Abigale  Yundt

Abigale Yundt


What Is Data Literacy and Why Should You Care?

How do you differentiate high class and middle class? Obviously, by (annual) income and/or net worth. But money is just a result. People with money usually have unique set of skills that sets them apart.

In medieval times, reading and writing skill in itself would be sufficient to set you apart. Literacy was not that common and was mostly characteristic of upper-middle or higher class.

This is mostly because reading and writing has very broad use. It’s not niche. It is and was used in almost every aspect of personal and professional life. Reading also improves your knowledge dramatically.

So what is such a skill in 21st century?

I would like to make a case it’s the ability to understand data and work with it. It is THE differentiator of modern age, because data is everywhere.

In fact, 90% of data has been created in the last 2 years. Predictions show there will be about 50 billion connected devices by 2030. Currently there are around 3,5 billion smartphones, representing people with access to the Internet in their pocket, reading articles, browsing social networks, chatting with friends and much more. Data is everywhere in personal life as well as business.

Here are some examples from personal life:

  • I get my credit card or bank statements — do I know what to look for? Trends of spending, distribution of spending among housing, car, food,…
  • Time spent on a phone in different apps — how am I spending my time? What can be improved?
  • How many emails do I send each day? Which day of the week was the most productive in the last 6 months?
  • I use smartwatch to track my heart rate and sleep — when am I getting good night sleep? When is my heart rate generally good? Can I identify patterns?

Now obviously there’s much value hidden in data for companies:

  • in production lines, you can analyze performance, potential vibrations that indicate upcoming shutdown, or energy consumption,
  • you can correlate weather with energy consumption and thus predict revenue of utility and energy companies
  • in retail, company can (and does) segment popularity of products, creates a profile of each and every buyer that uses their club card, etc.

#data #intelligence #literacy #business #management #data science

What is GEEK

Buddha Community

What Is Data Literacy and Why Should You Care?
 iOS App Dev

iOS App Dev


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


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.


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

A gentle Introduction to Data Literacy

With the evolution of Artificial Intelligence and other technological innovations, many companies neglect what remains the main asset of any business: its human capital. Humans generate data; data is the new oil; it is the new currency.
If you have the impression that you hear it before… The phrase “data is the new oil” is not mine… but it was said — and has been repeated since it was coined by the British mathematician Clive Humby in 2006 — to denote this value and power in the data in our business lives.
Data is not just numbers; they are texts, videos, images, audios, and all kinds of information encoded. Data are even people. The combination of millions, billions, trillions of information make up what we call Big Data.

#literacy #big-data #data #data-science #data-literacy

Cyrus  Kreiger

Cyrus Kreiger


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


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