Big data refers to the massive amounts of structured and unstructured data being created every day from sources like social media interactions, website clicks, and sensor data from devices. The volume, velocity, and variety of big data, known as the three V's, make it challenging to store, manage, and analyze. Additional challenges include the veracity and variability of big data. Big data is being used across many domains to gain insights, optimize business processes, improve sports performance and training, and support national security and law enforcement efforts through data analysis and mining. While big data holds great potential, many businesses have yet to fully leverage its capabilities.
2018년9월27일 뉴욕에서 열린 <데이터 사이언스 살롱> 세미나 후기입니다. Viacom, BuzzFeed, Forbes, The NewYork Times등 뉴욕 기반의 미디어 업계관계자들이 데이터사이언스 프로젝트를 공유하는 자리였습니다. One More Thing으로 구글 마운틴뷰에서 본 Crayon Graph가 포함되어 있어요!
data science history / data science @ NYTchris wiggins
talk delivered 2015-07-29 at ICERM workshop on "mathematics in data science"
workshop: https://icerm.brown.edu/topical_workshops/tw15-6-mds/
references: http://bit.ly/icerm
The document discusses the history and evolution of data science. It traces the field back to pioneers like John Tukey in the 1960s who introduced exploratory data analysis. It describes how fields like biology and genetics have been transformed by new machine learning tools and data science approaches. The document also outlines different types of data science work like supervised learning, unsupervised learning, and reinforcement learning. It emphasizes that successful data science requires a focus on people, ideas, and deliverables.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Big data refers to the massive amounts of structured and unstructured data being created every day from sources like social media interactions, website clicks, and sensor data from devices. The volume, velocity, and variety of big data, known as the three V's, make it challenging to store, manage, and analyze. Additional challenges include the veracity and variability of big data. Big data is being used across many domains to gain insights, optimize business processes, improve sports performance and training, and support national security and law enforcement efforts through data analysis and mining. While big data holds great potential, many businesses have yet to fully leverage its capabilities.
2018년9월27일 뉴욕에서 열린 <데이터 사이언스 살롱> 세미나 후기입니다. Viacom, BuzzFeed, Forbes, The NewYork Times등 뉴욕 기반의 미디어 업계관계자들이 데이터사이언스 프로젝트를 공유하는 자리였습니다. One More Thing으로 구글 마운틴뷰에서 본 Crayon Graph가 포함되어 있어요!
data science history / data science @ NYTchris wiggins
talk delivered 2015-07-29 at ICERM workshop on "mathematics in data science"
workshop: https://icerm.brown.edu/topical_workshops/tw15-6-mds/
references: http://bit.ly/icerm
The document discusses the history and evolution of data science. It traces the field back to pioneers like John Tukey in the 1960s who introduced exploratory data analysis. It describes how fields like biology and genetics have been transformed by new machine learning tools and data science approaches. The document also outlines different types of data science work like supervised learning, unsupervised learning, and reinforcement learning. It emphasizes that successful data science requires a focus on people, ideas, and deliverables.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
This document discusses how technology is changing the nature of jobs and the future of work. Key points:
1) Digital technologies are automating routine tasks and jobs that involve structured processes, while human workers will likely shift toward tasks requiring creativity, social skills and innovative thinking.
2) New online platforms are creating "networked work" where freelancers connect directly with clients, taking on more risks but also gaining more control over their work. However, this transition to more flexible work arrangements is not painless.
3) Demand is growing for STEM jobs across many industries as data analysis and computing become more widespread. While some jobs will be automated, technology also has the potential to create new types of jobs
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
Roger hoerl say award presentation 2013Roger Hoerl
This document discusses how statistical engineering principles can help address challenges with "Big Data" projects. It argues that simply having powerful algorithms and large datasets does not guarantee good models or results. The leadership challenge for statisticians is to ensure Big Data projects are built on sound modeling foundations rather than hype. Statistical engineering principles like understanding data quality, using sequential approaches, and integrating subject matter knowledge can help improve the success of Big Data analyses and provide the statistical profession an opportunity for leadership in this area. Statistical engineering provides a framework to structure Big Data projects and incorporate fundamentals of good science that are sometimes overlooked.
Big Data and the Social Sciences
Ex-Google engineer Abe Usher presents a talk about Big Data technology and methods applicable to social science.
Participants will learn techniques that are used by Google engineers to collect, clean, analyze, and visualize Big Data.
Additionally Mr. Usher will provide URLs to sample data, open source applications, and code to those interested in applying these Big Data methods themselves.
The document introduces the Data Analysis Framework (DAF), an online tool created by Legal Services Corporation grants to help legal aid organizations use data strategically. It provides examples of data questions legal aids may want to analyze, types of analyses like snapshots, comparisons, trends and geographic analyses. It also lists internal case and client data fields that could be analyzed, examples of external data resources, potential academic partners, and a matrix matching data questions with specific analysis approaches. The DAF is meant to help legal aids better understand their clients and cases by analyzing their own and external data.
Online text data for machine learning, data science, and research - Who can p...Fredrik Olsson
This slide deck concerns online text data for machine learning, artificial intelligence, data science, and scientific research. After this talk, you’ll know who can provide online text data, what types of data are hard to get, and principal data hygiene factors.
Updated in August 2019.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Big Data v. Small data - Rules to thumb for 2015Visart
Open data, big data, small data - what's the difference? Do you work with data? Small and medium sized businesses are pressured to transform traditional practices into data-driven models. In this presentation, CEO, Ugur Kadakal explains the big data v. small data and the insights we can pull from each for better business intelligence.
Do you work with data, or just like learning about it? Check out our blog on www.Visart.io for data stories and other resources.
DataEngConf: Data Science at the New York Times by Chris WigginsHakka Labs
This document discusses data science at The New York Times. It references various topics related to data science including predictive analytics, descriptive analytics, prescriptive analytics, data engineering, data science skills, and the importance of people, ideas, and tools/delivery in data science teams. It also references the data science work of Chris Wiggins and how data science has evolved the field of journalism and publishing.
Economic development in New Mexico can be achieved if we integrate the scientific and cultural tools, traditions and resources of the Rio Grande valley.
Big Data Analytics and Open Data : The presentation aim is to enhance the awareness about big data analytics by process and importance of open data. Two case studies overview with accuracy and introduction is presented by Sharjeel Imtiaz.
PhD from University of East London
Quontra solutions is your premier online IT educational destination in UK. It provides online IT courses like Selenium , Hadoop ,CCNA ,Cloud Computing ,Business Analyst and Many other IT courses. All the courses are designed by experienced instructors and designers. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment there is an urgent need for IT professional to keep themselves in trend with Hadoop and Big Data technologies
.
Quontra Specialties :
***All the courses are designed by Experienced Instructors and Designers.
***. Trainers are not limited to the syllabus, they explain off –the-shelf content also.
*** 24X7 technical support team .
***Unlimited access to all recorded sessions ,available after every live class.
***Syllabus built based on professional standards and employer insights.
***Trainers are Certified Experts in their corresponding field and they bring years of industry experience in to the training classes
This document summarizes a report analyzing the global influence of open data and how it has developed to become influential on business operations. It discusses how improving access to open data in the North East region of England could benefit small and medium businesses. The report finds that while open data is widely available and used in developing countries, the North East lags behind other UK regions in promoting open data use among companies. It suggests initiatives like hackathons and incentives to encourage using open data to help businesses and local governments.
1. The U.S. Census Bureau faces challenges from the rise of big data sources produced outside of traditional government surveys. These new sources are generated faster and more cheaply than surveys.
2. To remain reliable sources of demographic and economic information, the Census Bureau must integrate these new big data sources with traditional surveys. This requires linking massive datasets and developing new statistical modeling techniques.
3. The Census Bureau is exploring ways to use new big data sources like web search data, social media, and e-commerce transactions to improve surveys and provide more timely, detailed information. However, maintaining privacy and developing new technology is difficult.
El documento resume las características básicas de un procesador de texto, incluyendo la capacidad de trabajar con formatos de párrafo, fuentes, efectos de formato, cortar y pegar texto, ajustar espacios, alinear párrafos, establecer sangrías y tabulados, crear y modificar estilos. También incluye correctores ortográficos y gramaticales. Explica que los procesadores de texto evolucionaron de las necesidades de escritores más que de matemáticos, automatizando gradualmente los aspectos
El documento habla sobre el graffiti estadounidense. Explica que el graffiti generalmente consiste en inscripciones o pinturas sobre mobiliario urbano. Señala que el graffiti moderno surgió en Nueva York como una forma de expresión creativa, rebelde y revolucionaria. Además, menciona que la escena del graffiti de Nueva York tuvo una gran influencia en el desarrollo mundial del graffiti y que hoy en día es un movimiento dinámico y global con características compartidas pero matices regionales.
El grafiti en México se originó en Tijuana, frontera con Estados Unidos, donde los cholos adoptaron esta expresión con influencia de los muralistas chicanos. El grafiti mexicano utiliza estilos como tags, bombas, masterpieces y murales, aunque generalmente los espacios y obras son más respetadas que en otros lugares, con cada crew pintando en su propio territorio. El grafiti en México tiene una larga historia y las crews modernas han reemplazado a las antiguas pandillas con una forma muy particular de organización.
Este documento destaca la importancia de la comunicación intercultural en una sociedad multicultural y cómo fomenta la igualdad, la convivencia y la conciencia del entorno mediante el diálogo y la interacción entre culturas diferentes.
This document discusses how technology is changing the nature of jobs and the future of work. Key points:
1) Digital technologies are automating routine tasks and jobs that involve structured processes, while human workers will likely shift toward tasks requiring creativity, social skills and innovative thinking.
2) New online platforms are creating "networked work" where freelancers connect directly with clients, taking on more risks but also gaining more control over their work. However, this transition to more flexible work arrangements is not painless.
3) Demand is growing for STEM jobs across many industries as data analysis and computing become more widespread. While some jobs will be automated, technology also has the potential to create new types of jobs
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
Roger hoerl say award presentation 2013Roger Hoerl
This document discusses how statistical engineering principles can help address challenges with "Big Data" projects. It argues that simply having powerful algorithms and large datasets does not guarantee good models or results. The leadership challenge for statisticians is to ensure Big Data projects are built on sound modeling foundations rather than hype. Statistical engineering principles like understanding data quality, using sequential approaches, and integrating subject matter knowledge can help improve the success of Big Data analyses and provide the statistical profession an opportunity for leadership in this area. Statistical engineering provides a framework to structure Big Data projects and incorporate fundamentals of good science that are sometimes overlooked.
Big Data and the Social Sciences
Ex-Google engineer Abe Usher presents a talk about Big Data technology and methods applicable to social science.
Participants will learn techniques that are used by Google engineers to collect, clean, analyze, and visualize Big Data.
Additionally Mr. Usher will provide URLs to sample data, open source applications, and code to those interested in applying these Big Data methods themselves.
The document introduces the Data Analysis Framework (DAF), an online tool created by Legal Services Corporation grants to help legal aid organizations use data strategically. It provides examples of data questions legal aids may want to analyze, types of analyses like snapshots, comparisons, trends and geographic analyses. It also lists internal case and client data fields that could be analyzed, examples of external data resources, potential academic partners, and a matrix matching data questions with specific analysis approaches. The DAF is meant to help legal aids better understand their clients and cases by analyzing their own and external data.
Online text data for machine learning, data science, and research - Who can p...Fredrik Olsson
This slide deck concerns online text data for machine learning, artificial intelligence, data science, and scientific research. After this talk, you’ll know who can provide online text data, what types of data are hard to get, and principal data hygiene factors.
Updated in August 2019.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Big Data v. Small data - Rules to thumb for 2015Visart
Open data, big data, small data - what's the difference? Do you work with data? Small and medium sized businesses are pressured to transform traditional practices into data-driven models. In this presentation, CEO, Ugur Kadakal explains the big data v. small data and the insights we can pull from each for better business intelligence.
Do you work with data, or just like learning about it? Check out our blog on www.Visart.io for data stories and other resources.
DataEngConf: Data Science at the New York Times by Chris WigginsHakka Labs
This document discusses data science at The New York Times. It references various topics related to data science including predictive analytics, descriptive analytics, prescriptive analytics, data engineering, data science skills, and the importance of people, ideas, and tools/delivery in data science teams. It also references the data science work of Chris Wiggins and how data science has evolved the field of journalism and publishing.
Economic development in New Mexico can be achieved if we integrate the scientific and cultural tools, traditions and resources of the Rio Grande valley.
Big Data Analytics and Open Data : The presentation aim is to enhance the awareness about big data analytics by process and importance of open data. Two case studies overview with accuracy and introduction is presented by Sharjeel Imtiaz.
PhD from University of East London
Quontra solutions is your premier online IT educational destination in UK. It provides online IT courses like Selenium , Hadoop ,CCNA ,Cloud Computing ,Business Analyst and Many other IT courses. All the courses are designed by experienced instructors and designers. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment there is an urgent need for IT professional to keep themselves in trend with Hadoop and Big Data technologies
.
Quontra Specialties :
***All the courses are designed by Experienced Instructors and Designers.
***. Trainers are not limited to the syllabus, they explain off –the-shelf content also.
*** 24X7 technical support team .
***Unlimited access to all recorded sessions ,available after every live class.
***Syllabus built based on professional standards and employer insights.
***Trainers are Certified Experts in their corresponding field and they bring years of industry experience in to the training classes
This document summarizes a report analyzing the global influence of open data and how it has developed to become influential on business operations. It discusses how improving access to open data in the North East region of England could benefit small and medium businesses. The report finds that while open data is widely available and used in developing countries, the North East lags behind other UK regions in promoting open data use among companies. It suggests initiatives like hackathons and incentives to encourage using open data to help businesses and local governments.
1. The U.S. Census Bureau faces challenges from the rise of big data sources produced outside of traditional government surveys. These new sources are generated faster and more cheaply than surveys.
2. To remain reliable sources of demographic and economic information, the Census Bureau must integrate these new big data sources with traditional surveys. This requires linking massive datasets and developing new statistical modeling techniques.
3. The Census Bureau is exploring ways to use new big data sources like web search data, social media, and e-commerce transactions to improve surveys and provide more timely, detailed information. However, maintaining privacy and developing new technology is difficult.
El documento resume las características básicas de un procesador de texto, incluyendo la capacidad de trabajar con formatos de párrafo, fuentes, efectos de formato, cortar y pegar texto, ajustar espacios, alinear párrafos, establecer sangrías y tabulados, crear y modificar estilos. También incluye correctores ortográficos y gramaticales. Explica que los procesadores de texto evolucionaron de las necesidades de escritores más que de matemáticos, automatizando gradualmente los aspectos
El documento habla sobre el graffiti estadounidense. Explica que el graffiti generalmente consiste en inscripciones o pinturas sobre mobiliario urbano. Señala que el graffiti moderno surgió en Nueva York como una forma de expresión creativa, rebelde y revolucionaria. Además, menciona que la escena del graffiti de Nueva York tuvo una gran influencia en el desarrollo mundial del graffiti y que hoy en día es un movimiento dinámico y global con características compartidas pero matices regionales.
El grafiti en México se originó en Tijuana, frontera con Estados Unidos, donde los cholos adoptaron esta expresión con influencia de los muralistas chicanos. El grafiti mexicano utiliza estilos como tags, bombas, masterpieces y murales, aunque generalmente los espacios y obras son más respetadas que en otros lugares, con cada crew pintando en su propio territorio. El grafiti en México tiene una larga historia y las crews modernas han reemplazado a las antiguas pandillas con una forma muy particular de organización.
Este documento destaca la importancia de la comunicación intercultural en una sociedad multicultural y cómo fomenta la igualdad, la convivencia y la conciencia del entorno mediante el diálogo y la interacción entre culturas diferentes.
This white paper: Analyzes the big data revolution and the potential it offers organizations. Explores the critical talent needs and emerging talent gaps related to big data. Offers examples of organizations that are meeting this challenge head on. Recommends four steps HR and talent management professionals can take to bridge the talent gap.
Todays companies are dealing with an avalanche of data from socia.docxamit657720
Today's companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. According to one estimate, 2.5 quintillion bytes of data per day are generated around the world. Making sense of big data to improve decision making and business performance has become one of the primary opportunities for organizations of all shapes and sizes, but it also represents big challenges.
Green Mountain Coffee in Waterbury, Vermont, is analyzing both structured and unstructured audio and text data to learn more about customer behavior and buying patterns. The firm has 20 brands and more than 200 beverages and uses Calabrio Speech Analytics to glean insights from multiple interaction channels and data streams. In the past, Green Mountain was unable to use all the data it gathered when customers called its contact center. The company wanted to know more about how many people were asking for a specific product, which products generated the most questions, and which products and categories created the most confusion. By analyzing its big data, Green Mountain was able to gather information that was much more precise and use it to produce materials, web pages, and database entries to help representatives do their jobs more effectively. Management can now identify issues more rapidly before they create problems for customers.
A number of services have emerged to analyze big data to help consumers. There are now online services that enable consumers to check thousands of flight and hotel options and book their own reservations, tasks that travel agents previously handled. New mobile-based services make it even easier to compare prices and pick the best travel options. For instance, a mobile app from Sky scanner Ltd. shows deals from all over the web in one list sorted by price, duration, or airline so travelers don't have to scour multiple sites to book within their budget. Sky scanner uses information from more than 300 airlines, travel agents, and timetables and shapes the data into at-a-glance formats, with algorithms to keep pricing current and make predictions about who will have the best deal for a given market.
Big data is also providing benefits in law enforcement (see this chapter's Interactive Session on People), sports, education, science, and health care. A recent McKinsey Global Institute report estimated that the U.S. health care system could save $300 billion each year $1,000 per American through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. Health care companies are currently analyzing big data to determine the most effective and economical treatments for chronic illnesses and common diseases and provide personalized care recommendations to patients.
There are limits to using big data. A number of companies have rushed to start big data projects without first establishing a business goal for th ...
The modern American economy is driven by data. It determines the places where we shop, the products that we buy, the manner in which we buy them, and the way in which we express our reactions to these purchases.
Whitepaper - The need self service data tools, not scientistsJosh Howard
The federal government is one of the organizations most in need of data scientists, but hiring freezes, slashed training budgets and a lack of qualified candidates have all hampered the ability to recruit these types of professionals. Faced with such obstacles, agencies have been developing creative solutions to fill the hiring gap. Learn how to overcome these challenges with big data analytic tools.
The Insight Data Science Fellows Program is a 6-week postdoctoral training fellowship that teaches scientists industry skills in data science. The program is held in Silicon Valley and New York City and bridges the gap between academia and careers in data science. Fellows learn tools and techniques from mentors at companies and work on projects to gain skills and interview at mentor companies for jobs in data science.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
Bentley University partnered with labor market analytics firm Burning Glass to uncover which skills employers are looking for, what that means for the future of certain jobs, and how educational institutions should be preparing the next generation of our workforce.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
The whitepaper from IBM delves into workforce shifts and how organisations can leverage the shift, redesign work and build a smarter workforce to meet the organisation’s need for talent.
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
Big data is like a two-edged sword: It can bring many new opportunities for business, but it can also harm individuals and businesses in unanticipated ways
7 ‘Hidden’ Sources of Big Data That You HavePromptCloud
Big data is being generated by everything around us at all times. Data is arriving from multiple sources at an alarming velocity, volume and variety. Here are some underused sources of data.
This document discusses the rise of big data and its impact. It notes that while companies have always had to manage large amounts of data, new technologies and data sources have led to exponentially greater volumes, velocities, and varieties of data. Effectively analyzing this big data can provide valuable insights, but also poses major challenges in how to capture, store, manage and make sense of such diverse information. The document provides several examples of how cities and companies are now generating and analyzing big data through sensors and other technologies to improve services.
Big Data refers to large, complex datasets that traditional data processing applications are unable to handle efficiently. Spark is a fast, general engine for large-scale data processing that supports multiple languages and data sources. Spark uses resilient distributed datasets (RDDs) that operate on data stored in cluster memory for faster performance compared to the disk-based MapReduce model. DataFrames provide a distributed collection of data organized into named columns similar to a relational database, enabling SQL-like queries and optimizations.
Big Data Management and Employee Resilience of Deposit Money Banks in Port Ha...YogeshIJTSRD
The purpose of this study was to examine the relationship between Big Data Management and Employee Resilience of Deposit Money Banks in Port Harcourt, Rivers State, Nigeria. Primary data was generated through self administered questionnaire. This study was conducted in 17 Deposit Money Banks in Port Harcourt, Rivers State. The study used descriptive technique through the adoption of cross sectional research survey design. A total population of one hundred and two 102 managerial staff of the target banks was studied. A census sampling was adopted since the population was small. The reliability of the instrument was achieved by the use of the Cronbach Alpha coefficient with all the items scoring above 0.70. The hypotheses were tested using the Pearson Product Moment Correlation Coefficient with the aid of Statistical Package for Social Sciences version 23.0. The results of analysed data showed the dimensions of big data management data volume and data variety significantly correlated positively with the measures of employee resilience adaptive capacity and situational awareness. The study concludes that big data management significantly predicted employee resilience in Deposit Money Banks in Port Harcourt, Rivers State. Therefore, the study recommends that the dimensions treated in this study be adopted by management of banks as 21st century organization is gradually shifting interest from customers’ centric to data centric. Dr. (Mrs.) A. E. Bestman | Okparaji, Elera Sarah "Big Data Management and Employee Resilience of Deposit Money Banks in Port Harcourt, Rivers State, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41168.pdf Paper URL: https://www.ijtsrd.commanagement/general-management/41168/big-data-management-and-employee-resilience-of-deposit-money-banks-in-port-harcourt-rivers-state-nigeria/dr-mrs-a-e-bestman
Why is Data Science a Popular Career Choice.pdfUSDSI
Do you want to become the backbone of big corporates and giant business groups around the world? Beginning your career trajectory by grabbing the perfect spot in the data science certification courses provided around the world. The US Bureau of Labor Statistics projects 35.8% employment growth for data scientists till 2031, over a decade period beginning 2021. The growing use of machine learning and artificial intelligence technologies is another factor driving the demand for professionals skilled in data science.
Brimming with humungous career opportunities, the data science industry is set in motion to yield multitudinous growth opportunities across diversified industries worldwide. By automating procedures, increasing effectiveness, and allowing predictive capabilities, Artificial intelligence and machine learning algorithms hold the ability to change the entire landscape.
Data Science has become a fascinating career choice that calls for working closely with cutting-edge technology and addressing challenges. If you are someone who wishes to work with humungous data, has a passion for numbers, and has a clear vision of setting their career on a thriving path; data science is the right pick for you!
A diversified array of organizations is actively looking for data-hungry professionals who are coarsely skilled at data science to analyze data and churn out business decisions for the greater good of the company. Today is the ripe time to get started with a data science career, that promises an elevated trajectory and nothing else.
With the rise of technological innovations and industrial evolution, massive datasets become unmanageable. The future of such a massive explosion of data calls for an urgent appointment of qualified data scientists; enabling bigger business moves. This is where getting certified in the field makes sense.
Without wasting any further time, it is an advisable move to get certified in key data science skills that are sure to rage in the industry worldwide. Begin with the most trusted names in the data science certifications providers industry today!
https://www.usdsi.org/data-science-insights/why-is-data-science-a-popular-career-choice
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eQuest-Big Data for HR Insights - Significant Changes Shown in Job Seeker Search Habits on the Internet
1. 4/11/13 eQuest | Big Data for HR Insights
Significant Changes Shown in Job Seeker Search Habits on the
Internet
Big Data Study Shows Would-be Workers Widening Search
and Apply Days
Novemb er 13, 2012 – A recent report conducted by eQuest’s Big
Data division reveals that job seekers are no longer performing
job searches like they used to. eQuest is a middleware company
that delivers jobs on the Internet for the majority of the Global
Fortune 500. Job board, social media sites, and candidate view,
response, application and hiring data is collected and stored for
statistical research. Its database currently holds over 1.1 billion
records.
A sample study of over 1 million jobs posted on the Internet over
the last 90 days indicate that job seekers are searching and
submitting resumes at a fairly consistent rate from Monday
through Friday between the hours of 10am to 2pm with an
additional jump occurring after the dinner hour from 7pm to 9pm.
This is a dramatic difference from only a year ago when
candidate searches and responses were primarily occurring, and
at their highest rate, on Tuesday and Wednesday between 10am
and 2pm.
The report also found a significant jump in weekend activity,
normally a dead period for job seekers. Saturday and Sunday
activity jumped by over 100% from 2011.
Although the report indicates that overall traffic has become more
consistent; a deeper analysis revealed a huge variance in
candidate response rates depending on several characteristics –
the type of job, the type of career site, the location of the job, and
the method of device being searched from by the would-be job
seeker.
For example, jobs posted to Twitter experienced its highest level
of searches and responses from mobile devices on Sunday, Monday and Tuesdays – while Monster, CareerBuilder and
Indeed.com were at their strongest Tuesday, Wednesday and Thursday, from computers, during the lunch hour.
Healthcare job postings were most active when posted on Wednesday’s, retail jobs on Saturday’s, and technology jobs
were at their strongest Tuesday’s and Friday’s.
www.equest.com/products-services/big-data-for-hr-insights/ 1/2
2. 4/11/13 eQuest | Big Data for HR Insights
“It’s clear that companies must now consider the right day to post a job,” said David Bernstein, Vice President of eQuest’s
Big Data division. “Data must be utilized that can capture the highest level of candidate traffic for the right industry; at the right
job board; for the right location; at the right moment. Jobs posted on the wrong day could mean the difference between a hire
and a missed opportunity.”
This release is the first in a series of reports eQuest will be publishing as part of its Big Data for HR.
www.equest.com/products-services/big-data-for-hr-insights/ 2/2