Data Mining with big data total ieee project and entire files.Kinnudj Amee
abstract, literature survey, implementaion, sample code, html code, project description, bibilography, conclusion, result, modules, uml diagrams, design, and etc.
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 refers to large datasets that are too complex for traditional data processing applications. Examples include Wikipedia which contains terabytes of text and images. Big data is characterized by being automatically generated, from new sources like the internet, and not designed for easy use. Analyzing big data can provide competitive advantages through insights from hidden patterns. Tools used for big data include distributed servers, cloud computing, distributed storage, distributed processing, and high performance databases. Data mining of big data helps businesses make better decisions by discovering patterns and relationships. Applications of big data include smarter healthcare, homeland security, traffic control, and more. Risks include being overwhelmed by data, escalating costs, and privacy issues. Big data impacts IT through new job opportunities in
Big 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.
This document discusses mining social data from online sources to gain insights. It defines social data and information, and notes that unstructured data found online provides a rich source of knowledge. It recommends developing skills in statistics, data processing, and data visualization to extract value from social data. Finally, it outlines best practices for social media analytics, including defining goals, selecting metrics, targeting data sources, using analytics tools, and delivering insights through dashboards, reports, and infographics.
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
This document provides an overview of big data including:
- Defining big data as large and complex data sets that cannot be managed with traditional database tools.
- Describing the four V's of big data: volume, velocity, variety and veracity.
- Providing examples of how big data is being used including by companies to manage legal spend, for litigation strategy and jury selection, and potential uses for law firms like predictive analysis and improving business development.
This 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.
Data Mining with big data total ieee project and entire files.Kinnudj Amee
abstract, literature survey, implementaion, sample code, html code, project description, bibilography, conclusion, result, modules, uml diagrams, design, and etc.
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 refers to large datasets that are too complex for traditional data processing applications. Examples include Wikipedia which contains terabytes of text and images. Big data is characterized by being automatically generated, from new sources like the internet, and not designed for easy use. Analyzing big data can provide competitive advantages through insights from hidden patterns. Tools used for big data include distributed servers, cloud computing, distributed storage, distributed processing, and high performance databases. Data mining of big data helps businesses make better decisions by discovering patterns and relationships. Applications of big data include smarter healthcare, homeland security, traffic control, and more. Risks include being overwhelmed by data, escalating costs, and privacy issues. Big data impacts IT through new job opportunities in
Big 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.
This document discusses mining social data from online sources to gain insights. It defines social data and information, and notes that unstructured data found online provides a rich source of knowledge. It recommends developing skills in statistics, data processing, and data visualization to extract value from social data. Finally, it outlines best practices for social media analytics, including defining goals, selecting metrics, targeting data sources, using analytics tools, and delivering insights through dashboards, reports, and infographics.
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
This document provides an overview of big data including:
- Defining big data as large and complex data sets that cannot be managed with traditional database tools.
- Describing the four V's of big data: volume, velocity, variety and veracity.
- Providing examples of how big data is being used including by companies to manage legal spend, for litigation strategy and jury selection, and potential uses for law firms like predictive analysis and improving business development.
This 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.
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy.
This document discusses how scholars can prepare for the future of big data in relation to Islamic knowledge and religious ideology. It recommends that scholars take incremental steps in the near and mid terms to focus on improving business performance through big data. It also stresses the importance of moving past pilot projects, integrating different data repositories, establishing data-driven decision making processes, and having the right people and leadership to work towards these goals.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Big data is large and complex data that cannot be processed by traditional data management tools. It is characterized by high volume, velocity, and variety. Big data comes from many sources and in many formats, including structured, unstructured, and semi-structured data. Storing and processing big data requires specialized systems like Hadoop and NoSQL databases. Big data analytics can provide benefits like improved business decisions and customer satisfaction when applied to areas such as healthcare, security, and manufacturing. However, big data also presents risks regarding privacy, costs, and being overwhelmed by the volume of data.
Companies are increasingly using big data technologies like Hadoop to store and analyze large amounts of customer data to gain insights. This raises security issues as more data is collected and needs to be properly classified and owned. Big data is also being used for fraud detection and security event management to replace traditional SIEM systems that are difficult for IT departments to manage. While big data can process structured and unstructured data at large scales, specialized skills are required like expertise in Hadoop, data mining, and analyzing various data types.
Rodney Hite is a product manager for Big Data solutions at ViON. The document discusses the history and evolution of big data, from the earliest disk formats in the 1970s-80s that held kilobytes of data, to the present day where a variety of data sources generate huge volumes, velocities, and varieties of data. It outlines analytical techniques like semantic extraction, sentiment analysis, and predictive pattern analysis that can gain valuable insights from big data across domains like sports, security, fraud detection, and social media. The key to success is having an iterative strategy that focuses on desired results, future-proof technologies, integration, and using data scientists and engineers efficiently.
Learn why more data is collected about you than ever. How Google, Facebook, Twitter, Apple are part of the problem not the solution. Why trying to strengthen privacy laws may be too late. Get more insights from http://www.technoledge.com.au/b2b-blog
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This document provides an overview of big data concepts including definitions of big data, sources of big data, and uses of big data analytics. It discusses technologies used for big data including Hadoop, MapReduce, Hive, Mahout, MATLAB, and Revolution R. It also addresses challenges around big data such as lack of standardization and extracting meaningful insights from large datasets.
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 discusses big data, including the different units used to measure data size like bytes, kilobytes, megabytes, etc. It notes that big data is difficult to store and process using traditional tools due to its large size and complexity. Big data is growing rapidly in volume, velocity and variety. Some challenges in analyzing big data include its unstructured nature, size that exceeds capabilities of conventional tools, and need for real-time insights. Security, access control, data classification and performance impacts must be considered when protecting big data.
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
This presentation offers a basic understanding of Big Data. It does this by defining Big Data, offers a History of Big Data, Big Data by the Numbers and the 8 Laws of Big Data
Big data brings changes in thinking and business. It introduces new challenges for enterprises as well.
The document discusses the key features of big data including volume, velocity, variety and veracity. It provides examples of big data sources and applications. Benefits of big data include enabling predictions based on overall data rather than samples.
Big data is changing how businesses operate by creating new types of data-focused and technology companies. However, enterprises face challenges such as unclear big data needs, data silos, quality issues and talent shortages in deploying big data. Data security and privacy are also major considerations.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
This document discusses big data, defining it as large volumes of structured, semi-structured, and unstructured data that can be mined for information. It outlines four key characteristics of big data: volume, variety, velocity, and variability. It also discusses big data applications across various industries and provides examples of real-time big data applications. Finally, it covers challenges of conventional data systems and risks associated with big data projects.
Introduction to Big Data: Definition, Characteristic Features, Big Data Applications, Big Data vs Traditional Data, Risks of Big Data, Structure of Big Data, Challenges of Conventional Systems, Web Data, Evolution of Analytic Scalability, Evolution of Analytic Processes, Tools and methods, Analysis vs Reporting, Modern Data Analytic Tools
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
Bda assignment can also be used for BDA notes and concept understanding.Aditya205306
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy.
This document discusses how scholars can prepare for the future of big data in relation to Islamic knowledge and religious ideology. It recommends that scholars take incremental steps in the near and mid terms to focus on improving business performance through big data. It also stresses the importance of moving past pilot projects, integrating different data repositories, establishing data-driven decision making processes, and having the right people and leadership to work towards these goals.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Big data is large and complex data that cannot be processed by traditional data management tools. It is characterized by high volume, velocity, and variety. Big data comes from many sources and in many formats, including structured, unstructured, and semi-structured data. Storing and processing big data requires specialized systems like Hadoop and NoSQL databases. Big data analytics can provide benefits like improved business decisions and customer satisfaction when applied to areas such as healthcare, security, and manufacturing. However, big data also presents risks regarding privacy, costs, and being overwhelmed by the volume of data.
Companies are increasingly using big data technologies like Hadoop to store and analyze large amounts of customer data to gain insights. This raises security issues as more data is collected and needs to be properly classified and owned. Big data is also being used for fraud detection and security event management to replace traditional SIEM systems that are difficult for IT departments to manage. While big data can process structured and unstructured data at large scales, specialized skills are required like expertise in Hadoop, data mining, and analyzing various data types.
Rodney Hite is a product manager for Big Data solutions at ViON. The document discusses the history and evolution of big data, from the earliest disk formats in the 1970s-80s that held kilobytes of data, to the present day where a variety of data sources generate huge volumes, velocities, and varieties of data. It outlines analytical techniques like semantic extraction, sentiment analysis, and predictive pattern analysis that can gain valuable insights from big data across domains like sports, security, fraud detection, and social media. The key to success is having an iterative strategy that focuses on desired results, future-proof technologies, integration, and using data scientists and engineers efficiently.
Learn why more data is collected about you than ever. How Google, Facebook, Twitter, Apple are part of the problem not the solution. Why trying to strengthen privacy laws may be too late. Get more insights from http://www.technoledge.com.au/b2b-blog
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This document provides an overview of big data concepts including definitions of big data, sources of big data, and uses of big data analytics. It discusses technologies used for big data including Hadoop, MapReduce, Hive, Mahout, MATLAB, and Revolution R. It also addresses challenges around big data such as lack of standardization and extracting meaningful insights from large datasets.
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 discusses big data, including the different units used to measure data size like bytes, kilobytes, megabytes, etc. It notes that big data is difficult to store and process using traditional tools due to its large size and complexity. Big data is growing rapidly in volume, velocity and variety. Some challenges in analyzing big data include its unstructured nature, size that exceeds capabilities of conventional tools, and need for real-time insights. Security, access control, data classification and performance impacts must be considered when protecting big data.
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
This presentation offers a basic understanding of Big Data. It does this by defining Big Data, offers a History of Big Data, Big Data by the Numbers and the 8 Laws of Big Data
Big data brings changes in thinking and business. It introduces new challenges for enterprises as well.
The document discusses the key features of big data including volume, velocity, variety and veracity. It provides examples of big data sources and applications. Benefits of big data include enabling predictions based on overall data rather than samples.
Big data is changing how businesses operate by creating new types of data-focused and technology companies. However, enterprises face challenges such as unclear big data needs, data silos, quality issues and talent shortages in deploying big data. Data security and privacy are also major considerations.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
This document discusses big data, defining it as large volumes of structured, semi-structured, and unstructured data that can be mined for information. It outlines four key characteristics of big data: volume, variety, velocity, and variability. It also discusses big data applications across various industries and provides examples of real-time big data applications. Finally, it covers challenges of conventional data systems and risks associated with big data projects.
Introduction to Big Data: Definition, Characteristic Features, Big Data Applications, Big Data vs Traditional Data, Risks of Big Data, Structure of Big Data, Challenges of Conventional Systems, Web Data, Evolution of Analytic Scalability, Evolution of Analytic Processes, Tools and methods, Analysis vs Reporting, Modern Data Analytic Tools
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
Bda assignment can also be used for BDA notes and concept understanding.Aditya205306
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
This document provides an overview and guided tour of big data. It begins with defining big data as any data collection that is too large or complex to be managed with traditional data approaches. It then discusses the key characteristics of big data, including volume, variety, and velocity. The document outlines different types of big data sources and discusses both the opportunities and risks of working with big data, emphasizing the importance of understanding the data quality, representativeness, and ensuring insights provide value. It concludes by discussing the need for big data analysis to be grounded in business questions and combining appropriate statistical and analytic techniques.
How to Enable Personalized Marketing Even Before 'Big Data'DocuStar
This document discusses how companies can enable personalized marketing even without extensive big data capabilities. It defines big data and outlines the challenges it poses for marketing departments in terms of volume, velocity, and variety of data as well as costs, skills, and cross-functional collaboration needed. The document then introduces the MarketHUB+ solution as an alternative that allows personalized marketing through efficient use of field sales knowledge without the challenges of big data. It claims MarketHUB+ provides quick implementation and ROI as well as marketing services and workflow automation.
This document discusses the concept of big data. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional database techniques due to their size and complexity. It notes that big data has the characteristics of volume, variety, and velocity. The document also discusses Hadoop as an implementation of big data and how various industries are generating large amounts of data.
This document discusses data science and the challenges of big data. It notes that data science uses theories from fields like computer science, mathematics, and statistics to analyze large amounts of data. Data scientists are key to realizing opportunities in big data by finding patterns and insights to help decision makers. The document outlines some example courses that could be part of a data science concentration, including mathematics, statistics, programming, data mining, and machine learning.
This document provides information about big data analytics. It defines what data and big data are, explaining that big data refers to extremely large data sets that are difficult to process using traditional data management tools. It discusses the volume, variety, velocity, and veracity characteristics of big data. Examples of big data sources and sizes are provided, such as the terabytes of data generated each day by the New York Stock Exchange and Facebook. The document also covers structured, unstructured, and semi-structured data types; advantages of big data processing; and types of digital advertising.
Big data refers to datasets that are too large to be captured, stored, managed and analyzed by traditional database software tools. As technology advances, the size of datasets considered big data will also increase. Big data comes from a variety of sources like social networks, sensors, and transactional systems and is growing rapidly. Analyzing big data using technologies like MapReduce and NoSQL databases can provide valuable insights in applications such as recommendations, medical research, and fraud detection. However, big data also raises issues around privacy, access, and long-term preservation and usability of the data.
Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries J...Kevin Pledge
Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
This document provides an introduction to data science, including:
- The large amounts of data being collected from sources like the web, financial transactions, and social networks.
- How "big data" refers to data that is expensive to manage and hard to extract value from due to its volume, velocity, and variety.
- What data scientists do, such as finding patterns and stories in data to help decision makers, and how data science draws from fields like computer science, mathematics, and statistics.
Top 10 Digital Marketing Institute in lucknow.pptxzaireendigitech
Welcome to our ppt on the top 10 digital marketing institutes in Lucknow! If you're looking to enhance your skills in the dynamic field of digital marketing, Lucknow offers several excellent training options. Our curated list highlights the best digital marketing institutes in Lucknow, providing comprehensive courses that cover SEO, social media marketing, PPC, content marketing, and more. These institutes are renowned for their experienced faculty, practical training, and industry-relevant curriculum. Whether you're a beginner or a professional seeking to upgrade your skills, these institutes can help you achieve your career goals in digital marketing.
What is Digital Marketing: A Comprehensive GuideV-tech Marketing
Digital technologies have transformed marketing. Traditional methods like print and TV ads are giving way to digital strategies, reshaping how brands connect with consumers online. Welcome to the era of digital marketing, where engagement in the digital realm is key. Let's delve into what digital marketing entails in our interconnected world.
Digital Marketing Company in India - DIGI BrooksDIGI Brooks
This infographic provides guidance on marketing analytics, helping businesses grow using tools like Google Analytics and AI, measuring ROI, and analysing future trends to track business development.
https://digibrooks.com/digital-marketing-services/
Why bridging the gap between PR and SEO is the only way forward for PR Profes...Isa Lavs
The lines between PR and SEO are blurring. SEOs are increasingly winning PR briefs by leveraging data and content to secure high-value placements. In this presentation, I explore the merging of PR and SEO, highlighting why SEO specialists are increasingly taking ‘PR’ business. I uncover the hidden SEO potential using PR tactics and discuss how to identify missed opportunities. I'll also offer insights into strategies for converting PR initiatives into successful link-building campaigns.
Embark on style journeys Indian clothing store denver guide.pptxOmnama Fashions
Finding the perfect "Indian Clothing Store Denver" is essential for those seeking vibrant, authentic, and culturally rich attire in the heart of Colorado. Denver, a city known for its diverse culture and eclectic fashion scene, offers a variety of options for those in search of traditional and contemporary Indian clothing. Whether you're preparing for a wedding, festival, or cultural event, or simply wish to incorporate the elegance and beauty of Indian fashion into your wardrobe, discovering the right store can make all the difference.
What Software is Used in Marketing in 2024.Ishaaq6
This paper explores the diverse landscape of marketing software, examining its pivotal role in modern marketing strategies. It provides a comprehensive overview of various types of marketing software tools and platforms essential for enhancing efficiency, optimizing campaigns, and achieving business objectives. Key categories discussed include email marketing software, social media management tools, content management systems (CMS), customer relationship management (CRM) software, search engine optimization (SEO) tools, and marketing automation platforms.
The paper delves into the functionalities, benefits, and examples of each type of software, highlighting their unique contributions to effective marketing practices. It explores the importance of integration and automation in maximizing the impact of these tools, addressing challenges and strategies for seamless implementation across different marketing channels.
Furthermore, the paper examines emerging trends in marketing software, such as AI and machine learning applications, personalization strategies, predictive analytics, and the ethical considerations surrounding data privacy and consumer rights. Case studies illustrate real-world applications and success stories of businesses leveraging marketing software to achieve significant outcomes in their marketing campaigns.
In conclusion, this paper provides valuable insights into the evolving landscape of marketing technology, emphasizing the transformative potential of software solutions in driving innovation, efficiency, and competitive advantage in today's dynamic marketplace.
This description outlines the scope, structure, and focus of the paper, giving readers a clear understanding of what to expect and why the topic of marketing software is important and relevant in contemporary marketing practices.
Boost Your Instagram Views Instantly Proven Free Strategies.pptxInstBlast Marketing
Join Performance Car Exclusive to drive the finest supercars, engineered with advanced materials and cutting-edge technology for peak performance.
https://instblast.com/instagram/free-instagram-views
Title: Making Money the Easy Way: A Quick Guide to Generating IncomeWilliamZinsmeister
Welcome to "Making Money the Easy Way: A Quick Guide to Generating Income." This book is designed to provide you with practical, actionable strategies to generate income with minimal effort. Whether you’re looking to supplement your current income or create a full-time revenue stream, this guide covers a variety of methods to help you achieve your financial goals. We will explore opportunities available online, various investment strategies, profitable side hustles, creative approaches, and essential financial tips to ensure sustainable income growth.
Compitive analysis on Noise pvt Ltd.pptxSauravDey45
ChatGPT
Competitive Analysis: Noise Smartwatch
Overview
Noise is an Indian electronics brand that primarily manufactures smartwatches, wireless earphones, and other electronic accessories. Noise smartwatches have gained significant popularity due to their affordable pricing, feature-rich offerings, and stylish designs. The competitive landscape for Noise smartwatches includes both local and international brands that cater to various market segments. This analysis will focus on key competitors, market positioning, product features, pricing strategies, and consumer preferences.
Key Competitors
Amazfit (Huami):
Strengths: Known for excellent battery life, robust fitness tracking, and premium build quality.
Weaknesses: Slightly higher price points compared to Noise.
Products: Amazfit Bip U, Amazfit GTS series.
Realme:
Strengths: Strong brand presence, integration with Realme smartphones, and aggressive pricing.
Weaknesses: Limited variety in smartwatch models.
Products: Realme Watch, Realme Watch S.
Boat:
Strengths: Competitive pricing, appealing designs, and extensive marketing.
Weaknesses: Relatively new to the smartwatch market, which may affect consumer trust.
Products: Boat Storm, Boat Flash.
Samsung:
Strengths: High brand credibility, advanced features, and premium design.
Weaknesses: Higher price points make it less accessible to budget-conscious consumers.
Products: Galaxy Watch Active 2, Galaxy Watch 3.
Xiaomi:
Strengths: Strong ecosystem integration, affordable pricing, and extensive features.
Weaknesses: Less focus on premium design compared to some competitors.
Products: Mi Band series, Mi Watch.
Market Positioning
Noise positions itself as an affordable yet feature-rich alternative in the smartwatch market. Its target demographic includes budget-conscious consumers and fitness enthusiasts who seek value for money without compromising on essential features like fitness tracking, notifications, and battery life. Noise leverages its strong online presence and partnerships with e-commerce platforms to reach its audience effectively.
Product Features Comparison
Noise Smartwatches:
Key Features: Heart rate monitoring, SpO2 tracking, multiple sports modes, customizable watch faces, notifications, and music control.
Battery Life: Typically lasts 7-10 days on a single charge.
Build Quality: Focus on lightweight and comfortable designs with water-resistant capabilities.
Amazfit Smartwatches:
Key Features: Advanced fitness tracking, GPS, AMOLED displays, and long battery life (up to 20 days).
Battery Life: 10-20 days depending on the model.
Build Quality: Premium materials and durable designs.
Realme Smartwatches:
Key Features: Basic fitness tracking, SpO2 monitoring, and notifications.
Battery Life: Up to 9 days.
Build Quality: Sleek designs but slightly limited in variety.
Boat Smartwatches:
Key Features: Heart rate monitoring, multiple sports modes, and customizable watch faces.
Advertising and Promotion of whisper by Sakthi Sundarsakthisundar2001
This presentation is an invaluable resource for marketing professionals, students, and anyone interested in understanding the dynamics of effective advertising and promotion in the feminine hygiene sector. Explore how Whisper maintains its brand leadership and continues to innovate in a competitive market.
Advanced Storytelling Concepts for MarketersEd Shimp
Every marketer knows you’re supposed to tell a story, but do you know how to tell a story? Do you know why you’re supposed to tell a story? Do you even truly know what a story is? While many marketing presentations emphasize the value of mythic storytelling, the nuts and bolts of actually constructing a story are never explored.
The goal of marketing may be to achieve specific KPIs that drive sales, which is very objective, but the top of the marketing funnel requires a softer approach. In our data-driven results-oriented fast-paced world, marketers must quantify results, but those results will never be achieved unless prospects are first approached with humanity.
There is a common misunderstanding that the so-called “soft skills” of marketing such as language and art are unmeasurable and subjective, but while the objective measures of market research are merely 100 years old, the rules of aesthetics have been perfected over the last 2,500 years.
Great story construction is a skill that requires significant knowledge and practice. This presentation will be a review of the ancient art of story construction.
We will discuss:
• Rhetoric – The art of effective communication
• The Socratic Method – You cannot teach, but you can persuade people to learn
• Plato’s Cave – You sell products, but you market ideas
• Aristotle’s Six Dramatic Elements – The secret recipe for marketing stories
This is for senior marketers who are tasked with creating effective narratives or guiding others in the process. By the end of the session, attendees will have gained the knowledge needed to work storytelling into all phases of the buyer’s journey.
2024 Trend Updates: What Really Works In SEO & Content MarketingSearch Engine Journal
The future of SEO is trending toward a more human-first and user-centric approach, powered by AI intelligence and collaboration. Are you ready?
Watch as we explore which SEO trends to prioritize to achieve sustainable growth and deliver reliable results. We’ll dive into best practices to adapt your strategy around industry-wide disruptions like SGE, how to navigate the top challenges SEO professionals are facing, and proven tactics for prioritizing quality and building trust.
You’ll hear:
- The top SEO trends to prioritize in 2024 to achieve long-term success.
- Predictions for SGE’s impact, and how to adapt.
- What E-E-A-T really means, and how to implement it holistically (hint: it’s never been more important).
With Zack Kadish and Alex Carchietta, we’ll show you which SEO trends to ignore and which to focus on, along with the solution to overcoming rapid, significant and disruptive Google algorithm updates.
If you’re looking to cut through the noise of constant SEO and content trends to drive success, you won’t want to miss this webinar.
How to Start Affiliate Marketing with ChatGPT- A Step-by-Step Guide (1).pdfSimpleMoneyMaker
Discover the power of affiliate marketing with ChatGPT! This comprehensive guide takes you through the process of starting and scaling your affiliate marketing business using the latest AI technology. Learn how to leverage ChatGPT to generate content ideas, create engaging articles, and connect with your audience through personalized interactions. From building your strategy and optimizing conversions to analyzing performance and staying updated with industry trends, this eBook provides everything you need to know to succeed in affiliate marketing. Whether you're a beginner looking to start your online business or an experienced marketer wanting to take your efforts to the next level, this guide is your roadmap to success in the world of affiliate marketing.
2. Textbook Definitions
• “extremely large data sets that may be analysed computationally to reveal
patterns, trends, and associations, especially relating to human behaviour
and interactions.”
• “Big data is a broad term for data sets so large or complex that traditional
data processing applications are inadequate. Challenges include analysis,
capture, data curation, search, sharing, storage, transfer, visualization, and
information privacy. The term often refers simply to the use of predictive
analytics or other certain advanced methods to extract value from data,
and seldom to a particular size of data set.”
3. In Simple Terms
• Big data is literally just a lot of data. While it's more of a marketing term
than anything, the implication is usually that you have so much data that
you can't analyze all of the data at once because the amount of memory
(RAM) it would take to hold the data in memory to process and analyze it
is greater than the amount of available memory.
4. An Example of Big Data
• Let’s say Facebook wants to know which adverts work best for people with
degrees. Let's say there are 200,000,000 Facebook users with degrees, and
they have been each served 100 ads. That's 20,000,000,000 events of
interest, and each "event" (an ad being served) contains several data
points (features) about the ad: what was the ad for? Did it have a picture
in it? Was there a man or woman in the ad? How big was the ad? What
was the most prominent color? Let's say for each ad there are 50
"features". This means you have 1,000,000,000,000 (one trillion) pieces of
data to sort through. If each "piece" of data was only 100 bytes, you'd
have about 93 GB of data to parse. That's pretty big but we’re still only on
the brink of "big data" territory, it gets much much bigger!