The dataset used to create the examples below contains all places of worship across the United States and its territories. The data was collected and curated in 2019 and downloaded from Data World: US Places of Worship. Containing over 49,000 records, the dataset includes locations of places of worship, as well as the religious types that each facility is used by. This includes Buddhist, Christian, Hindu, Islamic, Judaic, and Sikh places of worship.
Conventional bar graphs will be used to familiarize us with the information. The same data will then be presented using geospatial visualizations (data placed on maps). Using maps can provide more context around the information, allowing readers to make more connections and obtain a better understanding.
It behooves us to first gain an understanding of the distribution of the most popular kinds of places of worship in the United States. Figure 1 provides an interesting spread, with Christian places of worship overwhelmingly the most common. The second most prevalent is Judaic, with Buddhist and Muslim places of worship following. Hindu and other religious places of worship are the least common in the United States.
Figure 2 provides a view of the total number of places of worship in each state and territory. Additionally, we can see the split of which types of places of worship are included in each state. California and Texas, being two of the three largest states, have the most places of worship.
By viewing the same data with a proportional symbol map in Figure 3, the average locations of the establishments within each state are evident. The size of each symbol represents how many of each religion type are represented in each state by the number of worship buildings. While present in all states, there is a concentration of Christian places of worship in the eastern half of the country. The non-green circles show us where other religious places of worship have a larger presence at the state level.
#data-visualization #religion #geospatial #visualization
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
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
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.
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
When I work with real estate data, I often I do one task right when I open the data set, which is make a geo-visualization of the data. I do this without the use of any complicated packages or shapefiles. Not only is it a great way to visualize the physical space in which my housing set lives, but I can use this visualization to see other elements that might inform my target. All I need is Seaborn and a dataset with some lat/long information.
I start by loading my relevant packages and load my data set. In this example I’m using the King County housing dataset.
#maps #data-science #python #pandas #data-visualization #visualize your map data with basic viz packages
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