Big Data (Büyük Veri) Nedir?ReneraldBig Data yani büyük veri nedir diyorsanız ve büyük veri analizinin ne gibi yararlar sağlayacağını merak ediyorsanız sizin için Renerald olarak bu sunumu hazırladık. Büyük veri analizleri sayesinde, stratejilerinizi bilimsel veriler ışığında geliştirip şirketinize inanılmaz artı değerler kazandırabileceksiniz.
Big Data / Büyük Veri Nedir?Veli BahçeciBig data kavramı hakkında en temel bilgiler ve örnek big data senaryolarının yer aldığı bir sunumdur. Büyük verinin hangi sektörlerde ve nasıl kullanılabileceğine dair ipuçları da yer alan sunumda 4 adet dikkat çekici video da yer almaktadır.
Yazılarıma göz atmak için: velibahceci.com 'u ziyaret edebilirsiniz.
Big dataPooja ShahThis document discusses big data, including its definition as large volumes of structured and unstructured data from various sources that represents an ongoing source for discovery and analysis. It describes the 3 V's of big data - volume, velocity and variety. Volume refers to the large amount of data stored, velocity is the speed at which the data is generated and processed, and variety means the different data formats. The document also outlines some advantages and disadvantages of big data, challenges in capturing, storing, sharing and analyzing large datasets, and examples of big data applications.
Big data analytics with Apache HadoopSuman SaurabhMapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
Big dataAmi Redwan HaqWhat is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
The Advantages and Disadvantages of Big DataNicha TatsaneeyapanThe 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.
Big dataNimish KochharThis document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
International Data Spaces: Data Sovereignty for Business Model InnovationBoris OttoThis presentation given at the European Big Data Value Forum on November 13, 2018, in Vienna introduces International Data Spaces (IDS) as a reference architecture and implementation for data sovereignty. The IDS archiecture rests on usage control technologies and trusted computing environments and, thus, forms a strategic enabler for a fair data economy which respects the interests of the data owners.
Big Data PPT by Rohit DubeyRohit DubeyThis document provides an introduction to big data. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It discusses the three V's of big data - volume, variety and velocity. Volume refers to the large scale of data. Variety means different data types. Velocity means the speed at which data is generated and processed. The document outlines topics that will be covered, including Hadoop, MapReduce, data mining techniques and graph databases. It provides examples of big data sources and challenges in capturing, analyzing and visualizing large and diverse data sets.
Büyük Veri, Hadoop Ekosistemi ve Veri BilimiAnkara Big Data MeetupThis document provides an overview of big data, Hadoop ecosystem, and data science. It discusses key concepts like what big data is, different types of big data, evolution of big data technologies, components of Hadoop ecosystem like MapReduce, HDFS, HBase, components for data ingestion and analytics. It also summarizes common techniques used in data science like descriptive analytics, predictive analytics, prescriptive analytics, and provides examples of exploratory data analysis and data mining.
Big data by Mithlesh sadhMithlesh SadhThis document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Big datavaleri kopaleishviliBig data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
Visualization For Data ScienceAngela ZossData science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. The rise of data science has accompanied a comparable rise in business intelligence and the demand for visualizations and dashboards that can explain models, summarize results, assist with decision making, and even predict outcomes. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. This talk will explore the landscape of visualization for data science – using visualization for data exploration and communication, reproducible approaches to visualization, and how to develop better instincts for visualization choice and graphic design.
Big DataVinayak KamathThis document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
Big DataRohit JainThis document provides an overview of big data, including:
- A brief history of big data from the 1920s to the coining of the term in 1989.
- An introduction explaining that big data requires different techniques and tools than traditional "small data" due to its larger size.
- A definition of big data as the storage and analysis of very large digital datasets that cannot be processed with traditional methods.
- The three key characteristics (3Vs) of big data: volume, velocity, and variety.
Big Data pptVivek GautamThis document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks and benefits. It defines big data as large volumes of diverse data that can be analyzed to reveal patterns and trends. The three key characteristics are volume, velocity and variety. Examples of big data sources include social media, sensors and user data. Tools used for big data include Hadoop, MongoDB and analytics programs. Big data has many applications and benefits but also risks regarding privacy and regulation. The future of big data is strong with the market expected to grow significantly in coming years.
Impact of big data on analyticsCapgeminiWhat is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Data Warehousing 2016Kent GrazianoThese are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
State of AI Report 2023 - Air Street CapitalAI Geek (wishesh)Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
Big DataSeminar LinksA Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big Data & Data ScienceBrijeshGoyaniI've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Big datagopichand naragamThis 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.
Big DataSubhavinolin RajaThe document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Big dataNimish KochharThis document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
International Data Spaces: Data Sovereignty for Business Model InnovationBoris OttoThis presentation given at the European Big Data Value Forum on November 13, 2018, in Vienna introduces International Data Spaces (IDS) as a reference architecture and implementation for data sovereignty. The IDS archiecture rests on usage control technologies and trusted computing environments and, thus, forms a strategic enabler for a fair data economy which respects the interests of the data owners.
Big Data PPT by Rohit DubeyRohit DubeyThis document provides an introduction to big data. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It discusses the three V's of big data - volume, variety and velocity. Volume refers to the large scale of data. Variety means different data types. Velocity means the speed at which data is generated and processed. The document outlines topics that will be covered, including Hadoop, MapReduce, data mining techniques and graph databases. It provides examples of big data sources and challenges in capturing, analyzing and visualizing large and diverse data sets.
Büyük Veri, Hadoop Ekosistemi ve Veri BilimiAnkara Big Data MeetupThis document provides an overview of big data, Hadoop ecosystem, and data science. It discusses key concepts like what big data is, different types of big data, evolution of big data technologies, components of Hadoop ecosystem like MapReduce, HDFS, HBase, components for data ingestion and analytics. It also summarizes common techniques used in data science like descriptive analytics, predictive analytics, prescriptive analytics, and provides examples of exploratory data analysis and data mining.
Big data by Mithlesh sadhMithlesh SadhThis document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Big datavaleri kopaleishviliBig data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
Visualization For Data ScienceAngela ZossData science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. The rise of data science has accompanied a comparable rise in business intelligence and the demand for visualizations and dashboards that can explain models, summarize results, assist with decision making, and even predict outcomes. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. This talk will explore the landscape of visualization for data science – using visualization for data exploration and communication, reproducible approaches to visualization, and how to develop better instincts for visualization choice and graphic design.
Big DataVinayak KamathThis document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
Big DataRohit JainThis document provides an overview of big data, including:
- A brief history of big data from the 1920s to the coining of the term in 1989.
- An introduction explaining that big data requires different techniques and tools than traditional "small data" due to its larger size.
- A definition of big data as the storage and analysis of very large digital datasets that cannot be processed with traditional methods.
- The three key characteristics (3Vs) of big data: volume, velocity, and variety.
Big Data pptVivek GautamThis document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks and benefits. It defines big data as large volumes of diverse data that can be analyzed to reveal patterns and trends. The three key characteristics are volume, velocity and variety. Examples of big data sources include social media, sensors and user data. Tools used for big data include Hadoop, MongoDB and analytics programs. Big data has many applications and benefits but also risks regarding privacy and regulation. The future of big data is strong with the market expected to grow significantly in coming years.
Impact of big data on analyticsCapgeminiWhat is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Data Warehousing 2016Kent GrazianoThese are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
State of AI Report 2023 - Air Street CapitalAI Geek (wishesh)Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
Big DataSeminar LinksA Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big Data & Data ScienceBrijeshGoyaniI've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Big datagopichand naragamThis 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.
Big DataSubhavinolin RajaThe document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Dijitalin Yükselişi ile Değişen Ticaret ve Pazarlama TrendleriCihan SalimBilişim teknolojileri ucuzlarken milyonlarca yeni kullanıcının dijitale erişiminin artmasıyla bilgi ve ürünün marjinal dağıtım maliyetleri 0'a doğru hızla iniyor.
Dağıtımı kolaylaşan bilgi, artık çok sahipli ve tek başına eskisi kadar güç sağlamıyor.
İnternet'in geçer akçesi, yeni para birimleri, dikkat ve itibar.
Bedava veya 'umursanmayacak kadar ucuz'u bir şekilde iş modellerine, ticaret ve ortaklık şekillerine dahil oluyor.
Kurumlar dışa dönük yüzlerinde nasıl bir iş modeli belirleyebilir, içe dönükken yeni teknolojilerden nasıl faydalanabilir?
Tüm bu dinamiklerin ışığında pazarlama, dijitalle imtihanında sınıfta kalmamak için nasıl değişmeli?bi
12. map info kullanıcı konferansı altdataAltan Atabarut, MSc.Başarsoft Mapinfo Konferansı Alteryx Sunumu.
Self-servis veri analizi ve coğrafi verilerle mevcut müşteri verileri ve 3. parti veri setlerinin birleşimine örnekler...
Dijital LiderlikÖzgür Kurtuluşİş yaşamı profesyonellerinin, dijital teknolojiler ile ortaya çıkan önemli inovasyon fırsatlarını, organizasyona değer sağlamak amacıyla kullanmak için, iş modellerini tanımlamaya ve tasarlamaya yönelik olarak yönetimi ve diğer personeli yönlendirme becerileridir.
Yeni Medya, Nesnelerin İnterneti ve Pazarlamanın GeleceğiYiğit KalafatoğluSevgili Sunay Şener ile birlikte kurduğumuz, Kadir Has Üniversitesi'ne bağlı #IoTAkademi bünyesinde gerçekleşen "Nesnelerin İnterneti İle Pazarlama" Sertifika programı müfredatı ve eğitmenlerinin anlatılarından; ayrıca 2014 yılında kaleme aldığım "Prospective Paradigm of Marketing Studies: Internet Of Things" makalesinden yararlanarak oluşturduğum ve ilk olarak Bilkent Üniversitesi'nde TTGV'bin konuğu olarak aktardığım; "IOT 101" tadındaki sunumum. Yorumlarınızı bekliyorum.
Novida-Dijital Güç (Bilen Toplum)Novida Global2017 yılının verileriyle derlenen sunumda Dijital Dünyanın nerelere evrilmekte olduğu, Kestirimsel Veri Analitiğinin artacak önemi ve Pazarlamanın yeniden şekillenmesi anlatılmaltadır.
Veri Madenciliği ve Makine Öğrenmesi Konularına GirişŞadi Evren ŞEKERVeri bilimi ve ver madenciliği konularına giriş, sektörün durumu, iş imkanları, problemleri ve geleceği.
Veri Kullanımı ve Programatik Reklamcılık- IABMutlu Dogus YildirimAnkara Marka Festivali Sunumu
Konu: Veri Kullanımı ve Programatik Reklamcılık
Verinin dijital reklamcılık dünyasındaki yeri, programatik reklamcılıki ve programatik reklamcılığın veriyle olan ilişkisini inceliyoruz.
2. İlknur Demirbaş
Hacettepe Üniversitesi Bilgisayar Mühendisliği (2012)
Yazılım Mühendisliği (2012-2016)
İçerik Üreticisi (https://www.instagram.com/ailece.ankara/)
3. BÜYÜK VERİ (BIG DATA) NEDİR?
Büyük boyutta olan & zamanla katlanarak büyüyen
veri
Tüm bilgilerin dijital olarak olarak ulaşılabilir olması
Örn. Sosyal medya paylaşımları, bloglar, elektronik
cihazların log dosyaları, sensörlerden gelen veriler,
GSM operatörlerinin arama kayıtları
Bilgi çöplüğünden hazine çıkarma
5. BÜYÜK VERİ (BIG DATA)
Yapılandırılmış veri: Ürün, kategori, müşteri, fatura..
Yapılandırılmamış veri: Tweet, beğeni, tıklama..
Geleneksel veritabanı araçları ve algoritmaları ile
işlemesi zor
Artan depolama ve hesaplama kapasitesi
Nesnelerin İnterneti (Internet of Things, kısaca IoT)
6. BÜYÜK VERİNİN AVANTAJLARI
Örnekleme modeli Tüm verinin analizi
Daha doğru analizler, doğru aksiyonlar
Tasarruf(Para, Zaman, Emek)
Daha iyi bir dünya
7. BÜYÜK VERİNİN AVANTAJLARI
ÖRNEKLER
Dolandırıcılığın önlemesi (Bankacılık)
Batı Afrika’da Ebola salgınının Big Data ile önlenmesi (Sağlık sektörü)
Navigasyonlar (Ulaşım sektörü)
Topografi ve iklim ölçümleri yapılmasını sağlayarak daha verimli tarım yapılması (Tarım sektörü)
Tüketicinin ihtiyaç ve taleplerini daha iyi tespit ederek kişiselleşmiş ürünler önerilmesi (İmalat ve
Perakende sektörü)
9. BÜYÜK VERİ (BIG DATA) RİSKLERİ
Kişisel veriler
Dijital Fişleme
Kişiye özel düzenlenmiş içerik akışı & PsikoAnaliz