Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Everybody has heard of Big Data, and its promise as the next great frontier for innovation. However, Big Data is neither new nor easily defined. What are the key drivers that make Big Data so critically important today? What is the single idea behind Big Data that promises such game changing outcomes for capable organizations? Who are the skilled talent that deliver Big Data results?
This presentation briefly reviews the opportunities, motivation and trends that are driving Big Data disruption. Data science is introduced as the enabling engine for Big Data transformation via the creation of new Data Products. The data scientist is defined and his tools, workflow and challenges are reviewed. Finally, practical tips are presented for approaching data product development.
Key takeaways include:
- Big Data disruption is driven by four megatrends
- Data is the essential raw material for creating valuable Data Products
- Data scientists are heterogeneous by role & skill set, but share common tools, workflows and challenges
- Data science talent is more important than raw data for Big Data success
These slides are modified from an invited presentation for the Gwinnett Chamber of Commerce on March 18, 2014. An excerpt was presented at the Georgia Pacific Social Media Working Session on March 19, 2014.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Everybody has heard of Big Data, and its promise as the next great frontier for innovation. However, Big Data is neither new nor easily defined. What are the key drivers that make Big Data so critically important today? What is the single idea behind Big Data that promises such game changing outcomes for capable organizations? Who are the skilled talent that deliver Big Data results?
This presentation briefly reviews the opportunities, motivation and trends that are driving Big Data disruption. Data science is introduced as the enabling engine for Big Data transformation via the creation of new Data Products. The data scientist is defined and his tools, workflow and challenges are reviewed. Finally, practical tips are presented for approaching data product development.
Key takeaways include:
- Big Data disruption is driven by four megatrends
- Data is the essential raw material for creating valuable Data Products
- Data scientists are heterogeneous by role & skill set, but share common tools, workflows and challenges
- Data science talent is more important than raw data for Big Data success
These slides are modified from an invited presentation for the Gwinnett Chamber of Commerce on March 18, 2014. An excerpt was presented at the Georgia Pacific Social Media Working Session on March 19, 2014.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Big Data Information Architecture PowerPoint Presentation SlideSlideTeam
Feel enthralled by all the attention by our Big data information architecture PowerPoint presentation slide offers. While designing the perfect framework for a durable system, it could get tricky to represent all the data in a systematic manner. Manifesting complex ideas in a simplified manner doesn't always comes handy. That's the reason we have well-researched formats and designs for professional and prolonging solutions. Our team of experts makes sure that all the PPT slides are framed to work for the best of the client. Numerous icons and images are used here for visual engagement. We have covered up every viewpoint of data structure possible, including, data market forecast, financial aspects, social media approach and different comparisons used in data analysis for an out of box view. Our sole and intriguing PowerPoint slides are your gateway to progress and serves you in holding your viewer's consideration towards the concept of discernment and improves the quality and accuracy of the business processes. Discourage injudicious comments with our Big Data Information Architecture PowerPoint Presentation Slide. Ensure folks adhere to the decorum.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
The new age big data technologies include predictive analytics, no SQL databases, search and knowledge discovery, stream analytics, in-memory data fabric, data virtualization and more.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Big Data Career Path | Big Data Learning Path | Hadoop Tutorial | EdurekaEdureka!
This Hadoop tutorial on Big Data Career Path and Learning Path ( Why Big Data Career blog: https://goo.gl/Hx1hbk ) will tell you why Big Data analytics is the best career move. Learn about various job roles, salary trends and learning paths in Big Data domain. Below are the topics covered in this Big Data Career Path and Learning Path Tutorial:
1) Big Data Domains
2) Big Data Job Roles and Trends
3) Big Data Salary Trends
4) Big Data Career Path
5) Big Data Learning Path
6) Edureka Big Data Certification Courses
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#BigDataCareer #HadoopCareer #BigDataLearningPath
Big Data Information Architecture PowerPoint Presentation SlideSlideTeam
Feel enthralled by all the attention by our Big data information architecture PowerPoint presentation slide offers. While designing the perfect framework for a durable system, it could get tricky to represent all the data in a systematic manner. Manifesting complex ideas in a simplified manner doesn't always comes handy. That's the reason we have well-researched formats and designs for professional and prolonging solutions. Our team of experts makes sure that all the PPT slides are framed to work for the best of the client. Numerous icons and images are used here for visual engagement. We have covered up every viewpoint of data structure possible, including, data market forecast, financial aspects, social media approach and different comparisons used in data analysis for an out of box view. Our sole and intriguing PowerPoint slides are your gateway to progress and serves you in holding your viewer's consideration towards the concept of discernment and improves the quality and accuracy of the business processes. Discourage injudicious comments with our Big Data Information Architecture PowerPoint Presentation Slide. Ensure folks adhere to the decorum.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
The new age big data technologies include predictive analytics, no SQL databases, search and knowledge discovery, stream analytics, in-memory data fabric, data virtualization and more.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Big Data Career Path | Big Data Learning Path | Hadoop Tutorial | EdurekaEdureka!
This Hadoop tutorial on Big Data Career Path and Learning Path ( Why Big Data Career blog: https://goo.gl/Hx1hbk ) will tell you why Big Data analytics is the best career move. Learn about various job roles, salary trends and learning paths in Big Data domain. Below are the topics covered in this Big Data Career Path and Learning Path Tutorial:
1) Big Data Domains
2) Big Data Job Roles and Trends
3) Big Data Salary Trends
4) Big Data Career Path
5) Big Data Learning Path
6) Edureka Big Data Certification Courses
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#BigDataCareer #HadoopCareer #BigDataLearningPath
Big data & data science challenges and opportunitiesJose Quesada
Even when most companies see the advantages of using more data in their decisions, few actually do. Why is that? A few ideas on challenges and opportunities for (middle-size) companies. Talk audience was an engineering association, where most people represented engineering-centric companies in Germany (often in manufacturing).
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Fully embracing a BI tool can mean the difference between the full payoff of your data analytics and returns that are just so-so. Learn how to avoid BI pitfalls and boost BI adoption to become a truly data driven organisation.
"Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about...Kai Wähner
I discuss a good big data architecture which includes Data Warehouse / Business Intelligence + Apache Hadoop + Real Time / Stream Processing. Several real world example are shown. TIBCO offers some very nice products for realizing these use cases, e.g. Spotfire (Business Intelligence / BI), StreamBase (Stream Processing), BusinessEvents (Complex Event Processing / CEP) and BusinessWorks (Integration / ESB). TIBCO is also ready for Hadoop by offering connectors and plugins for many important Hadoop frameworks / interfaces such as HDFS, Pig, Hive, Impala, Apache Flume and more.
What does a data scientist actually do? Here at Good Rebels we wanted to outline a profile of this new profession, with the help of various industry leaders from academia, business and institutions. In short, we concluded that the main tasks of a data scientist are to identify data, transform it when incomplete, categorize it, prepare it for analysis, perform the analysis, visualize the results and communicate them.
Big Data v. Small data - Rules to thumb for 2015Visart
Open data, big data, small data - what's the difference? Do you work with data? Small and medium sized businesses are pressured to transform traditional practices into data-driven models. In this presentation, CEO, Ugur Kadakal explains the big data v. small data and the insights we can pull from each for better business intelligence.
Do you work with data, or just like learning about it? Check out our blog on www.Visart.io for data stories and other resources.
BigData & Supply Chain: A "Small" IntroductionIvan Gruer
In the frame of the master in logistic LOG2020, a brief presentation about BigData and its impacts on Supply Chains at IUAV.
Topics and contents have been developed along the research for the MBA final dissertation at MIB School of Management.
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
Monday was another great conference by MinneAnalytics! #MinneFRAMA was a great success with over 1,100 attendees at Science Museum of Minnesota. Alison Rempel Brown is a great host! A Teradata colleague told me that her post about my presentation "blew up" with hits and she got over 2K views, and 60+ likes. I'm proud to be a part of this great #datascience organization brining #machinelearning and #artificialintelligence #analytics to our #bigdata clients. If you want my slides, here they are.
Data Scientist Job Hotspots - Top Cities For Career Advancement And Innovatio...DataSpace Academy
With businesses worldwide fast inclining towards data-driven decision-making, there is a rising demand for data scientists. Employers are also ready to pay high remuneration and great perks for skilled data scientists. But what are the best places in the country and the world for data professionals? The blog here offers a glimpse of the best cities in India and across the world for building a proliferating and high-paying career in data science. Along with the job hotspots, you will also get an idea about the average pay package available in these cities.
Have you ever get overwhelmed with the buzzwords like Business Intelligence, Big Data, Business Analytics, Data Warehouse, Data Mining, Data Visualization, Decision Support Systems, and Expert Systems? This presentation gives you a brief enlightening introduction and outlines how these technologies can help organizations earn competitive advantage.
Furthermore, the presentation explains the some important BI tools' adoption and return on investment considerations.
Urban big data are increasingly being used in urban GIS research. This presentation, developed for an introductory GIS course, introduces the seven V's of big data: volume, variety, velocity, veracity, value, visualization, and variability. Major sources of urban big data include sensor systems, user-generated content, administrative systems, private sector data, humanities data, and hybrid data. These categories and the seven V's are illustrated with six examples of cutting-edge urban big data research: the Array of Things sensing network, Austin bike data, analysis of London's transport network, scraping Craigslist to analyze rental housing, the Mapping Inequality Project, and the EPA Smart Locations Database. The presentation concludes with a discussion of critiques surrounding the use of big data.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
1. Fickr: Nikos Koutoulas
Big Data & Career Paths
Marcos Colebrook
Univ. de La Laguna
@MColebrook
ETS Ingeniería Informática – 16.06.2014#BigDataCanarias
2. Contents
Big Data facts
Definition of Big Data
Techs & Tools
Data Science: skills and career
paths
Conclusions
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 2
7. Big Data in Facebook
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 7
8. Google trends on Big Data
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 8
Hadoop
Big Data
Data
Analytics
Massive Data
9. Father to the ‘Big Data’ term
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 9
Source: S. Lohr (2013), The Origins of ‘Big Data’: An Etymological Detective Story.
John R. Mashey
Chief Scientist at Silicon Graphics
10. Big Data: think-tank Policy Exchange
Big Data: datasets that are too
awkward to work with using traditional,
hands-on database management tools.
Big Data Analytics: the process of
examining and interrogating big data
assets to derive insights of value for
decision making.
16.06.2014 10#BigDataCanarias: "Big Data & Career Paths"
Source: C. Yiu (2012), The Big Data Opportunity.
11. What is Big Data?
Big Data is a term that describes
large volumes of high velocity,
complex and variable data that
require advanced techniques and
technologies to enable the capture,
storage, distribution, management,
and analysis of the information.
16.06.2014 11#BigDataCanarias: "Big Data & Career Paths"
Source: Demystifying Big Data (2012), TechAmerica Foundation.
12. Big Data
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 12
Source: J. Bloem et al. (2012), VINT Research Report 1: Creating Clarity with Big Data.
13. Sources & types of data
16.06.2014 #BigDataCanarias: "Big Data & Career Paths" 13
Source: Big Data, BBVA Innovation Edge 2013 (from Booz & Company “Benefitting from Big Data: Leveraging Unstructured
Data Capabilities for Competitive Advantage”)
14. Big Data sources
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Source: M. Schroeck et al. (2012), Analytics: The Real-World Use of Big Data.
15. The three Vs of Big Data
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Source: D. Soubra (2012), The 3Vs that define Big Data.
16. The other “Vs” in Big Data
“ ’Vs’ like veracity,
validity, value,
viability, etc. are
aspirational qualities
of all data, not
definitional qualities of
Big Data.”
Doug Laney
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Source: D. Laney (2013), Batman on Big Data.
17. What is really important in Big Data?
“The Big in Big Data relates to
importance not size”
Rafael Irizarry
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Source: R. Irizarry (2014), The Big in Big Data relates to importance not size.
19. Is Big Data a marketing campaign?
“If you’re like me, the mere mention of Big Data now
turns your stomach.
Nearly every business intelligence (BI) vendor,
publication, and event has Big Data flashing in neon
colors in Times Square dimensions.
Never before have I seen an idea in the BI space elicit
this much obsession. Why all the fuss? Why, indeed.
Essentially, Big Data is a marketing campaign, pure
and simple.”
Stephen Few
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20. Gartner's 2013 Hype Cycle
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Source: Gartner's 2013 Hype Cycle for Emerging Technologies
21. Big Data: McKinsey Report
140.000 – 190.000 more deep analytical talent positions,
and 1.5 million data savvy managers needed to take full
advantage of Big Data in the USA.
Techniques: data mining (cluster analysis, classification,
regression, etc), (un)supervised learning, ML, neural
networks, optimization, predictive modeling, statistics,
simulation, etc.
Technologies: BI, Cassandra, DW, ETL, Hadoop, HBase,
Map/Reduce, R, RDBMS, etc.
Potential of Big Data in five domains:
Healthcare
Public Sector
Retail
Manufacturing
Telecommunications.
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Source: J. Manyika, et al. (2012), Big Data: The Next Frontier for Innovation, Competition and Productivity.
27. Salary vs. Data Tools
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Source: J. King, R. Magoulas (2013), Data Science Salary Survey.
28. Median Salary vs. #Tools
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Source: J. King, R. Magoulas (2013), Data Science Salary Survey.
30. Data Role vs. Data Skills
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Source: H.D. Harris et al. (2013), Analyzing the Analyzers
31. Big Data capabilities
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Source: M. Schroeck et al. (2012), Analytics: The Real-World Use of Big Dat.
32. Market & jobs opportunity
The demand for Big Data services spending
projected to reach $132,300M in 2015.
By 2015, Big Data demand will reach 4.4 million
jobs globally, but only one-third of those jobs will
be filled.
The demand for services will generate 550,000
external services jobs in the next 3 years.
Another 40,000 jobs will be created at software
vendors in the next 3 years.
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Source: Big Data, BBVA Innovation Edge 2013 (from Gartner’s “Top Technology Predictions for 2013 and Beyond”)
33. Statiscian: a sexy job
“I keep saying the sexy job in the next ten years will be
statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the
1990s?
The ability to take data—to be able to understand it, to
process it, to extract value from it, to visualize it, to
communicate it—that’s going to be a hugely
important skill in the next decades [...]”
Hal Varian
Google’s Chief Economist
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Source: Hal Varian on how the Web challenges managers, McKinsey & Co. 2009.
35. Data Science Venn Diagram
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Source: Drew Conway (2010).
36. Data Scientist skill set: ACM
A data scientist requires an integrated
skill set spanning mathematics,
machine learning, artificial
intelligence, statistics, databases, and
optimization, along with a deep
understanding of the craft of problem
formulation to engineer effective
solutions.
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Source: V. Dhar (2013), Data Science and Prediction, Comm. of the ACM.
37. Intelligence over DIKW
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Source: The Internet of Things 2010 at YouTube (1:40).
38. Data→Info→Knowledge→Understanding
→Wisdom!!
“There are known knowns.
These are things we know that
we know.
There are known unknowns.
That is to say, there are things
that we know we don't know.
But there are also unknown
unknowns. There are things we
don't know we don't know.”
Donald Rumsfeld
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Source: C. Somohano (2013), Big Data [sorry] & Data Science: What Does a Data Scientist Do?
39. BI vs. Data Discovery
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Source: J. Kolb (2010), The New Reality for Business Intelligence and Big Data.
40. Data Science Teams
Data scientists as having the following qualities:
Technical expertise: the best data scientists typically have
deep expertise in some scientific discipline.
Curiosity: a desire to go beneath the surface and discover
and distill a problem down into a very clear set of
hypotheses that can be tested.
Storytelling: the ability to use data to tell a story and to be
able to communicate it effectively.
Cleverness: the ability to look at a problem in different,
creative ways.
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Source: D.J. Patil (2011), Building Data Science Team.
41. Data Science skills: Accenture
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Source: J.G. Harris et al. (2013), The Team Solution to the Data Scientist Shortage.
42. Insight Data Science Fellow Program
6 week, full-time, postdoctoral
data science training fellowship
in Silicon Valley or New York City.
Self-directed, project-based
learning (no classes!).
Software Engineering Best
Practices: Python, Git, Flask,
Javascript.
Storing and Retrieving Data:
MySQL, Hadoop, Hive.
Statistical Analysis & Machine
Learning: NumPy & SciPy,
Pandas, scikit-learn, R.
Visualizing and
Communicating Results: D3
Javascript library, visualization
and presentation best practices.
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43. Insight Data Engineering Fellow
Program
6 week, full-time,
professional data
engineering training
fellowship in Silicon Valley,
California.
Self-directed, project-based
learning (no classes!).
Big Data Infrastructure.
Extracting data.
Transforming data.
Loading / Storing data.
Building visualizations
and dashboards.
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44. Conclusions
Big Data is still an emerging topic that gathers a lot of new
technologies, and needs some time to mature.
But, on the other hand, it has a true market opportunity.
Data Science / Engineering skills to acquire:
Math/Statistics and business knowledge.
Technical expertise: R, Python, Hadoop, Spark/Storm, D3,
Java/Javascript, ...
Curiosity and cleverness.
Storytelling: ability to communicate results.
Trends:
Data Visualization
Predictive Modelling
Social Analytics
Data Mining / Machine Learning
Forensic Computer Science
Spark / Storm vs. Hadoop MapReduce
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45. References (1/3)
1. Big Data (2013), BBVA Innovation Edge (31 pp).
2. Demystifying Big Data: A Practical Guide To Transforming The Business
of Government (2012), TechAmerica Foundation (40 pp).
3. Gartner's 2013 Hype Cycle for Emerging Technologies Maps Out
Evolving Relationship Between Humans and Machines (2013), Gartner.
4. Hal Varian on How the Web Challenges Managers (2009), McKinsey &
Co.
5. Insight Data Engineering Fellows Program (2014).
6. Insight Data Science Fellows Program (2014).
7. The Internet of Things (2010), IBM Social Media.
8. What Happens In An Internet Minute? (2014), Intel.
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46. References (2/3)
9. J. Bloem, M. van Doorn, S. Duivestein, T. van Manen, E. van Ommeren (2012), VINT Research Report
1: Creating Clarity with Big Data, SOGETI.
10. D. Conway (2010), The Data Science Venn Diagram.
11. M. Deutscher, When Will the World Reach 8 Zetabytes of Stored Data? (2012), Silicon Angle (blog).
12. V. Dhar (2013), Data Science and Prediction, Communications of the ACM 56 (12), pp. 64-73.
13. S. Few (2012), Big Data, Big Ruse, Perceptual Edge - Visual Business Intelligence Newsletter (blog,
8 pp).
14. H.D. Harris, S.P. Murphy, M. Vaisman (2013), Analyzing the Analyzers, O’Reilly Media (40 pp).
15. J.G. Harris, N. Shetterley, A.E. Alter, K. Schnell (2013), The Team Solution to the Data Scientist
Shortage, Accenture Institute for High Performance.
16. R. Irizarry (2014), The Big in Big Data Relates to Importance Not Size, Simply Statistics (blog).
17. J. King, R. Magoulas (2013), Data Science Salary Survey, O’Reilly Media (23 pp).
18. J. Kelly (2013), Hadoop-NoSQL Software and Services Market Forecast 2012-2017, Wikibon (blog).
19. J. Kolb (2010), The New Reality for Business Intelligence and Big Data, Applied Data Labs (blog).
20. D. Laney (2013), Batman on Big Data, Gartner.
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47. References (3/3)
21. D. Laney (2013), Batman on Big Data, Gartner.
22. S. Lohr (2013), The Origins of ‘Big Data’: An Etymological Detective Story, The New York Times.
23. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A.H. Byers (2012), Big Data: The Next
Frontier for Innovation, Competition and Productivity, McKinsey Global Institute (156 pp).
24. R. Nair, A. Narayanan (2012), Benefitting from Big Data: Leveraging Unstructured Data Capabilities
for Competitive Advantage, Booz & Company (16 pp).
25. D.J. Patil (2011), Building Data Science Teams, O’Reilly Media (26 pp).
26. G. Piatetsky (2014), Big Data Landscape v3.0 Analyzed, KDnuggets (blog).
27. J. Podesta, P. Pritzker, E.J. Moniz, J. Holdren, J. Zients (2014), Big Data: Seizing Opportunities,
Preserving Values, The White House (79 pp).
28. M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, P. Tufano (2012), Analytics: The Real-World
Use of Big Data, IBM Global Services.
29. C. Somohano (2013), Big Data [sorry] & Data Science: What Does a Data Scientist Do?, Data Science
London (55 pp).
30. D. Soubra (2012), The 3Vs that define Big Data, Data Science Central (blog).
31. C. Yiu, The Big Data Opportunity (2012), Policy Exchange (36 pp).
32. P. Zikopoulos, C. Eaton, D. deRoos, T. Deutsch, G. Lapis (2012), Understanding Big Data, McGraw-Hill.
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48. Datos de contacto y cuestiones
¡¡Gracias!!
¿Preguntas?
Datos de contacto:
Marcos Colebrook
Email: mcolesan@ull.edu.es
Twitter: @MColebrook
SlideShare: www.slideshare.net/MarcosColebrookSantamaria
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