Kasey  Turcotte

Kasey Turcotte

1623213900

Visualize Your Map Data With Basic Viz Packages

Create an informative map visualization using only the basic tools

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

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Visualize Your Map Data With Basic Viz Packages
Kasey  Turcotte

Kasey Turcotte

1623213900

Visualize Your Map Data With Basic Viz Packages

Create an informative map visualization using only the basic tools

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

 iOS App Dev

iOS App Dev

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

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

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

Enterprise Data Management: Stick to the Basics

Lots of people have increasing volumes of data and are trying to run data management programs to better sort it. Interestingly, people’s problems are pretty much the same throughout different sectors of any industry, and data management helps them configure solutions.

The fundamentals of enterprise data management (EDM), which one uses to tackle these kinds of initiatives, are the same whether one is in the health sector, a telco travel company, or a government agency, and more! Therefore, the fundamental practices that one needs to follow to manage data are similar from one industry to another.

For example, suppose you’re about to set off and design a program. In this case, it may be your integration platform project or your big warehouse project; however, the principles for designing that program of work is pretty much the same regardless of the actual details of the project.

#big data #bigdata #big data analytics #data management #data modeling #data governance #enterprise data #enterprise data management #edm