talk about data visualization, there are also data visualization projects that you can easily run. Despite the fact that visuals are pretty simple to read, they are easily trusted compared to any other type of content.
Did you know that when visuals are used in a more professional setting, they add more value and can convey information much faster? Now you know! With data visualization there are data visualization projects that you can easily run. Also, there are data visualization project ideas for beginners. Let’s learn more!
Data Visualization Project Ideas for Beginners
When you talk of data visualization projects for freshers, it involves the process of presenting data effectively. It deals with using different data libraries and their respective features to enhance the success of your data presentation effort. The main focus will revolve around dealing with different types of visualization to facilitate the delivery of data to your target audience.
Below are some of the data visualization project ideas for beginners. Let’s check them out!
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Horizontal Bar Chart Using Pandas
Pandas is one of the most common libraries in the field of data science. It’s known to have remarkable data visualization features that make it a unique option. The advanced data manipulation features of pandas combined with data visualization make it a better option for exploring and understanding data before it’s used.
This type of data visualization project involves the process of creating a horizontal bar chart with the aid of pandas. A bar chart is a simple tool since you only have to plot numeric values that are attributed to the categorical features of the bars you intend to display. You can use the bar chart to compare data groups against one another.
This scenario may sound more ubiquitous since it explains why bar charts are more valuable and used in most scenarios. There is a lot of hidden information that beginners can uncover using the bar chart, which is essential during data visualization.
- Histogram with Plotnine
A histogram is one of the most popular data visualization tools you can convert into a project. The grammar of graphics used in this category is to create and describe a wide variety of statistical graphics and compose plots using the process of mapping to specific projects that involve different types of data in text or numeric form like app academy school reviews.
Plotting your chart using grammar gives room for easy customization and the development of more complex plots. In this project, you can easily create any type of histogram regardless of how complex and the amount of data it can accommodate. The histogram created in this case is used to summarize discrete or continuous data depending on the objective of data visualization.
A histogram is a better option when you want to present data patterns and distribution more clearly and concisely. It can as well uncover any existing outliers in the data during the analysis process.
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Scatter Plot with Matplotlib
If you are looking for a data visualization library that offers a lot of flexibility, Matplotlib is the most appropriate option. This data visualization library has the capability to offer interactive and animated visualizations depending on your preferred requirements. It also has a greater level in matters of programming since it has the responsibility to achieve more complex types of visualization.
A scatter plot from ChartExpo is designed to generate visuals within the same level as the publication upgrade. The project is mainly intended to advance the capabilities of a typical scatter chart and give it the ability to conduct more complex types of data visualization. The chart has the ability to discover the relationship between two different attributes within a particular dataset.
Given that different types of charts have varying configuration parameters, doing regular exercises will give you a more precise idea about how the library operates. Also, it gives you access to a wide variety of features you need during data visualization to make your work easier and generate more advanced data output.
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Stacked Bar Plot
The stacked bar plot is a good project for beginners who want to create more complex plots using the plotnine and the seaborn libraries. A stacked bar chart is one of the most important data visualization tools that you will always require to analyze your data. When exploring your data variables to know more about the amount of data, a variable is almost similar to the second categorical data variable.
In a stacked bar chart, every bar is made up of several sub-bars that directly correspond to the value of the secondary categorical data variable. Also, you can evaluate how every data variable impacted the other at a personal level. The chart gives you a better idea of what your data involves. Also, it’s a reliable option when you want to learn how one data variable affects the other throughout the data set.
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Box Plot with Seaborn
When you mention Seaborn, you simply refer to the statistical graphics library used in Python. This library incorporates a bunch of features ranging from color palettes to multiple in-built themes and many others. This data visualization project can enable you to generate plots ready for publication. It focuses on different parts of the chart rather than the appearance of the box plot itself.
On most occasions, box plots are used to conduct exploratory data analysis and uncover the distribution of the features across the chart. A box plot is the most preferred option when you want to define the distribution of certain features within a certain dataset. Also, it unmasks outliers in your data, making it easier for you to analyze data with a lot of ease.
In addition, the box plot is the best option when you want to generate colorful visuals for your data. The chart is attractive from a distance, thus good for attracting the reader’s attention. Also, it’s pretty easy to interpret, even for non-technical audiences.
Bottom Line
As a beginner in the field of data visualization, there are lots of things that you can achieve. There are a series of simple projects that you can easily conduct as you continue advancing your skills. Even though this might be considered one of the toughest activities you can engage in at the beginning, things tend to loosen up as you get started with the project.
Note that data visualization is a huge field involving many aspects you need to learn as time goes on. Provided that you have some basic guidelines on how to get started with the project, you are better positioned to learn more about it along the way. Most of the data visualization projects for freshers are meant to create a few more advanced charting tools for data visualization.