Circle Segments: High Dimensional Data on 2D

Visualizing data provides better understanding in exploratory data analysis. Frequencies, correlations, proportions of data can be interpreted easily. These statistics also play an important role in deciding machine learning methods. Especially understanding relations between variables. Therefore, scatter-plot is one of the most used techniques to understand the distributions or relations of one or more variables on certain locations. The challenge of scatter-plot is visualizing high dimensional data. Understandable dimension by humans can only be maximum 3 as x, y and z. It means that we can only visualize three variables in the same plot as points. Besides, interpreting 3D plots is harder than 2D plots. Therefore, we might try to add colors, shapes and sizes as other dimensions to 2D plots. However, another solution for this problem is scatter-plot matrix. Scatter-plot matrix is a method that creates 2D scatter-plots with each pair of variables and displays them on matrix structure. Thanks to that we can see all scatter-plots in the same visual.

There is also one more option for visualizing high dimensional data on 2D called “circle segments” which is suggested by Ankerst, M. et al. in 2001 [1]. In this article I am going to explain; what is Circle Segments Visualization and how to apply it on “matplotlib”. We will see following sections;

• What is Circle Segments visualization
• How Circle Segments Algorithms Works
• Application of Circle Segments with “matplotlib”

What is Circle Segments Visualization

As it’s known, color is one of the major components of visualization. It can be used for visualizing another dimension of data without adding any axis to plotting. Circle Segments visualization technique mostly depends on colors. It basically slices the circle to amounts of variables (dimensions). Every slice represents variable values as pixels from first observation to last observation. Algorithms assign colors to every pixel according to the observed value. For instance; we set the highest value of the variable as blue and lowest value as red. Let’s suppose, values of X variable increase from first observation to last observation and values of Y variable decrease from first observation to last observation. Therefore, colors of pixels in slice X will start from blue and will turn to red at the end of the slice and colors in slice Y will start from red and will turn to blue at the end. Plus, we can add more slices (variable) and compare them on 2D plotting.

An example Circle Segment visualization output that we are going to create in this article (visual by author)

#data-science #circle-segments #data-visualization #matplotlib #exploratory-data-analysis

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Circle Segments: High Dimensional Data on 2D

Visualizing data provides better understanding in exploratory data analysis. Frequencies, correlations, proportions of data can be interpreted easily. These statistics also play an important role in deciding machine learning methods. Especially understanding relations between variables. Therefore, scatter-plot is one of the most used techniques to understand the distributions or relations of one or more variables on certain locations. The challenge of scatter-plot is visualizing high dimensional data. Understandable dimension by humans can only be maximum 3 as x, y and z. It means that we can only visualize three variables in the same plot as points. Besides, interpreting 3D plots is harder than 2D plots. Therefore, we might try to add colors, shapes and sizes as other dimensions to 2D plots. However, another solution for this problem is scatter-plot matrix. Scatter-plot matrix is a method that creates 2D scatter-plots with each pair of variables and displays them on matrix structure. Thanks to that we can see all scatter-plots in the same visual.

There is also one more option for visualizing high dimensional data on 2D called “circle segments” which is suggested by Ankerst, M. et al. in 2001 [1]. In this article I am going to explain; what is Circle Segments Visualization and how to apply it on “matplotlib”. We will see following sections;

• What is Circle Segments visualization
• How Circle Segments Algorithms Works
• Application of Circle Segments with “matplotlib”

What is Circle Segments Visualization

As it’s known, color is one of the major components of visualization. It can be used for visualizing another dimension of data without adding any axis to plotting. Circle Segments visualization technique mostly depends on colors. It basically slices the circle to amounts of variables (dimensions). Every slice represents variable values as pixels from first observation to last observation. Algorithms assign colors to every pixel according to the observed value. For instance; we set the highest value of the variable as blue and lowest value as red. Let’s suppose, values of X variable increase from first observation to last observation and values of Y variable decrease from first observation to last observation. Therefore, colors of pixels in slice X will start from blue and will turn to red at the end of the slice and colors in slice Y will start from red and will turn to blue at the end. Plus, we can add more slices (variable) and compare them on 2D plotting.

An example Circle Segment visualization output that we are going to create in this article (visual by author)

#data-science #circle-segments #data-visualization #matplotlib #exploratory-data-analysis

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data