What is Data Science Visualization?: 3 Critical Aspects
Data Science is a field in Information Technology that focuses on extracting insight from data (Structured and Unstructured Data) and applying the knowledge and actionable insights in solving problems. One of the most important subsets of this field is Data Science Visualization.
Table of Contents
Data Science Visualization is a powerful way to graphically represent your data. It makes it easier for Data Analysts, and Data Scientists to analyze data and derive meaningful insights. There are a lot of tools available that can help you visualize your data such as Tableau, Looker, Sisense, etc.
This article discusses the role of Data Science Visualization in Data Science as an interdisciplinary field. It also provides an overall picture of the most commonly used plotting types and their significance. This article also talks about the top 3 Data Science Visualization Tools available in the market.
Table of Contents
- What is Data Science Visualization?
- Importance of Data Science Visualization
- Data Science Visualization: Data Plotting Types and their Significance
- Top 3 Data Science Visualization Tools
- Conclusion
What is Data Science Visualization?
Data Science Visualization is the representation of data graphically in any format. It is the most efficient way of communicating facts with non-technical professionals and helps them draw inferences from the data.
Many companies today are data-driven. The data they acquire is sitting in some Data Lake, usually in the cloud. The data collected is pulled out of the Data Lakes, cleaned, and stored in a Data Warehouse. Data Scientists work with these data to build and train Machine Learning Models, make Predictive Analyses, and visualize them.
Unlike Data Science, where Numerical Data is processed with scientific methods and algorithms, Data Science Visualization converts the dataset into visual content. Datasets can be represented graphically with Data Science Visualization Tools (e.g. Tableau, Looker, Microsoft Power BI, etc). These tools have a wide range of capabilities apart from just visualization. They allow you to create and share dashboards with others, perform Data Analysis, Create Charts, Modify and Change Visualizations, etc.
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Importance of Data Science Visualization
Listed below are the important aspects of Data Science Visualization in the field of Data Science:
1) Data Cleaning
Data Science Visualization can help detect Null values of data items in large datasets by representing them distinctively. This helps professionals reduce the burden of finding these errors before working with the data.
The data to be processed by a Data Scientist could be pulled from multiple data sources like Databases, Datasets, etc. This data could consist of redundancy and noise which needs to be eliminated before analysis. Visualizing these datasets gives you a complete overview without assumptions about the correctness of the data.
2) Data Exploration
The visual representation of data helps both technical and non-technical professionals/personnel have an overview of what the data is about. They can then tinker and also draw conclusions based on what they see.
Data Science Visualization gives anyone the power to perform Explorative Analysis on datasets provided. It makes it explanatory (all principles employed). The advent of user-friendly Data Science Visualization Tools like Tableau has also improved the process. These Data Science Visualization Tools provide on-the-go analysis on portable devices. Data Scientists can also leverage this to improve their decision-making process by identifying anomalies and relationships between data items.
3) Evaluation of Modeling Outputs
Data Scientists build Machine Learning Models for Predictive Analysis. These models are trained with large datasets to improve them. When training the models, the results are evaluated with Data Science Visualization Tools to know how well the model is doing and where it is lacking.
Apart from visualizing just outputs, the test data used to train the models and the model’s responsiveness can also be visualized to make more informed decisions.
4) Identifying Trends
Data Scientists and Data Analysts, at times, work with real-time data to derive meaningful trends. As real-time data is always fluctuating, it becomes difficult to analyze it. This is where the data can be visualized using charts and graphs for better understanding. This helps in making informed decisions not just in Data Science but in Business Intelligence in general.
5) Presenting Results
The result of analysis at any point of processing can always be visualized. The visualization can be done by anyone with knowledge of Data Science Visualization Tools, not just a Data Scientist. So far the data is from a supported data source, a Data Science Visualization Tool can represent it in its supported formats such as Graphs, Curves, or Charts.
Data Science Visualization: Type of Data Plots & their Significance
Following are the 6 most commonly used Data Plotting Types in the field of Data Science Visualization:
1) Bar Plot
A Bar Plot is very easy to understand and therefore is the most widely used plotting model. Simplicity and Clarity are the 2 major advantages of using a Bar Plot. It can be used when you are comparing variables in the same category or tracking the progression of 1 or 2 variables over time. For example, to compare the marks of a student in multiple subjects, a Bar Plot is the best choice.
2) Line Plot
A Line Plot is widely used for the comparison of stockpiles, or for analyzing views on a video or post over time. The major advantage of using Line Plot is that it is very intuitive and you can easily understand the result, even if you have no experience in this field. It is commonly used to track and compare several variables over time, analyze trends, and predict future values.
3) Scatter Plot
A Scatter Plot uses dots to illustrate values of Numerical Variables. It is used to analyze individual points, observe and visualize relationships between variables, or get a general overview of variables.
4) Area Plot
An Area Plot displays Quantitative Data graphically. It is very much like Line Plot but with the key difference of highlighting the distance between different variables. This makes it visually clearer and easy to understand. It is generally used to analyze progress in Time Series, analyze Market Trends and Variations, etc.
5) Histogram
A Histogram graphically represents the frequency of Numerical Data using bars. Unlike Bar Plot, it only represents Quantitative Data. The bars in the Histogram touch each other i.e. there is no space between the bars. It is generally used when you are dealing with large datasets and want to detect any unusual activities or gaps in the data.
6) Pie Chart
A Pie Chart represents the data in a circular graph. The slices in a Pie Chart represent the relative size of the data. Pie Chart is generally used to represent Categorical Data. For example, comparison in Areas of Growth within a business such as Profit, Market Expenses, etc.
Top 3 Data Science Visualization Tools
Although there are various Data Science Visualization Tools available in the market, the top 3 favored tools are listed below:
1) Tableau
Tableau is the most preferred Data Science Visualization Tool by Data Analysts, Data Scientists, and Statisticians. It gives you the power to explore and analyze data in seconds. You can connect your data to your Tableau account to analyze the trends. Tableau is highly compatible with Spreadsheets (Excel, Access, etc), Databases (Microsoft SQL Server, MySQL, MonetDB, etc), and Big Data (Cloudera Hadoop, DataStax Enterprise, etc). You can also access your Data Warehouses or Cloud Data using Tableau. It is very easy to use and helps you transform and shape your data for analysis.
For more information on Tableau, click here.
2) Looker
Looker is a Data Science Visualization Tool that provides a real-time dashboard of the data for more in-depth analysis. This gives you the advantage of taking instant decisions based on the Data visualization obtained. Looker provides you easy connection with your Cloud-Based Data Warehouses like Amazon Redshift, Google BigQuery, Snowflake, as well as 50 SQL supported languages.
To learn more about Looker as a Business Intelligence tool, follow this link.
3) Microsoft Power BI
Microsoft Power BI is a Data Science Visualization Tool that focuses on creating a data-driven Business Intelligence culture in an organization. It helps you quickly connect to your data, model it, and then visualize it for better analysis. Microsoft Power BI also gives you the option to securely share meaningful insights of your data among your team members with a graphical view. It supports 100s of data sources (on-premise or cloud) like Excel, Salesforce, Google Analytics, and Social Networks (Facebook, Twitter, Reddit, etc). It also supports IoT devices for real-time information.
For more information on Microsoft Power BI, click here.
Conclusion
Data Science Visualization remains an important tool in communicating information between teams and the general public. It is also a required skill by Data Scientists and Data Analysts to help them communicate facts with their team and also non-technical personnel. As there are tons of Data Science Visualization Tools available in the market, the factors affecting the choice should revolve basically around your workload and budget.
This article provided a comprehensive overview of Data Science Visualization and how it is important for Data Scientists, Data Analysts, etc. The article also talked about the most commonly used Data Plotting Types and their significance. It also provided a brief description of the top 3 commonly used Data Science Visualization Tools. To learn more about the Bi Chart types & Visual Lists, click here.
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