For enquiries call:

Phone

+1-469-442-0620

Aage ki Socho

HomeBlogData ScienceData Visualization in Data Science: Types, Examples and Tools

Data Visualization in Data Science: Types, Examples and Tools

Published
22nd Sep, 2023
Views
view count loader
Read it in
14 Mins
In this article
    Data 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!

    What is Data Visualization?

    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.

    Why is Data Visualization Important in Data Science?

    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.

    Examples of Data Visualization in Data Science

    Here are some popular data visualization examples. 

    1. Weather reports: Maps and other plot types are commonly used in weather reports. 
    2. Internet websites: Social media analytics websites such as Social Blade and Google Analytics use data visualization techniques to analyze and compare the performance of websites. 
    3. Astronomy: NASA uses advanced data visualization techniques in its reports and presentations. 
    4. Geography 
    5. Gaming industry

    What Makes Data Visualization Effective?

    To get the most out of data visualization, you should consider the following things. These are the fundamentals of data visualization. 

    • Clarity: Data should be visualized in a way that everyone can understand. 
    • Problem domain: When presenting data, the visualizations should be related to the business problem. 
    • Interactivity: Interactive plots are useful to compare and highlight certain things within the plot. 
    • Comparability: We can compare the thighs easily with good plots. 
    • Aesthetics: Quality plots are visually aesthetic. 
    • Informative: A good plot summarizes all relevant information. 

    Importance of Data Visualization in Data Science

    Earlier, I mentioned the importance of data visualization in data science. Here are some more details. 

    1. Data cleaning

    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. 

    2. Data Exploration

    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. 

    3. Evaluation of modeling outputs

    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. 

    4. Identifying trends

    Time and seasonal plots are useful in time series analysis to identify certain trends over time. 

    5. Presenting results

    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

    Different Types of Data Visualization in Data Science

    There are many data visualization types. The following are the commonly used data visualization charts. 

    1. Distribution plot

    A distribution plot is used to visualize data distribution—for example: A probability distribution plot or density curve.

    Distribution plot

    Source: seaborn.pydata.org

    2. Box and whisker plot

    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.

    Box and whisker plot

    3. Violin plot

    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.

    Violin plot

    Source: seaborn.pydata

    4. Line plot

    A line plot is created by connecting a series of data points with straight lines. The number of periods is on the x-axis.

    Line plot

    5. Bar plot

    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.

    Bar plot

    6. Scatter plot

    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.

    Scatter plot

    7. Histogram

    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.

    Histogram

    8. Pie chart

    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.

    Pie plot

    9. Area plot

    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.

    Area plot

    Source: python-graph-gallery.com

    10. Hexbin plot

    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.

     Hexbin plot

    Source: python-graph-gallery.com

    11. Heatmap

    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.

    Heatmap

    Do you want to be familiar with these plot types and many other things in data science? Enroll in Data Science Online Bootcamp.

    Data Visualization Process/Workflow

    The data visualization process or workflow includes the fowling key steps. 

    1. Develop your research question

    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. 

    2. Get or create your data

    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. 

    3. Clean your 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. 

    4. Choose a chart type

    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.  

    5. Choose your tool

    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. 

    6. Prepare data

    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! 

    7. Create a chart

    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.

    Tools and Software for Data Visualization

    There are multiple tools and software available for data visualization.  

    1. Python provides open-source libraries such as  

    • Matplotlib 
    • Seaborn 
    • Plotty 
    • Bokeh 
    • Altair

    2. R provides open-source libraries such as 

    • Ggplot2 
    • Lattice

    3. Other data visualization libraries  

    • IBM SPSS 
    • Minitab 
    • Matlab for data visualization 
    • Tableau 
    • Microsoft Power BI are popular among data scientists. 

    Tableau and Microsoft Power BI are popular among data scientists.

    Unlock your potential with our effective ccba training course. Gain the skills you need to excel in business analysis. Enroll now!

    Data Visualization Techniques in Data Science

    Some of the main data visualization techniques in data science are univariate analysis, bivariate analysis and multivariate analysis. 

    1. Univariate 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. 

    2. Bivariate Analysis

    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. 

    3. Multivariate Analysis

    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 and Disadvantages of Data Visualization

    Advantages 

    There are many advantages of data visualization. Data visualization is used to: 

    • Communicate your results or findings with your audience 
    • Tune hyperparameters 
    • Identify trends, patterns and correlations between variables 
    • Monitor the model’s performance 
    • Clean data 
    • Validate the model’s assumptions 

    Disadvantages 

    There are also some disadvantages of data visualization. 

    • We need to download, install and configure software and open-source libraries. The process will be difficult and time-consuming for beginners. 
    • Some data visualization tools are not available for free. We need to pay for those.  
    • When we summarize the data, we’ll lose the exact information. 

    Data Visualization Best Practices

    1. Set the context

    We need to develop a research question that could be solved with a data-driven approach.  

    2. Know your audience

    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! 

    3. Choose an effective visual

    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. 

    4. Keep it simple

    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.

    Essential Skills for Data Visualization

    You should have the following data visualization skills for effective data visualization. 

    1. Programming

    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.  

    2. Software Expertise

    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. 

    3. Data Science Skills

    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. 

    4. Public Speaking and Presentation 

    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. 

    5. Machine Learning

    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.

    Conclusion

    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.

    Frequently Asked Questions (FAQs)

    1What are the three main goals of data visualization?
    • Communicating your results or findings with your audience 
    • Exploring (knowing) your data 
    • Identify trends, patterns and correlations between variables 
    2How is data visualization used in data science?

    Data visualization is used in every aspect of data science:   

    • Tuning hyperparameters 
    • Monitoring the model’s performance 
    • Cleaning data 
    • Validating the model’s assumptions 
    3What are the major challenges of data visualization
    • Choosing the right plot type 
    • Identifying the needs of your audience 
    • Developing the research question convert it to a data science question 
    • Collecting data 
    4What are the benefits of data visualization?

    Commons use cases of data visualization include: 

    • Communicate your results or findings with your audience 
    • Tune hyperparameters 
    • Identify trends, patterns and correlations between variables 
    • Monitor the model’s performance 
    • Clean data
    • Validate the model’s assumptions
    Profile

    Rukshan Manorathna

    Author

    B.Sc. in Industrial Statistics. Supporting your data science education since 2020. Top 50 Data Science/AI/ML Writer on Medium. Write articles on Data Science, Machine Learning, Deep Learning, Neural Networks, Python, and Data Analytics. Proven track record of converting complex topics into something valuable and easy to understand.

    Share This Article
    Ready to Master the Skills that Drive Your Career?

    Avail your free 1:1 mentorship session.

    Select
    Your Message (Optional)

    Upcoming Data Science Batches & Dates

    NameDateFeeKnow more
    Course advisor icon
    Course Advisor
    Whatsapp/Chat icon