On Data Exploration and Visualisation

So you’ve got a hot dataset you want to take a look at. Nice. How you visualise it is going to depend on what kind of data it is. Is it one, two, three, or more-dimensional? Is it discrete or continuous? Do you know?

Often I find myself thinking I know what the nature of a dataset is, then in the process or trying to visualise it I discover what is really is. To me data visualisation is more a process of exploring data than it is about showing it off, after all, how can you say a visualisation is good if you can’t say with certainty that it’s valid?

Now let’s get to business. In this post we’ll use some earthquake magnitude data as an example. I’ll save you the details and say that when doing data exploration and visualisation it’s not too important what the data represents so long as we can find and capture all the important aspects of the data in our visualisation.

I say _important _because there are often aspects of data that are not of interest to the work being undertaken. For example, with the earthquake magnitudes my interest is in the high values (5+) so if a trend exists in the low values (3-) I would not consider it important.

On this point, it’s critical that we don’t get caught up chasing a data feature just because it’s there; we always need to remember the purpose of our work. You may rebut: “but often we do not know if a data feature is important until we understand it”, and that is true; we should consider every lead we find as worth exploring, but once we can see that it is not relevant to our purpose then we can let it go. In this way data exploration is just that: exploration. We are akin to the adventurer searching for knowledge in a tomb of unknown size and complexity. What is the knowledge we seek? What risks can we take to find that knowledge (i.e. how much time do we have to go looking)?

Figure 1: scatter plot of magnitudes values of mww magnitude type and mB magnitude type. Data are shown by blue dots. The dotted line is that with slope 1 and intercept 0 and is plotted for reference. The solid line is that fit to the data by orthogonal regression whose parameters are presented in the plot title.

Figure 1 shows a first pass at data visualisation for the earthquake magnitude dataset. Here I’ve plotted the values of mww and mB, two different magnitude types, for the same earthquakes and fitted a line to the data using an orthogonal regression.

At a glance this data visualisation seems to present the data well: all the data is shown, the scatter is clear, what a 1:1 relationship between values would look like is shown (dotted line), and the line of best fit is overlain, giving us an quantitative measure of how the data relate. But how much data is on this plot? We can see how the data scatters, but we can’t tell if there are multiple data points stacked at certain value pairs. Similarly, the orthogonal regression gives us an idea of what the overall data relationship is (although the exclusion of an error estimate in the regression makes it unknown how well the regression actually fits the data), but we don’t know what the data relationship looks like in smaller parts of the dataset.

#science #data-visualization #data-science

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On Data Exploration and Visualisation
Siphiwe  Nair

Siphiwe Nair

1620466520

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

Gerhard  Brink

Gerhard Brink

1620629020

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).


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

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Cyrus  Kreiger

Cyrus Kreiger

1618039260

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

Macey  Kling

Macey Kling

1597579680

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