15 Data Visualization Projects for Beginners with Source Code

Top 15 Data Visualization Projects Ideas for Beginners and Students in Python with Source Code for 2021 to help you find the right type for your ML project.

15 Data Visualization Projects for Beginners with Source Code
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Consider that you are with the following data table and its associated graph:

Age 

Daily consumption

Dairy 

Staple Food

High-CalorieFood

Supplements

0- 10

50

30

10

10

11- 30

35

45

15

5

31- 50

25

55

13

7

51- 80

40

40

4

16

 

Data Visualization Project Ideas

Even if you’ve just skipped over the figures, you’d agree that the graph is at the very least a tad bit more memorable and appealing than data tables or text. However, if the analyst would have used a few data pre-processing methods and presented the same data through more appropriate charts, it would look more appealing and understandable at the same time. Additionally, one will not need to dive deeper into the graphs to extract the information from them. This is what makes data visualization so incredibly useful and powerful! 


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There have been plenty of studies and findings to prove this fact. According to the Massachusetts Institute of Technology, 90% of the information transmitted to the brain is visual. Further, they have found that our brains possess the incredible ability to process an image in just 13 milliseconds, which can perhaps explain why you would be able to understand the graph before the table. The studies conducted at the University of Minnesota have similar findings by revealing that the human brains process visuals 60,000 times faster than text.

Further, not only are claims made by visuals more likely to be read but they have also been found to be perceived as more believable by 97% of people, unlike the 68% obtained by using only text. 

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Consequently, using visuals in professional settings help add to business value as they convey information faster, aid good decision-making, and improve productivity and efficiency. Nucleus Research has found that business intelligence with data visualization capabilities will offer a return on investment of $13.01 for every dollar spent. With how much expenditure is made and time spent on data collection and processing, getting the most out of data is naturally a well-in-demand skill. Excellent presentation of data-driven insights is an indispensable step in any data science or machine learning project since the latter involves modelling to fit the data and requires revealing hidden patterns from data.

15 Data Visualization Project Ideas for 2021

This article will explore 15 data visualization projects ideas and how to use them in different types of machine learning and data science projects. These projects on data visualization are a perfect blend of beginner and advanced level ideas that will help you leverage data visualization as a key skill in a more practical manner.

data visualization projects

Without much ado, lets scroll down to get hold of divers data visualization projects ideas for students and beginners to explore in 2021.

Data Visualization Projects Ideas for Beginners

The first step towards an effective presentation of data involves understanding and using the various available libraries and their features. In the following project ideas, therefore, your objective will be to use some of the various available visualization libraries, namely matplotlib, pandas, seaborn, and ggplot.

Matplotlib is a data visulization library that provides a great deal of flexibility owing to its low-level nature. It is capable of providing static, animated, and interactive visualizations, however, a greater level of programming effort may be required to achieve the more complex plots or to achieve infographic designs that look publication-grade. 

This project, although simple, is intended entirely towards understanding the various features available and configurable using the matplotlib library for a simple scatter plot, which is generally used to observe the relations between two attributes in the dataset. While different plots have different configurable parameters, a hands-on exercise should give you a rough idea of how the library works. Additionally, it will help you in understanding the feature engineering and feature selection part of various machine learning projects.

For this task, you can replicate the scatter plot shown below for the popular Iris dataset available at https://archive.ics.uci.edu/ml/datasets/iris

data visualization projects ideas

HINTS: One easy way to achieve this is to use Pandas to read the tabular data and plot sepal length vs width using matplotlib. To replicate the plot above, you can divide the Iris dataset according to the flower species and then allot colors steel blue, tab: orange, and tab: green and markers circle, pentagon, and star to Iris-setosa, Iris-versicolor, and Iris-virginica, respectively. 

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Pandas is a commonly used library in the data-science domain. However, its visualization capability is not as well-known. The vast data manipulation capabilities of Pandas combined with the visualization feature make it especially desirable for exploring and understanding data before using it.

This data visualization project involves plotting a horizontal bar chart using Pandas. Bar charts, in general, include plotting numeric values attributed to a categorical feature as bars the former and latter plotted on different axes. From a bar chart, we can effectively compare groups against the others. As this use case is ubiquitous, it explains why bar charts are so valuable and popular. Therefore, there is much to learn from understanding bar plots and how to plot them. Another thing to keep in mind is that bar plots are quite useful if one wants to draw comparisons between different features of a machine learning project’s dataset.

NOTE: The plots generated here are, however, Matplotlib objects. However, it is important to remember that there might still be situations when it will be preferable or even necessary to use matplotlib directly. One such instance is when a customisation or a plot is yet to be supported by pandas. 

For this task, you can replicate the horizontal bar plot shown below for the Kaggle Digimon Database. Observe how only the first five rows of the DigiDB_digimonlist.csv have been used for this plot.

Horizontal Bar Chart using Pandas

HINTS: After importing the dataset using Pandas, another DataFrame using columns 'Lv50 Atk' and 'Lv50 Spd' from the original DataFrame with 'Digimon' column as the index, the plot.barh() function is used to generate the horizontal bar plot shown above.

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Seaborn is another statistical graphics library in Python built on top of matplotlib. The numerous built-in themes, color palettes, and functions enable one to generate publication-quality plots by focusing on the different elements within the plots rather than on how to plot them.

For this exercise, you can plot a boxplot using Seaborn, similar to the plot shown below. Boxplots are often used in exploratory data analysis and express the distribution of a feature in a dataset with a five-number summary. Hence, they can be helpful  to observe the skew of distribution and to identify potential outliers. This in turn allows one to analyse which machine learning model will work best for the given dataset.

You can use the same dataset as in the previous data visualization project idea for beginners, and compare the boxplots for Lv50 SP, Lv50 Atk, Lv50 Def, Lv50 Int, and Lv50 Spd columns. Observe how there are outliers for Lv50 Atk, Lv50 Def, and Lv50 Int.

Boxplot with Seaborn

HINTS: A pastel palette has been used for this plot.

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The plotnine library involves a Python implementation of the Grammar of Graphics and is based on the popular ‘ggplot2’ library in R. Grammar of Graphics is used to describe and create a wide range of statistical graphics and compose plots by mapping data to the objects that constitute a plot. Plotting with grammar allows for easy customization and creation of even, otherwise, complex plots.

In this data visulization project, you can plot a histogram for the ‘Lvl50 HP’ column of the previously used Digimon Database as shown below. Histograms serve to summarise discrete as well as continuous data. Like box plots, histograms are a great way to depict the data distribution. However, the pattern is more clearly observable in histograms. In addition, they can show any outliers or gaps in the data. Also, just like boxplots, histograms support the easy implementation of machine learning techniques on a dataset.

Histogram with Plotnine (ggplot)

HINTS: The aesthetic mapping for the histogram is set to fill='..count..'. The gradient is filled with low and high values as "cyan" and "blue" respectively.

This data visulization project idea for beginners emphasisez on the ease of creating slightly complex plots with seaborn and plotnine. Stacked Bar Charts can be helpful in some cases like the one demonstrated in the Introduction section. In scenarios where you are exploring data to know how much of a variable is based on the levels of a second categorical variable, stacked bar charts can be handy. Each bar in a stacked bar chart is composed of several sub-bars, corresponding to the value of a secondary categorical variable. Consequently, we can observe how much each one contributed to the total.

For this exercise, you can try to replicate the graph provided in the introduction section (plotted using matplotlib) with more high-level libraries. You can even consider doing the same in matplotlib to get a first-hand comparison of the plotting effort. Additionally, you may check out the use of the Plotly library for creating diverging stacked bar plots for a sentiment analysis natural language processing project.

Data Visualisation Projects Idea -Intermediate Level

Now that you have become familiar with some basic Python libraries for data visulization, in this section, we will explore data visulization projects that go beyond the basic plots towards more creative data visualization styles as well as more complex feature relations. All these projects will come in handy when you will try out different ML project ideas.

Heatmaps

For anything from analysis of shopping patterns, population maps to flight delays, heat maps are a definite go-to visualization. They possess the irreplaceable ability to identify problem areas or areas of interest by using colors that are easier to distinguish and comprehend than numeric values. This allows one to deal with large volumes of data faster, empowering you to make decisions quickly -and isn’t that what data scientists and businesses alike look for?!

To replicate the heatmap provided above, you can use the flights.csv of the  2015 Flight Delays and Cancellations dataset on Kaggle. You will be required to do a bit of preprocessing to plot the heatmap with Seaborn.

HINTS: cmap has been set to “Blues”, linewidth to 0.3, and the colour-bar’s keyword arguments to 0.8 shrink. 

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Word Clouds are quite popular data visualizations for audio data. However, unlike the previous infographics described, word clouds may be useless for applications like exploratory data analysis, performing sentiment analysis using natural language processing, etc., since they do not quite represent numerical data too well.

So, why use them then? For, while rife with limitations, they can be pretty appealing to look at, especially for presentations, qualifying themselves as a neat substitute to the mundane lines and circles. And appeal can sometimes be more important than just numbers! Was that not one of the primary purposes of data visualizations in the first place…?

While most word clouds are built based on the frequency of words, to switch things up a bit for this intermediate data visulization project, you could perhaps try to develop your word cloud of Digimon names based on Level 50 Attack or Level 50 HP in the Digimon Dataset. Feel free to try new things and explore without limiting yourself to just word clouds. The aim here is to be creative with your visualizations to make them more appealing while improving your proficiency in the data visualization domain.

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Radial Bar Plot

Image Source - Amcharts

As we have started talking about about creative visualizations, radial bar pots deserve a mention. When designing infographics, it is essential to remember that no matter how good the data you are presenting, it is worth nothing if it is not read! Hence, presenting spectacular designs and creative but informative visualizations is considered an asset in any industry.

Variants of the traditional plots like bar plots in the form of radial bar plots can be just as informative as their predecessors. However, a tad bit more appealing to the eyes. For this exercise, you can build a radial plot for any one of the datasets used earlier. While you might find that this data visualization project idea is harder than it sounds owing to the lack of direct functions like those for the traditional plots, it will be worth the effort, and you might even come across more fun variants of the primary plots in the process. Also, we highly recommend you work on a few machine learning and deep learning projects to understand the utility of radial bar plots as these are readily used by data science experts for feature engineering.

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4) Interactive Plot with Plotly (using Cufflinks)

Interactive plots are a great way to capture your audience’s attention, especially in a setting like a website homepage. By employing interactive features, you can make your infographics more appealing and less cluttered looking and more informative with features like pop-up information boxes. 

For this data visualization project, you can try to implement any of the graphs described in the previous projects using Plotly with Cufflinks and observe how different your experience with reading and understanding the graph is. Make sure to explore as widely as you can since you will be using Plotly frequently for the more advanced projects.

5) Basic Interactive Binned Scatter Plot with Altair

Altair is another statistical visualization library for Python. Vega and Vega-Lite is yet another powerful grammar-based library (like ggplot) and enables one to quickly build a wide range of statistical visualizations. 

You can implement a unique binned scatterplot for this task by plotting fixed acidity against pH for the Wine Quality Dataset  (https://www.kaggle.com/rajyellow46/wine-quality) on Kaggle (downloaded from the UCI Machine Learning Repository). You can also go a step ahead and try working on the Wine Quality Prediction machine learning project that will introduce you to the logistic regression machine learning model.

NOTE: Make sure you add the interactive feature to this graph. You will be sure to find that this is not hard to achieve with Altair once you have the graph in place! And although this interactive interface is perhaps not as sophisticated as the one you explored with Plotly, you would agree that it is quite neat.

Basic Interactive Binned Scatter Plot with Altair

HINTS: The size of the marks generated by mark_circle() is set by the count, and the pH values set the color.

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Advanced-Data Visaulization Projects Ideas

You must be familiar with correlation matrices which describe the correlation coefficients between multiple variables. But what about correlograms? Fret not if you haven’t heard of them, for a correlogram is essentially a graphical representation of a correlation matrix. They consist of a  combination of scatter plots (between each pair of numerical variables) and histograms at the diagonals to represent the distribution of each variable. 

Just one glance at the plot below, and you would agree about the invaluable insights these graphs could give you in the exploratory data analysis phase of various machine learning and deep learning projects, by providing both the correlation coefficients between each pair of variables as well as the scatter pattern between them at a glance.

Correlogram

http://www.sthda.com/english/wiki/correlation-matrix-a-quick-start-guide-to-analyze-format-and-visualize-a-correlation-matrix-using-r-software

The above graph is a modification of the traditional correlogram with the correlation matrix above the diagonal and the correlogram below. The graph above has been implemented on the mtcars datase. Howeverr, you could also try to implement it for the numerical data in the Wine Quality Dataset  (https://www.kaggle.com/rajyellow46/wine-quality). 

HINT: You might find it simpler to accomplish the above task with R language owing to the availability of suitable features (since statistical computing and graphics are among R’s most popular use cases).  

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Time Series visualizations are generally used to plot the changes in a parameter or parameters over time. Incorporating the interactive feature for time series plots can be especially useful when a long time period is to be plotted, or the changing trends need to be observed in detail by zooming, etc.

To get yourself familiar with interactive time series visualizations, you can plot a time series graph with Plotly for the temperature variations in some Brazilian cities using the data provided in the Temperature Time-Series for some Brazilian cities Kaggle dataset. Make sure to include features like range sliders into your plot to make it user-friendly and get the most out of the interactive capabilities. With this advanced data visualization project, you will gain hands-on experience on how different types of interactive features can help enhance an infographic.  

If you want to learn more about the significance of time series visualizations, check out these 15 Time Series Project Ideas for Beginners.

Interactive Sunburst Charts

https://plotly.com/python/sunburst-charts/

Sunburst charts are used to represent hierarchical data. They are also known as Ring Charts or Radial Treemap as the innermost circle represents the top of the hierarchy moving to the lower sections of the hierarchy as we move outwards. They can be very explanatory and easy to understand as every node with leaves can be represented as a standalone sunburst chart by incorporating the interactive feature. When working on this project, you will learn how such hierarchical mappings can give important insights regarding categorical variables besides being appealing to look at.

This dataviz project aims to implement an interactive sunburst chart using  implement an interactive sunburst chart using Amazon Top 50 Bestselling Books 2009 - 2019 with the hierarchical order: genre, author, and finally the name of the book.

Race bar charts are fun and highly appealing animated bar charts that display the way values change or grow over time. They are a great way to demonstrate trends in data and the changes that occurred over a period of time. Therefore, for this project, you can construct your own race bar chart using matplotlib or any of the other librarie, for the number of matches won by the 73 different teams in the Turkish Super League dataset (https://www.kaggle.com/faruky/turkish-super-league-matches-19592020). To implement this task with matplotlib, you will need to build the basic plot and then should be able to animate it using matplotlib.animation.

Choropleth maps are handy to represent statistical data concerning geographical regions. This can be used for representing housing prices, weather, or even social phenomena. 

For the final project, you can implement  a choropleth map for one of the most ravaging issues in recent times i.e,. the spread of Covid 19 around the world (https://www.kaggle.com/mohamedhanyyy/covid19-worldspreading). As you plot this graph, you will observe there are few other ways you can convey data related to geographical locations more effectively and impactfully than that by using a choropleth map!  

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Let’s Make it Picture Perfect!

Arriving at the perfect visualization you need for your data exploration phase, your business meeting, or any other fancy or not-so-fancy occasion does not just happen miraculously. Like any other domain, it needs to be thoroughly explored and experimented with to arrive at a stage where you can conjure up the most appropriate visualization in a breath. And what better way to improvise your learning process than to take up some hands-on projects that leverage these data visualization project ideas? We thus suggest you visit the ProjectPro repository that contains enterprise-grade projects in Data Science and Big Data. These projects will introduce you to various deep learning models, deep learning techniques and concepts, different types of deep neural networks (recurrent neural networks, artificial neural networks, convolutional neural networks, etc.) along with the best machine learning projects on Twitter data, stock market, loan prediction, customer segmentation, etc.

So pick up a project or two, get your hands dirty, and before you know it, you’ll manage to learn all the practicalities and get those graphs looking picture perfect!!!

 

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