This document discusses the rise of big data and data-driven economies. It notes that data has become a new class of economic asset and that many governments and organizations have recognized the importance of harnessing big data. It then describes some of the key characteristics of big data and drivers that are generating large volumes of data such as mobile devices, the internet of things, user-generated content, and cloud computing. The remainder of the document discusses concepts such as the data value chain, different types of data analytics, and various use cases and case studies to illustrate how big data is being applied.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
6 levels of big data analytics applicationspanoratio
6 levels of big data analytics applications: what you can expect from descriptive, investigative, advanced, adaptive, predictive, prescriptive analytics applications.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
Data Science and Big Data Analytics are everywhere. They are buzzwords that everyone is talking about. Garnet even released Hype Cycle for Data Science in July this year. And yet, many people are still confused as to what data science and big data analytics are and why they will become the new black!
This slide focuses on the core concepts and clarify the mis-understanding of those myths.
Machine learning is permeating nearly every industry – from retail and financial services to entertainment and transportation. And, while it's been slow to make its way into healthcare, machine learning stands to transform this space, too… positioning us to better diagnose, predict outcomes, provide follow-up care, and tailor treatments.
In this webinar, PointClear Solutions' Michael Atkins discusses the current state of machine learning in healthcare and what we can expect in the near future:
• What is machine learning and how is it being used today?
• What are some of the risks and obstacles we face in implementing this new technology?
• Looking into the future, what role will machine learning play in transforming healthcare?
• How can my company prepare for machine learning?
This document discusses the rise of big data and data-driven economies. It notes that data has become a new class of economic asset and that many governments and organizations have recognized the importance of harnessing big data. It then describes some of the key characteristics of big data and drivers that are generating large volumes of data such as mobile devices, the internet of things, user-generated content, and cloud computing. The remainder of the document discusses concepts such as the data value chain, different types of data analytics, and various use cases and case studies to illustrate how big data is being applied.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
6 levels of big data analytics applicationspanoratio
6 levels of big data analytics applications: what you can expect from descriptive, investigative, advanced, adaptive, predictive, prescriptive analytics applications.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
Data Science and Big Data Analytics are everywhere. They are buzzwords that everyone is talking about. Garnet even released Hype Cycle for Data Science in July this year. And yet, many people are still confused as to what data science and big data analytics are and why they will become the new black!
This slide focuses on the core concepts and clarify the mis-understanding of those myths.
Machine learning is permeating nearly every industry – from retail and financial services to entertainment and transportation. And, while it's been slow to make its way into healthcare, machine learning stands to transform this space, too… positioning us to better diagnose, predict outcomes, provide follow-up care, and tailor treatments.
In this webinar, PointClear Solutions' Michael Atkins discusses the current state of machine learning in healthcare and what we can expect in the near future:
• What is machine learning and how is it being used today?
• What are some of the risks and obstacles we face in implementing this new technology?
• Looking into the future, what role will machine learning play in transforming healthcare?
• How can my company prepare for machine learning?
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
El big data analytics donde menos te lo esperas - Alex RayónBig-Data-Summit
This document discusses applications of big data analytics in unexpected areas. It focuses on applications in music and agriculture. In music, big data is used to explore data, perform clustering, association rules, prediction, and analyze social media. In agriculture, big data is used to analyze variables affecting beet performance and quality, optimize costs, characterize farmers, and understand what makes farmers change practices. The project aims to help stakeholders by providing tailored advice to farmers to improve productivity and quality.
The document discusses the advantages and disadvantages of big data. It begins by defining big data and noting some common misconceptions. The advantages of big data include its volume, variety, velocity, and potential value. However, the disadvantages include the resources needed to work with big data, the costs associated with it, security risks, and challenges in finding the right analytics tools.
We are generating 2.5 Billion GB of data every day. That's a lot of data! We will need super human expertise to make sense out of it. Well, that's exactly what AI can help us do it.
This talk is going to focus on:
i) What is AI?
ii) How AI can help with health care?
iii) How FHIR will help with the adoption of AI
iv) What are the next three steps for any health organization in order to adopt AI?
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
Ten years ago there were rumours of the death of causal inference. Big data was supposed to enable us to rely on purely correlational data to predict and control the world.
https://www.bigdataspain.org/2017/talk/why-big-data-didnt-end-causal-inference
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This document discusses big data and provides examples to illustrate key concepts. It begins by defining big data in terms of volume, velocity, and variety of data from various sources like the internet and social networks. It then discusses enterprise information systems and new paradigms for storing, processing, and communicating large amounts of data using cloud computing and distributed systems. Examples are given of how data can be analyzed to gain insights and knowledge to inform decision making, such as what products people buy before hurricanes. The document outlines the big data technology stack and discusses tools for capturing data, processing it, analyzing correlations and causality, and formulating hypotheses. Finally, it provides the specific example of SportVU, a player tracking technology, to demonstrate how
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
Societal Impact of Applied Data Science on the Big Data StackStealth Project
This document discusses big data and data science projects at a Center for Data Science. It provides an overview of various research areas like healthcare informatics, intelligent systems, social computing, and big data security. It also describes technologies used for big data like machine learning, distributed databases, and data integration. Specific projects are summarized, such as predicting hospital readmissions for congestive heart failure patients and detecting malware activity based on domain names. The document outlines the steps involved in building predictive models, from data understanding to predictive modeling. Performance of initial models is discussed, with areas for improvement noted.
Identifying sick cannabis with ai defcon 2018Harry Moreno
This talk covers how we built a predictive model for plant disease in Cannabis. We cover methodology, model training, model evaluation, deployment and ideas for improving the model. Check out the deployed model at https://chronicsickness.com
In this presentation from the Hurricane Electric Carrier Event, Rich Brueckner from insideBIGDATA describes what's really behind this phenomenon and why you should care.
Watch the video presentation: http://wp.me/p3RLEV-1r1
When Big Data and Predictive Analytics Collide: Visual Magic HappensInfini Graph
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive analysis.
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
Applied Machine Learning for the IoT - Data Science Pop-up SeattleDomino Data Lab
The Internet of Things is about data, not things. Some forecasts that by 2018 the number of connect things will exceed the combined number of personal computers, smartphones, and tablets. Each ’thing’ can produce a tremendous stream of data from sensors and other sources. This presentation will discuss progress, examples, challenges, and opportunities with machine learning for the IoT. A short presentation will be done on some recent applications of ML (using H2O) to the domains of machine prognostics / health management (PHM) and agriculture. Presented by Hank Roark, Data Scientist / Hacker at
H2O.ai.
Data Science is the new black! However, becoming a data scientist requires knowledges in various areas. This slide discuss what one should learn to become a data scientist.
The document discusses whether Facebook can cause depression in users. It notes that heavy Facebook users may see their own lives as less interesting compared to peers' profiles. Previous studies show a link between Facebook and depression, but the causation is unclear. The author conducted a small survey of 20 teens and found that while Facebook is not solely to blame, it can trigger depressed feelings in those already prone to depression by enhancing feelings of social disconnection. Some solutions proposed are remembering that Facebook does not show full realities and taking a break from the platform.
Kenneth Cukier discusses the frontiers of big data and how it is transforming how we live, work and think. Some key points are that with big data, more data is not just more of the same but rather is new, better and different. Big data allows for new insights like identifying drug side effects from search queries without a medical study. Data is becoming a new raw material and machine learning is powering advances in areas like computer translation, speech recognition and self-driving cars. However, regulation is needed regarding privacy and ownership with increased data collection.
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
El big data analytics donde menos te lo esperas - Alex RayónBig-Data-Summit
This document discusses applications of big data analytics in unexpected areas. It focuses on applications in music and agriculture. In music, big data is used to explore data, perform clustering, association rules, prediction, and analyze social media. In agriculture, big data is used to analyze variables affecting beet performance and quality, optimize costs, characterize farmers, and understand what makes farmers change practices. The project aims to help stakeholders by providing tailored advice to farmers to improve productivity and quality.
The document discusses the advantages and disadvantages of big data. It begins by defining big data and noting some common misconceptions. The advantages of big data include its volume, variety, velocity, and potential value. However, the disadvantages include the resources needed to work with big data, the costs associated with it, security risks, and challenges in finding the right analytics tools.
We are generating 2.5 Billion GB of data every day. That's a lot of data! We will need super human expertise to make sense out of it. Well, that's exactly what AI can help us do it.
This talk is going to focus on:
i) What is AI?
ii) How AI can help with health care?
iii) How FHIR will help with the adoption of AI
iv) What are the next three steps for any health organization in order to adopt AI?
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
Ten years ago there were rumours of the death of causal inference. Big data was supposed to enable us to rely on purely correlational data to predict and control the world.
https://www.bigdataspain.org/2017/talk/why-big-data-didnt-end-causal-inference
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This document discusses big data and provides examples to illustrate key concepts. It begins by defining big data in terms of volume, velocity, and variety of data from various sources like the internet and social networks. It then discusses enterprise information systems and new paradigms for storing, processing, and communicating large amounts of data using cloud computing and distributed systems. Examples are given of how data can be analyzed to gain insights and knowledge to inform decision making, such as what products people buy before hurricanes. The document outlines the big data technology stack and discusses tools for capturing data, processing it, analyzing correlations and causality, and formulating hypotheses. Finally, it provides the specific example of SportVU, a player tracking technology, to demonstrate how
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
Societal Impact of Applied Data Science on the Big Data StackStealth Project
This document discusses big data and data science projects at a Center for Data Science. It provides an overview of various research areas like healthcare informatics, intelligent systems, social computing, and big data security. It also describes technologies used for big data like machine learning, distributed databases, and data integration. Specific projects are summarized, such as predicting hospital readmissions for congestive heart failure patients and detecting malware activity based on domain names. The document outlines the steps involved in building predictive models, from data understanding to predictive modeling. Performance of initial models is discussed, with areas for improvement noted.
Identifying sick cannabis with ai defcon 2018Harry Moreno
This talk covers how we built a predictive model for plant disease in Cannabis. We cover methodology, model training, model evaluation, deployment and ideas for improving the model. Check out the deployed model at https://chronicsickness.com
In this presentation from the Hurricane Electric Carrier Event, Rich Brueckner from insideBIGDATA describes what's really behind this phenomenon and why you should care.
Watch the video presentation: http://wp.me/p3RLEV-1r1
When Big Data and Predictive Analytics Collide: Visual Magic HappensInfini Graph
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive analysis.
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
Applied Machine Learning for the IoT - Data Science Pop-up SeattleDomino Data Lab
The Internet of Things is about data, not things. Some forecasts that by 2018 the number of connect things will exceed the combined number of personal computers, smartphones, and tablets. Each ’thing’ can produce a tremendous stream of data from sensors and other sources. This presentation will discuss progress, examples, challenges, and opportunities with machine learning for the IoT. A short presentation will be done on some recent applications of ML (using H2O) to the domains of machine prognostics / health management (PHM) and agriculture. Presented by Hank Roark, Data Scientist / Hacker at
H2O.ai.
Data Science is the new black! However, becoming a data scientist requires knowledges in various areas. This slide discuss what one should learn to become a data scientist.
The document discusses whether Facebook can cause depression in users. It notes that heavy Facebook users may see their own lives as less interesting compared to peers' profiles. Previous studies show a link between Facebook and depression, but the causation is unclear. The author conducted a small survey of 20 teens and found that while Facebook is not solely to blame, it can trigger depressed feelings in those already prone to depression by enhancing feelings of social disconnection. Some solutions proposed are remembering that Facebook does not show full realities and taking a break from the platform.
Kenneth Cukier discusses the frontiers of big data and how it is transforming how we live, work and think. Some key points are that with big data, more data is not just more of the same but rather is new, better and different. Big data allows for new insights like identifying drug side effects from search queries without a medical study. Data is becoming a new raw material and machine learning is powering advances in areas like computer translation, speech recognition and self-driving cars. However, regulation is needed regarding privacy and ownership with increased data collection.
This document discusses the economic benefits of big data, including innovation, improved decision making, and new jobs, but also notes there are problems that come along with these benefits, such as optimization over ethics and potential mass unemployment. It argues that to realize the economic gains of big data, these problems must be addressed through leadership, strong institutions, and empowered individuals.
This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
Winning the big data revolution: what businesses leaders need to knowMicrosoft Ideas
Tout le monde parle des big data. Mais peu nombreux sont ceux qui savent véritablement ce qu’ils désignent et surtout comment les entreprises peuvent les utiliser pour révéler leur véritable potentiel. Comment donner du sens à ces données ? Comment en faire un avantage compétitif majeur ? Comment sortir vainqueur de cette révolution ? A l’occasion de la sortie de son dernier ouvrage, dans lequel il explore le phénomène big data et les nouveaux business model qui sont en train d’émerger, Kenneth Cukier nous livrera, lors d’une keynote inédite, les clefs pour réussir cette transformation fondamentale.
Speakers : Kenneth Cukier (The Economist)
How Digital & Big Data Revolution Will Transform Primary Care MedicinePYA, P.C.
A recent presentation given by PYA Principals Kent Bottles, MD, and David McMillan provides food for thought when it comes to the digital transformation of primary care medicine. The pair spoke at the University of North Carolina Physicians Network on the topic “How Digital & Big Data Revolution Will Transform Primary Care Medicine.”
This document discusses some of the key issues around big data including potential implications, views on data collection purposes, identity theft concerns, and regulatory challenges. It notes that while big data is often viewed as increasing identity theft risks, the overall rates have actually remained flat since 2005. Additionally, the EU is poised for sweeping changes to data protection laws for member states, while US policy reviews are underway due to Edward Snowden's NSA leaks.
THE NEW ETHICS OF BIG DATA - KENNETH CUKIERBig Data Week
Kenneth Cukier is the Data Editor of The Economist in London and the co-author of the award-winning book “Big Data: A Revolution That Will Transform How We Live, Work, and Think” with Viktor Mayer-Schönberger in 2013, a New York Times Bestseller translated into 20 languages. He is a regular commentator on BBC, CNN, and NPR, and a member of the World Economic Forum’s council on data-driven development. In 2002-04, Mr. Cukier was a research fellow at Harvard’s Kennedy School of Government. He is a board director of International Bridges to Justice and a member of the Council on Foreign Relations.
The Identity Crisis: The Story of a Social Media DisasterJenniferDong95
Living in the new world of social media and technology, society has developed an identity crisis. We have become obsessed with creating an appearance of happiness, success, and perfection. Our obsession with self-presentation is deceiving and dangerous. We have lost the ability to see reality by fostering a false sense of identity developed through our social networks. From “selfie addictions” to low self-esteem, we have already been warned of the dangerous implications. However, what is most terrifying is our utter ignorance to our “loss of self.” In the words of Stephen Marche, “The more you try to be happy, the less happy you are.” By continuing down this path of addiction, obsession, and instability, how can we ever achieve a state of true happiness? Most importantly, how can we find ourselves amidst a world of technological madness?
Big data and emerging technologies like the Internet of Things are causing an explosion in the amount of data available, which will reach 44 zettabytes by 2020. This "third platform" of cloud, social, big data, and mobility technologies is disrupting businesses and providing new opportunities for data-driven decisions and insights. Data analytics techniques like descriptive, diagnostic, predictive, and prescriptive analytics can enhance productivity when applied to these large and diverse data sources.
This document discusses big data in the oil and gas industry. It defines big data as high volumes of data from various sources that comes in at a fast velocity. This data has value for oil and gas companies by enabling quicker and more accurate decisions. The document outlines sources of big data growth for oil and gas companies and how big data is driving innovation. It also discusses how the oil and gas industry represents the majority of the energy industry and how big data can provide value through business decisions, investments, production planning and safety.
As the Big Data market has evolved, the focus has shifted from data operations (storage, access and processing of data) to data science (understanding, analyzing and forecasting from data). And as new models are developed, organizations need a process for deploying analytics from research into the production environment. In this talk, we'll describe the five stages of real-time analytics deployment:
Data distillation
Model development
Model validation and deployment
Model refresh
Real-time model scoring
We'll review the technologies supporting each stage, and how Revolution Analytics software works with the entire analytics stack to bring Big Data analytics to real-time production environments.
The document discusses the history and future of wearable technology. It describes how wearables have evolved from early inventions in the 1980s to today's popular devices in areas like fitness tracking and smartwatches. The document also explores the growing markets for wearables in industries like healthcare, fashion, and entertainment. Experts predict that wearables will become smaller, more integrated into daily life, and able to monitor more health data over the next decade as the technology continues to advance.
Big Data Analytics and Hadoop is presented. Key points include:
- Big data is large and complex data that is difficult to process using traditional methods. Domains that produce large datasets include meteorology, physics simulations, and internet search.
- The four V's of big data are volume, velocity, variety, and veracity. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. Its core components are HDFS for storage and MapReduce for processing.
- Apache Hadoop has gained popularity for big data analytics due to its ability to process large amounts of data in parallel using commodity hardware, its scalability, and automatic failover. A Hadoop ecosystem of
Big data refers to extremely large data sets that are too large to be processed with traditional data processing tools. It is data that is growing exponentially over time. Examples include terabytes of new stock exchange data daily and petabytes of new data uploaded to Facebook each day from photos, videos, and messages. Big data comes in structured, unstructured, and semi-structured forms. It is characterized by its volume, variety, and velocity. Big data analytics uses specialized tools to analyze these huge datasets to discover useful patterns and information that can help organizations understand the data. Tools for big data analytics include Hadoop, Lumify, Elasticsearch, and MongoDB. Big data has applications in banking, media, healthcare, manufacturing, government, and other
Big Data Definition & Characteristic.
Company Dominates Big Data.
Big Data and Other Technologies.
Big Data and UN.
Big Data for Statistics.
Big Data for Development.
Big data & Open Data.
Big data & SDG’s.
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, risks, benefits, and future. It defines big data as large, diverse, and growing datasets that require new processing techniques. The key characteristics are volume, velocity, and variety. Common sources include user data, sensors, social media, and system logs. Tools used include Hadoop, Spark, MongoDB and cloud platforms. Applications span customer analytics, business intelligence and scientific research. Risks include privacy and escalating costs, while benefits are improved decision making, customer insights and new business opportunities. The future of big data is projected to be a multi-billion dollar industry with growing demand for data scientists and analysts.
This document discusses big data principles including what data is, why big data is important, how it differs from traditional data, and its key characteristics. Big data is characterized by volume, variety, and velocity. It comes from many sources and in many formats. Tools like Hadoop enable storage and analysis at scale. Applications include search, customer analytics, business optimization, health, and security. Benefits are better decisions and flexibility to store now and analyze later. The future of big data is predicted to be a $100 billion industry growing at 10% annually.
This document provides an introduction to understanding big data analytics. It defines big data as information that can't be processed or analyzed using traditional tools. Big data is growing rapidly, doubling every year, and by 2020 about 1.7 megabytes of new information will be created every second for every person on Earth.
The document outlines a plan to explain what big data is, why it is important, what data analytics is, and where it is used. It defines data analytics as examining, inspecting, cleansing, transforming, and modeling data to draw conclusions. The document discusses descriptive, predictive, diagnostic and unsupervised/supervised analytics methods. It concludes that big data analytics is an important research topic that allows for descriptive and predictive analysis
This document provides an overview of data science, big data, and the data preprocessing steps involved in data science projects. It defines data science as extracting meaningful insights from large, structured and unstructured data using scientific methods, technologies and algorithms. It also defines big data in terms of the volume, variety and velocity of data. The document outlines common data sources that generate big data and applications of big data such as in finance, healthcare, transportation and more. It concludes by describing the key steps in data preprocessing: data cleaning, transformation and reduction to prepare raw data for analysis.
This document discusses big data, defining it as large, complex data that cannot be handled by traditional data tools. It notes that big data is generated from sources like social media, mobile devices, sensors, and scientific instruments. It then describes characteristics of big data like volume, velocity, and variety. The document outlines technologies used to manage big data, including Hadoop, MapReduce, and BigTable. It concludes by discussing advantages of analyzing big data and always changing nature of big data.
Business Analytics and Data mining.pdfssuser0413ec
Business analytics involves analyzing large amounts of data to discover patterns and make predictions. It uses techniques like data mining, predictive analytics, and statistical analysis. The goals are to help businesses make smarter decisions, identify trends, and improve performance. Data mining is the process of automatically discovering useful patterns from large data sets. It is used to extract knowledge from vast amounts of data that would otherwise be unknown. Data mining helps businesses gain insights from their data to increase sales, improve customer retention, and enhance brand experience.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
The document provides an overview of big data concepts including definitions, statistics on data generation and internet usage, applications and examples, challenges, and data types. It discusses key big data concepts such as the 3Vs of volume, velocity and variety; more Vs including veracity, value and visualization; data science areas and skills; the data workflow; and examples from companies like UPS, Walmart, eBay, and Kaiser Permanente.
Big data PPT prepared by Hritika Raj (Shivalik college of engg.)Hritika Raj
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, risks and benefits. Big data is characterized by volume, velocity and variety of structured and unstructured data that is growing exponentially. It is generated from sources like mobile devices, sensors, social media and more. Tools like Hadoop, MapReduce and data analytics are used to extract value from big data. Potential applications include healthcare, security, manufacturing and more. Risks include privacy and scale, while benefits include improved decision making and new business opportunities. The big data industry is rapidly growing and transforming IT and business.
This document provides an introduction to big data concepts. It discusses the characteristics of big data, including volume, velocity, variety, veracity, and value. Volume refers to the large amount of data being generated. Velocity refers to the speed at which data is created and needs to be analyzed. Variety means data comes in different forms like text, images, video. Veracity refers to the quality and reliability of data. Value means the usefulness of data for businesses. The document also covers challenges in analyzing big data and different technologies used like Hadoop, Spark and cloud computing.
This document provides an overview of big data analytics. It defines big data as large, complex datasets that require new techniques and tools to analyze. The key characteristics of big data are described as the 5 V's: volume, velocity, variety, veracity, and value. Hadoop is introduced as an open-source framework for distributed processing of large datasets across clusters of computers using MapReduce. The document also outlines different types of big data analytics including descriptive, predictive, supervised, and unsupervised analytics. It concludes with an overview of the analytics life cycle and some common analytics tools.
This document provides an introduction to a training course on big data analytics. It discusses why big data has become important due to the exponential growth in data volume, velocity, and variety. The course aims to focus on cloud-based storage and processing of big data using systems like HDFS, MapReduce, HBase and Storm. It emphasizes that learning involves actively asking questions. Big data is introduced by explaining the three V's of volume, velocity and variety. Examples of big data usage are given in areas like baseball analytics, political campaigns and election predictions. Challenges of big data integration and processing large volumes of heterogeneous data are also covered.
Introduction to Big Data
Big Data is a massive collection of data that is growing exponentially over time.
It is a data set that is so large and complex that traditional data management tools cannot store or process it efficiently.
Big data is a type of data that is extremely large in size.
The document discusses big data analytics and related topics. It covers the evolution of technology, an overview of big data analytics including the 5 V's (volume, variety, velocity, value, and veracity). It also discusses research topics in big data, tools and software, literature surveys on various big data studies, identified research gaps, and a proposed activity chart and bibliography. The document provides a comprehensive overview of big data analytics, key concepts, potential research areas, and literature in the field.
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by its volume, velocity and variety. It is generated from sources like users, applications, sensors and more. Tools like Hadoop and databases are used to store, process and analyze big data. Big data analytics can provide benefits across many industries and applications. However, it also poses risks around privacy, costs and skills that must be addressed. The future of big data is promising, with the market expected to grow significantly in the coming years.
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Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
2. Contents
• Introduction
• What is Big Data?
• Characteristics of Big Data
• What is Big Data analytics?
• How does Big Data work?
• Application of Big Data
• Big Data growth
• What’s trending
• Conclusion
• References
3. Introduction
• A decade ago amount of data produced was less.
• Today the amount of data in the world is increasing
rapidly, outstripping not only our machines, but also
our imagination.
4. What can be done with this data?
• Scrapping this data is not a great idea.
• Big data has the potential to help companies improve
operations and make faster, more intelligent and
accurate decisions.
• More accurate analyses will lead to more confident
and effective decision making. And better decisions
can mean cost reductions and reduced risk.
5. Definition
• Big Data is a new term given to a diverse field of data
analysis in which the datasets are so massive that
they become hard to store, work, predict and analyze
using traditional databases and software.
7. Volume
• It is the quantity of data generated that determines
the value and potential of data .
• Facebook, gets more than 12 million photos every
hour .
• Tweets on twitter cross over 400 million every day.
8. Velocity
• Its states the rate at which data is generated.
• Every minute on YouTube 48 hours of new videos are
uploaded.
• Every minute Google processes 2 million search
queries.
9. Variety
• It is the category to which the data belongs.
• The categories include Health sectors, Social
networking, Banking etc.
10. What is Big Data analytics?
• Analyzing the large data and reaching to conclusions
is called as Big Data analytics .
• Explanation using real life incidents,
– Google’s Flu Trends.
– Target Retailer.
11. Google’s Flu Trends
• Here Google predicted the flu trends just by
analyzing the data.
• In the year 2009 a new flu virus ‘H1N1’ was
discovered.
• 250-500k deaths every year, worldwide.
• Swine flu pandemic is worse.
• Surveillance
Centers for Disease Control and Prevention (CDC).
Problems Faced by CDC,
– Weekly
– 1-2 week publication lag
12. • Google took 50 million common search terms that
was typed in United States and compared the
number with CDC data on the spread of the flu.
• They processed 450 million different models in order
to test the search terms and prediction was almost
similar the stats processed by CDC .
What did they do?
13. Target Retailer
• Target retailer predicted the pregnancy just by
analyzing the buy trends of the consumers.
• Story of a pregnant teenager.
• This shows that real time data is never false.
14. How Big Data Works?
• Apache Hadoop -Apache Hadoop is the software
most commonly associated with Big Data. Apache
states it as “a framework that allows us for the
distributed processing of massive data sets across
clusters of computers using simple programming
models”.
• With Hadoop, no data is too big. It is possible to
process a huge data in just 3 minutes which takes
more than 20 hours for traditional systems.
15. • MapReduce - To make effective splitting of data
MapReduce is used. It is a software framework that
allows primary to split the input data set into
independent chunks that are processed in a
completely parallel manner.
Simple Block Diagram
18. What’s trending
• By analyzing the Big Data of DNA it is possible cure
genetic diseases like cancer.
• This can even predict where terrorists try to attack
only by analyzing the data.
19. Conclusion
• Big Data is the next big thing. Its about letting data
speak and real time data is never false, hence it is a
revolution that will transform how we think, live and
work.
20. References
• Victor Mayer-Schonberger, Kenneth Cukier “Big Data
– A Revolution”.
• Doing Data Science, By Cathy O'Neil, Rachel Schutt
Publisher: O'Reilly Media.
• http://hadoop.apache.org
Thank You