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HomeBlogData ScienceData Visualization in Data Science: Types, Examples and Tools
Data visualization in data science is pivotal for effectively communicating insights. A picture is often worth more than thousands of words, especially when it comes to deciphering complex data. That's precisely why data visualization in data science is crucial across all stages of a data science project, from understanding the data to validating models.
With the advancement of state-of-the-art technologies, crafting impactful data visualizations has become more streamlined and effective. Adhering to a standardized workflow ensures that the visualizations are comprehensible to a broad audience. In this discussion, we'll delve deep into various data visualization techniques, showcasing different graphs tailored for specific use cases within the realm of data visualization in data science.
Let’s dive in!
In simple terms, data visualization in data science refers to the process of generating graphical representations of information. These graphical depictions, often known as plots or charts, are pivotal in the realm of data science for effective analysis and interpretation. Understanding the various types of data visualization in data science is crucial to select the appropriate visual method for the dataset at hand. Different types serve different analytical needs, from understanding distributions with histograms to spotting trends with line charts. As one delves deeper into the data science field, the importance of mastering these visualization types becomes even more apparent.
There are many reasons for data visualization in data science. Data visualization benefits include communicating your results or findings, monitoring the model’s performance at the evaluation stage, hyperparameter tuning, identifying trends, patterns and correlation between dataset features, data cleaning such as outlier detection, and validating model assumptions.
Here are some popular data visualization examples.
To get the most out of data visualization, you should consider the following things. These are the fundamentals of data visualization.
Earlier, I mentioned the importance of data visualization in data science. Here are some more details.
Data visualization plays an important role in data clearing. Good examples are detecting outliers and removing multicollinearity. We can create scatterplots to detect outliers and generate heatmaps to check multicollinearity.
Before building any model, we need to do some exploratory data analysis to identify dataset characteristics. For example, we can create histograms for continuous variables to check for normality in the data. We can create scatterplots between two features to check whether they are correlated. Likewise, we can create a bar chart for the label column with two or more classes to identify class imbalance.
We can create a confusion matrix and learning curve to measure the performance of a model during training. Plots are also useful in validating model assumptions. For example, we can create a residuals plot and histogram for the distribution of residuals to validate the assumptions of a linear regression model.
Time and seasonal plots are useful in time series analysis to identify certain trends over time.
As a data scientist, you need to present your findings to the company or other related persons who do not have more knowledge in the subject domain. So, you need to explain everything in plain English. You can use informative plots that summarize your findings. Are you interested in data visualization? Get started with the best Data Science courses.
There are many data visualization types. The following are the commonly used data visualization charts.
A distribution plot is used to visualize data distribution—for example: A probability distribution plot or density curve.
Source: seaborn.pydata.org
This plot is used to plot the variation of the values of a numerical feature. You can get the values' minimum, maximum, median, lower and upper quartiles.
Similar to the box and whisker plot, the violin plot is used to plot the variation of a numerical feature. But it contains a kernel density curve in addition to the box plot. The kernel density curve estimates the underlying distribution of data.
Source: seaborn.pydata
A line plot is created by connecting a series of data points with straight lines. The number of periods is on the x-axis.
A bar plot is used to plot the frequency of occurring categorical data. Each category is represented by a bar. The bars can be created vertically or horizontally. Their heights or lengths are proportional to the values they represent.
Scatter plots are created to see whether there is a relationship (linear or non-linear and positive or negative) between two numerical variables. They are commonly used in regression analysis.
A histogram represents the distribution of numerical data. Looking at a histogram, we can decide whether the values are normally distributed (a bell-shaped curve), skewed to the right or skewed left. A histogram of residuals is useful to validate important assumptions in regression analysis.
A categorical variable pie chart includes each category's values as slices whose sizes are proportional to the quantity they represent. It is a circular graph made with slices equal to the number of categories.
The area plot is based on the line chart. We get the area plot when we cover the area between the line and the x-axis.
Source: python-graph-gallery.com
Similar to the scatter plot, a hexbin plot represents the relationship between two numerical variables. It is useful when there are a lot of data points in the two variables. When you have a lot of data points, they will overlap when represented in a scatter plot.
Source: python-graph-gallery.com
A heatmap visualizes the correlation coefficients of numerical features with a beautiful color map. Light colors show a high correlation, while dark colors show a low correlation. The heatmap is extremely useful for identifying multicollinearity that occurs when the input features are highly correlated with one or more of the other features in the dataset.
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The data visualization process or workflow includes the fowling key steps.
This may be a business problem or any other related problem that could be solved with a data-driven approach. You should note all the objectives and outcomes plus required resources such as datasets, open-source software libraries, etc.
The next step is collecting data. You can use existing datasets if they’re relevant to your research question. Alternatively, you can download open-source datasets from the internet or do web scraping to collect data.
Real-world data are messy. So, you need to clean them before using them for visualization. You can identify missing values and outliers and treat them accordingly. You can perform feature selection and remove unnecessary features from the data. You can create a new set of features based on the original features.
The chart type depends on many factors. For example, it depends on the feature type (numerical or categorical). It also depends on the type of visualization you need. Let’s say you have two numerical features. If you want to find their distributions, you can create two histograms for each feature. If you want to plot their variations, you can create box and whisker plots for each feature. You can create a scatterplot if you want to find a relationship (linear or non-linear, positive or negative) between the two features.
You can use open-source data visualization tools such as matplotlib, seaborn, plotty and ggplot. You can also use API-based software such as Matlab, Minitab, SPSS, etc.
You can extract relevant features. You can do feature standardization if the values of the features are not on the same scale. You can apply data preprocessing steps such as PCA to reduce the dimensionality of the data. That will allow you to visualize high-dimensional data in 2D and 3D plots!
This is the final step. Here. You define the title and names for the axes. You should also choose a proper chart background to ensure the content is easily readable.
There are multiple tools and software available for data visualization.
1. Python provides open-source libraries such as
2. R provides open-source libraries such as
3. Other data visualization libraries
Tableau and Microsoft Power BI are popular among data scientists.
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Some of the main data visualization techniques in data science are univariate analysis, bivariate analysis and multivariate analysis.
In univariate analysis, as the name suggest, we analyze only one variable at a time. In other words, we analyze each variable separately. Bar charts, pie charts, box plots and histograms are common examples of univariate data visualization. Bar charts and pie charts are created for categorical variables, while box plots and histograms are created for numerical variables.
In bivariate analysis, we analyze two variables at a time. Often, we see whether there is a relationship between the two variables. The scatter plot is a classic example of bivariate data visualization.
In multivariate analysis, we analyze more than two variables simultaneously. The heatmap is a classic example of multivariate data visualization. Other examples are cluster analysis and principal component analysis (PCA).
Advantages
There are many advantages of data visualization. Data visualization is used to:
Disadvantages
There are also some disadvantages of data visualization.
We need to develop a research question that could be solved with a data-driven approach.
This is very important as the visualizations depend on the type of audience you have. To present your findings to a business people audience, you need to create visualizations closely related to money, profits, and revenue the terms that business people are familiar with!
You need to create the right plot that addresses your requirement. To see the correlations between multiple variables, you can create histograms for each pair of variables. But that is not very effective. Instead, you can create a heatmap that is an effective way of visualizing correlations. When you have many categories, the pie chart is not suitable. Instead, you can create a bar chart. These are some examples of choosing an effective visual for your requirements.
Simple plots are easily readable. We can remove unnecessary backgrounds to make things stand out. We should not include much content in the plot. Title, names for axis, scale, and legends are just enough.
You should have the following data visualization skills for effective data visualization.
You should know R or Python language. R wins, hands down when it comes to data visualization. Its ggplot2 library provides high-level functions to make complex plots with less code. Data visualization in Python can be done using libraries like matplotlib, plotty, bokeh and seaborn for data visualization. Plotty and bokeh can be used for interactive data visualizations.
In addition to using R or Python languages, you can also use data visualization software such as Matlab, Minitab and SPSS for data visualization. Data visualization in Excel is also popular. However, they provide limited customizations for your plots. In addition to that, you cannot automate the plot creation process as you can do it with Python or R.
Data visualization is one of the data science skills. But, for effective data visualization, you need other data science skills such as statistical analysis, data cleaning, processing large data sets, data mining, etc. Data visualization cannot be done alone. It is a collection of these skills.
When it comes to presenting your findings to the company or other related people, you need to have excellent presentation skills. You should have more confidence when explaining things to a larger audience. For that, you should be familiar with the given problem domain.
Machine learning is the ability of computers to learn from data without being explicitly programmed. It is completely different from traditional programming. We can use machine learning algorithms to find important patterns and features in the data. Then, we can visualize those things. There are machine learning algorithms that can be used to perform data cleaning before data visualization. Machine learning is part of the data visualization process.
Data visualization is important in every aspect of data visualization in data science. We should clean our data before making any visualization. We should choose the right tool or software that addresses our needs, such as affordability, ease of use, etc. The main challenge in data visualization is choosing the right plot type. It depends on many factors. Finally, you need excellent public speaking and presentation skills to present your findings.
Today, we discussed data visualization applications and methods in detail with examples. Learning data visualization is not straightforward. You should master many skills for that. Go for KnowledgeHut’s best Data Science courses to upskill your skill.
Data visualization is used in every aspect of data science:
Commons use cases of data visualization include:
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