Shamsheer shah

1636543756

Data Visualization

What Is Data Visualization?

Data Visualization is graphical representation of data. It includes charts, graphs, maps, etc. which provide a manageable way to understand data patterns and trends. In technical terms, Data Visualization is the process in which large data sets are translated into information in the form of charts, graphs, and other visuals.  In this, you get a clear vision of data in the form of visuals. This makes data easy to comprehend for the human mind to understand.

Types of Data Visualization: Although there are two basic types of data visualization:

1. Static: Includes Infographics, a single opening view of a particular data story.

2. Interactive: Includes customized visuals in which your data can be represented by moving sliders or clicking a button.

Apart from basic types, there are other types of data visualization, mention below:

·        Column Chart

·        Bar Graph

·        Stacked Bar Graph

·        Line Graph

·        Dual-Axis Graph

·        Mekko Chart

·        Pie Chart

·        Scatter Plot

·        Waterfall Chart

·        Bubble Chart

·        Bullet Graph

·        Funnel Chart

·        Heat Map

Data Visualization Tools

Tools is a form of software which assist in visualizing data. Each tool has a different application but the basic purpose is to input data and convert it into information which is represented in visual form. Here is the list of few tools of data visualization:

1.     Microsoft Excel: Although excel is a spreadsheet tool. But it also includes data visualization properties, so it is also considered a data visualization tool with limited capabilities as compared to other tools.

2.     Tableau:  Itis one ofthe most popular data visualization tools as it is easy to use and is very powerful. It also offers various products along with customer relationship management. This tool helps in creating a huge variety of interactive charts that allows all stakeholders to understand data easily.

3.     Zoho Analytics: It is a tool in which data visualizations are created in a few minutes. It is business intelligence software that helps to create multidimensional data visualizations from data collected from multiple sources. It also allows you to share reports with others and allows you to publish it.

4.     Infogram: It is also a popular tool that is used to generate charts, reports and maps. It is popular among professionals, as it can be used to create infographics and additionally it includes a drag and drop editor also.

5.     Qlik Sense: It helps companies to become data driven by providing artificial intelligence systems, data analytics engines etc. You can easily combine and visualize large sizes of data. Data represented in this are interactive and also update itself according to the current data.

6.     SAP Analytics Cloud: It uses business intelligence and data analytics properties to evaluate data and create visuals of data to generate business outcomes. It provides you with complete customer satisfaction by clearing your doubts using artificial intelligence and NLP.

7.     Sisense:  This tool simplifies complex data and obtains insights for organizations. It provides data analytics properties to teams to make their companies the data-driven companies for the future.

8.     Looker: This tool studies data in–depth to discover useful insights. It also provides customer support wherein every doubt can be cleared by answering. Data visualization can be shared with anyone and can also export files in any format.

Other tools are IBM Congo Analytics, Domo, Klipfolio, Data wrapper, Google Charts etc.

 

Advantages of Data Visualization:

·        Easy Sharing of Data:  With data visuals, companies discover new mediums for communication. Instead of sharing bulk quantities of information, Visuals data is shared in which information is better absorbed.

·        Analysing Patterns: With it, it becomes easier to understand the patterns and trends of data, giving a better edge to companies to manipulate data.

·        Superior Method:

·        Better Understanding:  Data Visuals give better solutions to problems by representing data of both aspects giving better opportunity to understand.

·        Modification of Data: The biggestadvantage of data visuals is that data can be altered and modified giving business opportunities to establish better communication.

·        Discovering Relations between events: Business is affected by a lot of factors and finding relation between these factors helps in better decision making.

Disadvantages:

·        No Assistance: In this, a major disadvantage is that it doesn't provide any assistance due to which different audiences will interpret it differently.

·        One Sided: Information taken can be biased as information representation can occur with human interference, as a result can be one-sided.

·        Not Accuracy Estimation: Data can be accurate but visuals are based on estimates, as a result it can’t be accurate and can lead to speculative conclusions.

·        Wrong Focused People Can Lead to Skipping of Core: One of the drawbacks of data visualization is that the purpose of data is completed depending on its audience. If the audience is not focused, they can miss the core message of the analysis.

Why is Data Visualization Important?

Data Visualization is important for all sectors and companies as presenting data in more understandable form can leverage the information. It’s one of the most useful skills that any professional should attain to be ahead of others. As these skills help companies to make better decisions which in turn can increase your value. Data Visualization improves insights, enhances understanding, maintains the audience’s interest, and allows users to comprehend information easily, identify patterns, make predictions, and communicate information with others. So, data visualization is not only important for companies but also for professionals to enhance their value in the market.

If you want to learn this skill of presenting data in visual form and want to become a data-driven professional likedata scientist, data analyst, and etc. VisitLearnbay.co website to know details aboutdata science courses in Bangalore which will help you in attaining data visualizations and other skills.

 

 

 

 

 

 

 

 

What is GEEK

Buddha Community

 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

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

Analyzing Data From U.S. Road Accidents With Data Visualization

Every 24 seconds, a life is lost on the road, and it costs countries around 3% of their gross domestic product - World Health Organization.

With a fatality rate of 12.3% per 100,000 inhabitants, traffic accidents are a leading cause of death in the United States. In 2019, it was reported that 36,096 lives were lost on U.S. roads and according to the National Highway Traffic System Administration (NHTSA), it costs about $871 billion annually to the U.S. economy.

In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and also helps understand the factors that influence road fatalities in the United States.

“Having access to accurate and updated information about the current road situation enables drivers, pedestrians, and passengers to make informed road safety decisions.”

- Association For Safe International Road Travel.

#data-science #big-data-analytics #data-integration #solving-data-integration #data #data-analysis