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How to use Big Data in Healthcare
 
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www.siemens.com/big-data-in-healthcare A major trend will influence the way healthcare will be delivered around the world in the coming years: Big Data. This video points out the opportunities in making the data usable which could transform approaches in healthcare that have long defined the industry. Siemens Website: http://www.siemens.com/entry/cc/en?ytref=j88yrcW5NPM Siemens Press: http://www.siemens.com/press/en?ytref=j88yrcW5NPM Siemens Blogs: https://blogs.siemens.com?ytref=j88yrcW5NPM Siemens on LinkedIn: https://www.linkedin.com/company/siemens Siemens on Facebook: https://www.facebook.com/Siemens Siemens on Twitter: https://twitter.com/Siemens Siemens on Google+: https://plus.google.com/+Siemens/posts
Views: 8203 Siemens
What is CHIEF DATA OFFICER? What does CHIEF DATA OFFICER mean? CHIEF DATA OFFICER meaning
 
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What is CHIEF DATA OFFICER? What does CHIEF DATA OFFICER mean? CHIEF DATA OFFICER meaning - CHIEF DATA OFFICER definition - CHIEF DATA OFFICER explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A chief data officer (CDO) is a corporate officer responsible for enterprise wide governance and utilization of information as an asset, via data processing, analysis, data mining, information trading and other means. CDOs report mainly to the chief executive officer (CEO). Depending on the area of expertise this can vary. CDO is a member of the executive management team and manager of enterprise-wide data processing & data mining. The Chief Data Officer title shares its acronym with the Chief Digital Officer but the two are not the same job. The Chief Data Officer has a significant measure of business responsibility for determining what kinds of information the enterprise will choose to capture, retain and exploit and for what purposes. However, the similar-sounding Chief Digital Officer or Chief Digital Information Officer often does not bear that business responsibility, but rather is responsible for the information systems through which data is stored and processed. The role of manager for data processing was not elevated to that of senior management prior to the 1980s. As organizations have recognized the importance of information technology as well as business intelligence, data integration, master data management and data processing to the fundamental functioning of everyday business, this role has become more visible and crucial. This role includes defining strategic priorities for the company in the area of data systems and opportunities, identifying new business opportunities pertaining to data, optimizing revenue generation through data, and generally representing data as a strategic business asset at the executive table. With the rise in service-oriented architectures (SOA), large-scale system integration, and heterogeneous data storage/exchange mechanisms (databases, XML, EDI, etc.), it is necessary to have a high-level individual, who possesses a combination of business knowledge, technical skills, and people skills, guide data strategy. Besides the revenue opportunities, acquisition strategy, and customer data policies, the chief data officer is charged with explaining the strategic value of data and its important role as a business asset and revenue driver to executives, employees, and customers. This contrasts with the older view of data systems as mere back-end IT systems. More recently, with the adoption of data science the Chief Data Officer is sometimes looked upon as the key strategy person either reporting to the Chief Strategy Officer or serving the role of CSO in lieu of one. This person has the responsibility of measurement along various business lines and consequently defining the strategy for the next growth opportunities, product offerings, markets to pursue, competitors to look at etc. This is seen in organizations like Chartis, AllState and Fidelity.
Views: 1585 The Audiopedia
Data Mining & Machine Learning to empower business strategy
 
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This talk will focus on my recent work for the Acquisition and Retention team at MSN, in which we address the business problem of successfully predicting customer behavior far enough ahead that the predictions are actionable.  The goals of this work were; to impact churn, by allowing intervention to retain valuable customers; to allow the monetization of customers leaving the MSN dialup service for Broadband, by targeting high upgrade propensity customers to partners, and focusing those living outside areas with a partner relationship on MSN’s BYOA (Premium) product. Using the SQL Server 2005 data mining tools, we developed and deployed a set of 4 predictive models on MSN’s database of nearly 5 million active subscribers, around which MSN’s Acquisition & Retention team now builds business strategy. During the course of this work, we uncovered a simple abstraction of the subscription relationship that can be applied to any contract-free subscription business, in particular the post-number-portability wireless industry.
Views: 27 Microsoft Research
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 238 Wafeek Wahby
Healthcare Analytics, Health Informatics and Bioinformatics
 
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As you begin to look into healthcare analytics, you may find yourself searching for definitions and differences between health informatics, bioinformatics and healthcare analytics. It’s not hard to see why, as the three disciplines work in similar areas of the health, computing and IT space. But, in fact, they are very different and have uniquely defined roles that complement one another.
Views: 6583 Science
Designing a dashboard how to visualise and feedback Electronic Health Record EHR data for clinical s
 
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2018 Innovations in Cancer Treatment and Care Conference - hosted by Cancer Institute NSW. More information: https://www.cancer.nsw.gov.au/2018-innovations-conference
Views: 55 cancerNSW
Data Science: The Next Global Revolution | René Vidal | TEDxJHU
 
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René Vidal, a world leader in computer vision, machine learning, and medical image analysis, is a professor in the department of Biomedical Engineering at the Johns Hopkins University with secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. The staggering modern digital revolution has resulted in extraordinary advances in how to acquire and process complex data but automatically interpreting and utilizing this data, on par with human abilities, is exceedingly difficult. Dr. Vidal is passionate about his work developing mathematical models that enable computers to see, analyze, and interpret images, videos, and biomedical data which will help to revolutionize the world. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 1188 TEDx Talks
Raising the Digital Trajectory of Healthcare
 
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Table of Contents Q&A 1:14:29 Should healthcare be more digitized? Absolutely. But if we go about it the wrong way... or the naïve way... we will take two steps forward and three steps back. Join Health Catalyst's President of Technology, Dale Sanders, for a 90-minute webinar in which he will describe the right way to go about the technical digitization of healthcare so that it increases the sense of humanity during the journey. The topics Dale covers include: • The human, empathetic components of healthcare’s digitization strategy • The AI-enabled healthcare encounter in the near future • Why the current digital approach to patient engagement will never be effective • The dramatic near-term potential of bio-integrated sensors • Role of the “Digitician” and patient data profiles • The technology and architecture of a modern digital platform • The role of AI vs. the role of traditional data analysis in healthcare • Reasons that home grown digital platforms will not scale, economically Most of the data that’s generated in healthcare is about administrative overhead of healthcare, not about the current state of patients’ well-being. On average, healthcare collects data about patients three times per year from which providers are expected to optimize diagnoses, treatments, predict health risks and cultivate long-term care plans. Where’s the data about patients’ health from the other 362 days per year? McKinsey ranks industries based on their Digital Quotient (DQ), which is derived from a cross product of three areas: Data Assets x Data Skills x Data Utilization. Healthcare ranks lower than all industries except mining. It’s time for healthcare to raise its Digital Quotient, however, it’s a delicate balance. The current “data-driven” strategy in healthcare is a train wreck, sucking the life out of clinicians’ sense of mastery, autonomy, and purpose. Healthcare’s digital strategy has largely ignored the digitization of patients’ state of health, but that’s changing, and the change will be revolutionary. Driven by bio-integrated sensors and affordable genomics, in the next five years, many patients will possess more data and AI-driven insights about their diagnosis and treatment options than healthcare systems, turning the existing dialogue with care providers on its head. It’s going to happen. Let’s make it happen the right way.
Views: 383 Health Catalyst
8. Decision Trees for CEA (V1)
 
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Demonstration of the design and use of a decision tree structure for CEA. To view text subtitles for the audio portion, click the CC button on the bottom right of the video viewer.
Leadership Competencies & Data/Information  Management in Healthcare
 
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I have prepared this presentation in compliance with JCI (Joint Commission International) MOI.7 standard & (CBAHI) The Central Board of Accreditation for Healthcare Institutions MOI.5 standard. This presentation will be also helpful in preparation of CPHIMS and CAHIMS exams. Module 1:Leadership Competencies in Healthcare Module 2: Data/Information Management in Healthcare Module 3: Data Collection, Processing and Analysis Module 4: Selection and use of indicators in assessment and improvement of work Process (Decision Making) in Healthcare Module 5: Healthcare Dashboards vs. Scorecards Module 6: Data/Information Confidentiality, privacy and security in Healthcare After watching presentation click on the link given below (or copy paste) to claim your certificate. https://docs.google.com/forms/d/e/1FAIpQLSdw8zxSTxdWan96pdsf_kclGRuk-sno4Z3sb9_MOVbpzbGs4A/viewform?usp=pp_url&entry.1270919058&entry.1809680621
Mining the FDA Adverse Event Reporting System with Oracle Empirica Signal
 
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Learn how to identify safety and pharmacovigilance signals by data mining FAERS with Oracle's Empirica Signal. -- Ever since the European Union (EU) introduced new legislation that requires life sciences companies to proactively detect, prioritize, and evaluate safety signals, there has been an increased interest, not only from sponsors and CROs in the EU, but globally, in pharmacovigilance systems that can assist with the signal management process. Please join Perficient's Chris Wocosky, an expert in signal detection and management, for this video in which she discussed how your organization can use Empirica Signal, Oracle's state-of-the-art signal detection system to data mine the existing FDA Adverse Event Reporting System (FAERS) to determine safety signals. This video will help you to better understand how this solution can be used in daily pharmacovigilance activities. To view this webinar in its entirety, please visit: https://cc.readytalk.com/r/7ekwxbm7q33t&eom Stay on top of Life Sciences technologies by following us here: Twitter: http://www.twitter.com/Perficient_LS Facebook: http://www.facebook.com/Perficient LinkedIn: http://www.linkedin.com/company/165444 Google+: https://plus.google.com/+Perficient SlideShare: http://www.slideshare.net/PerficientInc
Views: 5089 Perficient, Inc.
Clinical Data Exchange Explained
 
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In the simplest of definitions, Clinical Data Exchange is mobilization of healthcare information electronically across organizations. The ability to electronically move clinical information among disparate healthcare information systems, while maintaining the meaning of the information being exchanged, is powerful. Clinical data Exchanges enables easy access and retrieval of clinical data to provide safer and more timely, efficient, effective, and equitable patient-centered care. Applied to a group, it help public health authorities analyze and assess the health of the population. In this video, our CTO, Kevin Adams tackles key questions regarding Clinical data Exchanges. He answers to few of the basic but important questions around its importance of payers perspective “Clinical Exchange” - “Why is a clinical exchange is important?” Kevin discusses “What are the roadblocks to clinical data exchange for payers?” and also “What is the future for data integration for payers?”
Views: 1179 Edifecs
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 17142 techEIU
Time Series Analysis - 1 | Time Series in Excel | Time Series Forecasting | Data Science|Simplilearn
 
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This Time Series Analysis (Part-1) tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data. Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs You can also go through the slides here: https://goo.gl/RsAEB8 A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R. Below topics are explained in this " Time Series in R Tutorial " - 1. Why time series? 2. What is time series? 3. Components of a time series 4. When not to use time series? 5. Why does a time series have to be stationary? 6. How to make a time series stationary? 7. Example: Forecast car sales for the 5th year To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-gj4L2isnOf8&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 34791 Simplilearn
Health Care Quality Improvement Using Control Charts for Rare Events
 
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Bucky Ransdell introduces the new RAREEVEENTS procedure, which is part of SAS/QC software for statistical quality improvement. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 1254 SAS Software
Blockchain in Healthcare
 
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Medical records are dispersed across multiple structures and sometimes they're not available when you need most A custom-built healthcare blockchain must be set up Blockchain is a mean to smooth-running the sharing of medical records in a safe way, preserving sensitive data from hackers and giving patients more control over their information It's an ubiquitous network infrastructure, gets authentication of all contributors Blockchain technology creates unique opportunities to lessen complexity and to enable teamworking Global blockchain-based patient identifier could be connected to hospital records as well as data from different sources as employee wellness programs and wearable health monitors When a physician visits a patient or writes down a recipe, the patient agrees to have a reference annexed to a blockchain information This blockchain could register crucial medical records in a pretty much everlasting cryptographic database, supported by an interconnection of computers, accessible to anyone running the software Every pointer logged on the blockchain becomes part of a patient’s record, not depending on which system the doctor is working, so caregivers use it with no trouble about incompatibility issues The system must ease the exchange of complex health information between patients and providers, as well as exchanges between providers, and between providers and payers, remaining protected from malicious attacks and conforming to privacy regulations Miners (medical researchers and health-care professionals) perform the work and get rewarded by access to aggregated anonymized data from records for epidemiological studies The health-care blockchain could utilize the plentiful computing resources of hospitals to verify the exchange of information A blockchain powered health information exchange could unbolt the real value of interoperability Blockchain-based systems have the capacity to reduce or eliminate the resistance and costs of current intermediaries This technology connect fractioned systems to generate insights and to better assess the value of care A nationwide blockchain network for electronic medical records can upgrade efficiency and sustain better health outcomes for patients Blockchain verification for clinical trials can assure truthfulness in clinical research publication and restore some of the undermined reputation from the clinical research community Clinicians salute the decentralized ledger allowing for a simpler, more efficient and cheaper way to participate peer-reviewed research We can have greater awareness of new clinical studies, disease prevention and genome strains This could start with grants to help researchers develop a repository to support clinical trials especially for diseases that have no therapy at the moment Donors in the network can choose the purpose of the studies worth to get advanced, can acquire control of the data sharing process and be rewarded Rewards as part of the blockchain contribution generates the emergence of a new shared economy where members in the network benefit from being “claimless” New treatments and drug discovery can obtain FDA or EMEA approvals faster cause of the sampling pool of data is much more diverse, focused and accessible Duplication of work and fraud is reduced as consensus of parties in the network concretizes trust in the network Smart contracts get strenghtened up on top of the blockchain transaction, so rules can be applied in terms of how the transactions are achieved Blockchain can provide the means to collect and identify where data are, so that healthcare stakeholders can access patient data on a large scale. Useful for implement programmes of population health https://www.linkedin.com/in/rodolfo-buccico-bb412624/
Views: 82 Seeds Moods
Privacy Protection and Intrusion Avoidance for Cloudlet-based Medical Data Sharing
 
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Privacy Protection and Intrusion Avoidance for Cloudlet-based Medical Data Sharing using Java Project Code, Report and PPT Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 1060 1000 Projects
The power of Real World Evidence Analytics
 
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Data is at the heart of a revolution in healthcare innovation: Real World Evidence. Massive volumes of Real World Data (RWD) are generated every day. Because of the complexity of sourcing and analyzing RWD, more time and effort is often spent on data acquisition, cleansing, standardization and encryption than extraction of insights. Real World Evidence Analytics, fueled by the power of data science, can address these challenges. Find out how by watching this brief video. And visit http://www.saama.com/ls for more information.
Views: 4144 SaamaInc
All you should know about Big Data – Hadoop,Careers,Scope,Modules,Highpaid jobs
 
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Get Recruitment Notifications of all private and govt jobs , Mock test details ,Previous year question papers only at Freshersworld.com – the No.1 jobsite for entry level candidates in India. (To register : http://freshersworld.com?src=Youtube ) – This video is all about “Careers and Training courses for Big Data There are a handful of working definitions for big data, but to me it is most simply put as data sets so large and complex that it becomes difficult or impossible to process them using traditional database management applications. Granted, the term may apply differently to different organizations. For a smaller company, facing hundreds of gigabytes of data for the first time may trigger a need to explore new tools. Other companies that generate tons of transactional data, like UPS, wouldn't flinch with their existing toolsets until they hit tens or hundreds of terabytes. For freshers who wants to start learning big data here are a few tips: 1. Begin with the basics: If you are looking at building a career in big data, you can start with developing the core aptitudes such as curiosity, agility, statistical fluency, research, scientific rigor and skeptical nature. You have to decide which facet of data investigation (data wrangling, management, exploratory analysis, prediction) are you looking at acquiring. The first step to learning big data is to develop basic level of familiarity with programming languages. 2. Experience in programming languages: Begin with developing basic data literacy and an analytic mindset by building knowledge of programming languages such as Java, C++, Pig Latin and HiveQL. Figure out where you want to apply your data analytics skills to describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations. 3. Expertise in Hadoop: Developing knowledge about Hadoop Map-Reduce and Java is essential if you’re looking to be a high-performance data software engineer. 4. What are you looking for? If you are looking for a career switch to big data, begin with developing the skill sets required to work with Hadoop. A well-rounded understanding of Hadoop requires experience in large-scale distributed systems and knowledge of programming languages. 5. Data Analytics Skills: If you want to learn the fundamentals and want to get an indepth understanding of every aspect of Big Data, the resource material provided by Apache’s library is very useful. The Hadoop programme offered by Apache is an open-source software for reliable, scalable, distributed computing. Some of the other programmes offered are HBase Hive, Mahout, Pig ZooKeeper. 6. Online Courses: The big data universe is still very young, to get a well rounded expertise in big data it is important to learn and hone skills related to the subject. Decide on the course based on the skill set you're looking to get. Just by dedicating some time and energy, you can tackle learning big data with these free online classes. Applications of Big Data Big data includes problems that involve such large data sets and solutions that require a complex connecting the dots. You can see such things everywhere. 1. Quora and Facebook use Big data tools to understand more about you and provide you with a feed that you in theory should find it interesting. The fact that the feed is not interesting should show how hard the problem is. 2. Credit card companies analyze millions of transactions to find patterns of fraud. 3. There are similar problems in defense, retail, genomics, pharma, healthcare that requires a solution. The companies offering jobs on Big Data are : Qualcomm India Pvt Ltd, Accenture, Dev Solutions So let us summarize as Big Data is a group of problems and technologies related to the availability of extremely large volumes of data that businesses want to connect and understand. The reason why the sector is hot now is that the data and tools have reached a critical mass. This occurred in parallel with years of education effort that has convinced organizations that they must do something with their data treasure. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube Download our app today to manage recruitment when ever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Support Vector Machine in R | SVM Algorithm Example | Data Science With R Tutorial | Simplilearn
 
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This Support Vector Machine in R tutorial video will help you understand what is Machine Learning, what is classification, what is Support Vector Machine (SVM), what is SVM kernel and you will also see a use case in which we will classify horses and mules from a given data set using SVM algorithm. SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph. SVM is a classification algorithm used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Now, let us get started and understand Support Vector Machine in detail. Below topics are explained in this "Support Vector Machine in R" video: 1. What is machine learning? 2. What is classification? 3. What is support vector machine? 4. Understanding support vector machine 5. Understanding SVM kernel 6. Use case: classifying horses and mules To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/w72XBR Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Support-Vector-Machine-in-R-QkAmOb1AMrY&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 9794 Simplilearn
Big Data Changes: Strategy & Finance / Management Consulting
 
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https://www.firmsconsulting.com Strategy Skills Podcast: https://itunes.apple.com/us/podcast/strategy-skills-podcast-management/id1021817294?mt=2 Case Interview Podcast: https://itunes.apple.com/us/podcast/about-case-interviews-strategy/id904509526?mt=2 Corporate Strategy M&A Study: https://www.firmsconsulting.com/technology-corporate-strategy/#!step-1 Market Entry Strategy Study: https://www.firmsconsulting.com/market-entry-strategy/#!step-2 Case Interviews Training: https://www.firmsconsulting.com/alice-and-michael/ https://www.firmsconsulting.com/felix/ https://www.firmsconsulting.com/sanjeev/ https://www.firmsconsulting.com/rafik/ https://www.firmsconsulting.com/samantha/ In a recent post we discussed how MBA-styled financial modelling is already outdated for high-end corporate strategy work. Many readers wrote in saying it could not be true since the schools have not changed their curriculum or are just starting to merge finance, strategy and analytics. First off, cutting edge thinking rarely originates in business schools alone. And business schools tend to be very slow to adapt when/if they eventually do. In these examples from the New York Times all you see are the schools putting MBA's alongside technologists and hoping for something to happen by osmosis. Hope is not a survival strategy. MBA programs are being incredibly shortsighted since they are merely looking to adopt the business models from the tech companies, like lean processes, modularization etc. They should go much further and teach their students some core coding skills because as this video shows, some work can only be done by a very intelligent MBA in corporate finance who can also code. Big Data is allowing corporate strategists to better advise and interact with clients. It more than just flashy apps, but a fundamental re-wiring of the way we do strategy. Learning some basic code will create an entirely more productive, creative and effective group of management consultants. Remember, we have been here before: when MBA schools and consulting firms refused to adopt spreadsheet programing as a core skill. Learning to code is just another way to analyze problems and a better way than relying on creaky spreadsheets. As Theodor Levitt showed in Marketing Myopia, people should avoid being entranced by a product, in this case spreadsheets, and focus on what the client wants, in this case better and more effective analyses with Big Data. Since spreadsheets where the traditional means to the end, of analyzing clients, we should adapt to some form of deeper coding since that is a better means to the very same end. Subscribers to our executive programs will have detailed access to the tools and step-by-step methods to build these sophisticated analyses.
Views: 9801 firmsconsulting
Radical Analytics | Data Driven Decision Making in Business | Analytics Webinar | Simplilearn
 
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About the Webinar: Web Analytics has always been associated with defining objectives, setting KPIs, seeking executive buy-ins, and embracing a data-driven culture. If this is something you still believe, then your ROI is probably showing a downward trend. Presenting – A whole new approach to data-driven decision making – Radical Analytics. About the Host: Join Stephane Hamel, Google product strategy expert and Most Influential Industry Contributor (Digital Analytics Association) for a webinar on Radical Analytics - Uncover blind spots in your organization's data pyramid. What will you learn? 1. What the Manifesto for Radical Analytics is all about 2. How to quit asking and start proposing 3. Why begging for “executive buy-in” is a waste of time 4. How to embrace and love the "12 Principles of Agile" 5. What Lean Six Sigma process can do for your analytics #WebAnalytics #Simplilearn #SimplilearnDigitalMarketing #DataAnalyticsSimplilearn ----------------------------------------------------------------------------------------- If you have any questions/doubts/suggestions related to this video, please let us know through the comments section below. Also, let us know if you are looking for video of any specific topic, we can create and upload it for you. ----------------------------------------------------------------------------------------- Explore our Digital Analytics Foundation Course: https://www.simplilearn.com/digital-marketing/digital-analytics-foundation-training-course?utm_campaign=Radical_Analytics-Webinar-ppDsPAaUj6E&utm_medium=Tutorials&utm_source=youtube The Digital Analytics Foundation course helps participants develop a comprehensive knowledge of the various frameworks, tools, and techniques pertaining to digital analytics. Participants will learn to track campaign performance, access visitor behavior, and gain the competitive intelligence required to drive continual optimization in their marketing campaigns and improve the online customer experience. The course will enable participants to: 1. Use digital analytics and analysis to make informed business decisions 2. Identify hidden and real business needs 3. Explain the concept of personas and the customer journey 4. Contrast the concepts of Acquisition/Behavior/Conversion 5. Identify KPIs that are relevant for a given organization and 6. segments of data that reveal actionable insights 6. Use the Define-Measure-Analyze-Improve-Control (DMAIC) approach when conducting an analysis 7. Describe how attribution modeling is used to adjust marketing 8. spending decisions 9. List the benefits of testing 10. Define and contrast A/B and multivariate testing 11. Understand the fundamentals of effective communication through reports and dashboards and pick the right visual components to convey your message Explore our Free Resource Section: https://www.simplilearn.com/resources/digital-marketing?utm_campaign=Radical_Analytics-Webinar-ppDsPAaUj6E&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 366 Simplilearn
THE HUMAN FACE OF BIG DATA | Monitoring Health | PBS
 
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THE HUMAN FACE OF BIG DATA premieres Wednesday, February 24, 2016, 10:00-11:00 p.m. ET on PBS. We’ve begun an age of collecting information from sensors that are cheap and plentiful so that we can continuously process and learn things about our lifestyles and health. We can start to understand how we can collectively as a culture change our behavior.
Views: 3215 PBS
Data Science Project Lifecycle and Skill Set
 
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In this video, the Speaker, Jason Geng, talked about the Data Science Project Lifecycle and Data Scientist Skill Set. He introduced the whole process of Data Science Project: Business Requirement, Data Acquisition, Data Preparation, Hypothesis & Modeling, Evaluation& Interpretation, Deployment, Operations, and Optimization. There are more detailed explanation and examples, which can help you to understand these procedural concepts accurately. In addition, the video also introduced some skill sets that are looked for when building data team. More from Data Application Lab Official Reviews: Subscribe on YouTube: https://www.youtube.com/channel/UCa8NLpvi70mHVsW4J_x9OeQ Website: https://www.dataapplab.com
How Marketers Use Data and Analytics - PepsiCo's Ricardo Arias-Nath
 
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In this interview clip, Ricardo Arias-Nath discusses the how marketers are, and will be, using analytics to improve their effectiveness. Ricardo has over 20 years of consumer and corporate strategy, marketing, business development, and merger & acquisition experience with a deep understanding of fast-moving consumer goods (FMCG,) Media and Entertainment, and Technology Industries in both domestic and international markets. At the time of this interview, he is serving as SVP and Chief Marketing Officer for PepsiCo Beverages Latin America, leading the business growth agenda, consumer and brand strategy, innovation, consumer engagement, and partnership development. Prior to joining PepsiCo, he worked as Managing Consultant for Zyman Group and Prophet Brand Strategy, managing a wide range of engagements for clients domestically and internationally in consumer and durable goods, telecommunications, and financial services industries. From 2000 to 2006, co-founded and served as Chief Executive Officer for Tokenzone in New York, taking the company from inception into a profitable and award-winning online marketing, research, and consulting services firm for media and consumer product companies worldwide such as Disney, MTV Networks, Warner Bros. and FIFA. Ricardo started at Procter & Gamble Latin America Division in a diverse range of finance, marketing, and general management roles, including P&L management, financial planning, M&A, brand management, and innovation.
Views: 6434 Anthony Miyazaki
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 62330 edureka!
5 Tips to Transform Your Data Driven Marketing Strategy
 
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Download the Deep Dive: http://www.gleanster.com/report/innovative-technologies-for-aligning-customer-acquisition-customer-management Compliments of: http://www.v12groupinc.com/ This Deep Dive will explore best practices for aligning customer management and customer acquisition. We'll explore how Top Performing organizations are coping with the challenges of fragmented customer data and fragmented marketing technologies. We'll also provide short-term actionable recommendations and strategies you can employ today, to improve your top line growth with existing marketing spend.
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 7139 Story by Data
Random Forest Algorithm - Random Forest Explained | Random Forest in Machine Learning | Simplilearn
 
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This Random Forest Algorithm tutorial will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python. Below are the topics covered in this Machine Learning tutorial: 1. What is Machine Learning? 2. Applications of Random Forest 3. What is Classification? 4. Why Random Forest? 5. Random Forest and Decision Tree 6. Use case - Iris Flower Analysis Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/K8T4tW Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 72980 Simplilearn
Big Data and predictive analysis: use case in the hotel industry
 
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In order to improve its offer in a business strongy challenged by new players who offer new hosting modal. A hotel company intends to implement a Big Data solution that can predict hotel occupancy so that rates can be optimized according to demand. Discover how this hotel company has implemented a predictive analysis tool with no previous experience in Big Data thanks to Public Cloud and Orange Business Services experts. More about Orange Business Services: Official website: http://www.orange-business.com/en Facebook: https://www.facebook.com/orangebusiness/ Twitter: https://twitter.com/orangebusiness Linkedin: https://www.linkedin.com/company/oran... Slideshare: http://www.slideshare.net/orangebusiness Pinterest: https://fr.pinterest.com/orangebusiness
Future Of The World: See How The Life & World Will Be In 2050
 
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Future 2050 :See How The Life & World Will Be In 2050- Will The World Be Better ? In 1900, only 14% percent of the world’s population lived in urban areas. Today, for the first time in history, more than half the planet’s population resides in cities, which are fast becoming innovation hubs and are developing quickly into smart cities. The number of smart cities around the world is expected to grow exponentially over the next few years and by 2050, 70 per cent of the world’s population will be living in smart cities. Smart cities use Internet of Things (IoT) devices and sensors to gather and analyse information across infrastructure. This helps city authorities to intelligently manage their assets, increase efficiencies, revolutionise transport, reduce costs, and in theory, enhance overall quality of life for residents. Here what will happen in cities and with transportation Public buildings are also set to become smarter and aware of their surroundings by 2050. They will monitor data in a bid to constantly improve themselves. They’ll use this data to run at optimum efficiency and also ensure each occupant is safe and comfortable. Through the use of technology like solar windows, buildings will gather their own energy and become entirely self-sufficient. If they have any energy left over, it’ll be offered to vehicles in the local area to ensure no one runs dry. Ports will also be taking a leap into the future. By 2060, cargo will travel through hyperloop and will be moved rapidly around the world in smart containers that know their contents and their destination. The ports themselves will will be automated, run on renewable energy and have zero carbon emissions. Planes and aircrafts will be totally different.Travel will be faster,prettier ,more ecological and pleasure will be greater. Virtual reality is also expected to change the world : See how . Virtual Reality in Entertainment:! In upcoming years you will be surprised by the fact that your flat-screen television that represents a means of entertainment for you right now, but by 2050, it will seem outdated. In 2050, you’ll likely demand that your enjoyment not is contained by the screen but via virtual reality. ! With this technology, we will be able to meet up with friends and family around the world in a more reliable environment. Your children will be able to interact with their friends by inviting them into the living room to dance around. Also, long-distance relationships will become a little more manageable because of virtual visits, and even business meetings could be arranged through it. It might be possible that by 2050, robots will be conducting surgeries or piloting our airplanes. They could be doing search and rescue missions or fighting in wars. Roboticists predict that by 2050, we could see Robots vs. Humans in the contest for the World Cup. Robots and VR will all become familiar household additions, acquiring everyday jobs and providing endless entertainment. Tomorrow’s generation will spend less time cooking and cleaning, but more time socializing with their full-resolution life-sized 3D friends, who are only virtually present. Here' s how robots and artificial intelligence will reshape the world. FROM SMARTPHONES to chatbots, artificial intelligence is already ubiquitous in our digital lives. You just might not know it yet. The momentum behind AI is building, thanks in part to the massive amounts of data that computers can gather about our likes, our purchases and our movements every day. And specialists in artificial intelligence research use all that data to train machines how to learn and predict what we want—or detest. AI algorithms will enable doctors and hospitals to better analyze data and customize their health care to the genes, environment and lifestyle of each patient. From diagnosing brain tumors to deciding which cancer treatment will work best for an individual, AI will drive the personalized medicine revolution. AI assistants will help older people stay independent and live in their own homes longer. AI tools will keep nutritious food available, safely reach objects on high shelves, and monitor movement in a senior’s home. The tools could mow lawns, keep windows washed and even help with bathing and hygiene. Many other jobs that are repetitive and physical are perfect for AI-based tools. But the AI-assisted work may be even more critical in dangerous fields like mining, firefighting, clearing mines and handling radioactive materials. The place where AI may have the biggest impact in the near future is self-driving cars. Unlike humans, AI drivers never look down at the radio, put on mascara or argue with their kids in the backseat. Thanks to Google, autonomous cars are already here, but watch for them to be ubiquitous by 2030. Driverless trains already rule the rails in European cities, and Boeing is building an autonomous jetliner But AI could also have bad consequences on society.
Views: 15256 enrigue8
Medication adherence in patients with movement disorders using non-wearable sensors
 
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Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. However, physician observations are typically assessed based on visual inspection of movement and are limited to clinical testing procedures. Consequently, medication adherence is difficult to measure when patients are away from the clinical setting. The authors propose a data mining driven methodology that uses low cost, non-wearable multimodal sensors to model and predict patients' adherence to medication protocols, based on variations in their gait. The authors conduct a study involving Parkinson's disease patients that are “on” and “off” their medication in order to determine the statistical validity of the methodology. The data acquired can then be used to quantify patients' adherence while away from the clinic. Accordingly, this data-driven system may allow for early warnings regarding patient safety. Using whole-body movement data readings from the patients, the authors were able to discriminate between PD patients on and off medication, with accuracies greater than 97% for some patients using an individually customized model and accuracies of 78% for a generalized model containing multiple patient gait data. The proposed methodology and study demonstrate the potential and effectiveness of using low cost, non-wearable hardware and data mining models to monitor medication adherence outside of the traditional healthcare facility. These innovations may allow for cost effective, remote monitoring of treatment of neurological diseases. Source: http://www.sciencedirect.com/science/article/pii/S0010482515002930
Bits, Bytes and Brains - Where Watson Is and Where its going? by IBM
 
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Watson has come a long way since the Jeopardy! win in 2011 and the journey continues. As IBM has emerged as a cognitive solutions and cloud platform company, Watson has evolved into the cognitive platform for IBM. In the early days of Watson, many of our solutions were both on-premise and monolithic in nature. This had to change. We've embarked on a journey towards a more nimble microservice architecture with a continuous delivery model. We would like to share with you a microcosm of our journey in the evolution of NLP (Natural Language Processing) microservices and applying them to Healthcare. What problems have we solved? What benefits have we seen? What are the challenges ahead? We will demonstrate an analytic NLP Pipeline, with ability to define sequential or asynchronous analytic flows, and the ability to configure the behavior of an analytic, the NLP pipeline service is a key component of our adaptable NLP service strategy within Watson Health. We will work our way from NLP microservices to their use in our Watson for Oncology offering, demonstrating the pluggable architecture we have built for Watson Health Please join us and connect with us at the conference. Sandhya Kapoor IBM has been my home since 1990. Over the years I have been a part of some incredible technology application projects that led to my start with Watson in 2010. Since Watson’s commercial success, I have been around the world talking to an entire global developer community and some of the largest companies on the planet about developing cutting edge technology and how to duplicate our efforts for specific uses in their industries. I’ve dedicated the last seven years to building the artificial intelligence and deep learning circuitry, and have recently been involved with the development of a similar platform to serve the healthcare industry. Scott Carrier Scott is Squad Leader for the Watson Health Annotator for Clinical Data (ACD) Service. A 15-year veteran of IBM, Scott joined the Watson Group back in December of 2011. Prior to his work in Watson Health services, Scott was NLP Lead for client solutions. Scott was recently designated an IBM Master Inventor with several patents to his name in the cognitive computing space. Last but not least, Scott is a proud father of two awesome kids with a third on the way and loves getting them excited about technology. Mike Wilcox Mike is currently a member of the Watson Health technical architecture team. This ‘senior level’ team has the mission to drive a common architecture across the umbrella of Watson Healthcare solutions and acquisitions, while supporting agile software development. Mike has focused on PaaS, ‘big data’ analytics, API Management, DevOps automation, 'high availability' infrastructural design and Healthcare terminology. Mike also has been a technical member evaluating acquisition candidates for Watson Health. After obtaining a Master's in Electrical Engineering, Mike gained his IT experience by designing and customizing IT solutions for some of largest corporations in the US during some of the fastest growing periods in IT history. Mike has shared his technical knowledge via mentoring, chalk talks and speaking engagements to clients across the USA as well as in France, Ireland, Portugal, Australia and Austria.
Views: 208 Devoxx
Text Mining lecture 4
 
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Text Mining Lecture 4 Topic: Making Words work: using financial text as a predator of financial events 1:12 Introduction 1:57 Background and literature 2:49 Vector space model 11:08 Methodology 24:00 Data 26:51 Testing Methodology 28:10 Results 31:27 Substitue- Complement test 32:32 Conclusion Topic: Textual Analyses in Accounting and Finance: A Survey 35:38 History of textual analyses 37:20 Background for business Textual Analyses 38:06 Related Literature 39:37 Challenges of textual analyses 41:54 Examples of studies using Readability 44:45 Defining and Measuring Readability 1:02:05 Bag of Words Methods 1:03:17 Word Lists 1:06:49 Zip’s Law 1:07:31 Term Weighting 1:07:58 Naive Bayes Methods 1:09:02 Thematic Structure in Documents 1:09:48 Implementation 1:11:28 Areas for future Research in Textual Analysis 1:12:40 Conclusion 1:13:44 Python Coding Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subsc…
FACTOM REVIEW:  Factom Cryprocurrency - Factom Coin
 
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FACTOM REVIEW - Factom Cryprocurrency - Factom Coin ★ CONTACTS ➤ [email protected] I think that the Factom workforce has the opportunity plus the probable to make an organization larger than Oracle and Palantir and IBM mixed The consensus algorithm Factom employs is faster, less expensive to operate, and is a lot more suitable for a publishing platform. Anchors in Bitcoin enable it to be to ensure Factom cannot modify its very own heritage. Your account will probably be closed and all knowledge will likely be permanently deleted and can't be recovered. Do you think you're confident? Please Examine if you will discover properties that can be blocking the satellite sign. GPS desires an open up region to work. 2.The acute volatility of cryptocurrencies could adversely effects the financial budgeting functions of any company Factom is not really sidechains: Factom can use sidechains: Is Factom mostly about proof of publication, proof of course of action, or proof of audit? At this time, professional medical data are held in paper or electronic files. This method of file keeping results in being a problem when individuals shift or if the region will become economically or politically volatile. One Remedy will be to acquire infrastructure on a person foundation and secured as a result of Factom platform. 1aagd ulne y nlbutasd- fi0srn n 5e edlif0espoovyt - idm n0aA0d01oefennl5icngsl0arp etsu,r eh cdaieoa y eoer Separating Factoids from Entry Credits opens up Factom to users who possibly don’t realize or don’t need to use cryptocurrency. They might simply just buy Entry Credits utilizing the currency of their preference. Factom then purchases Factoids with the open current market and burns them to address the purchase. Take into account, after the state has become replayed, the simulator proceeds to run. In order to quickly examine the ensuing condition, and (in the situation of a leader) operate much more transactions and such. And this is usually journaled, so There's an capacity to modify and rerun the modified states. one FCTBTC, 240 Very long $FCT resting on long-term assist Not loads of assistance below listed here. If we drop we're going to drop really hard. I feel This is certainly bounce territory while. These answers are Particularly apparent for data that’s essential by more than one Corporation; home finance loan documents, supply chain verification, health care documents, as well as other files that happen to be regularly audited are all illustrations. By way of example, Medical doctors can use cell equipment to access information of infants born in much-flung spots so they are often supplied the right vaccinations. The goal is for Blockchain to aid help you save life and means in 3rd-globe international locations. Aims on factom the Invoice and Melinda Gates Basis are aligned Together with the Main mission and vision of Factom. No human being can Manage the Factom protocol. The open-resource program would make the protocol purpose worldwide. Everyone has the prerogative to utilize it for virtually any function. The organization will provide developers with tools to develop new era of apps utilizing the Blockchain platform. Blockchain will protect all transactions inside the community of Bitcoin. The central server might be discarded just in case the app demands a central server to coordinate processes.
Views: 7520 ICO REVIEW
Government Contracting - The EU’s New Data Privacy Law GDPR - Win Federal Contracts Bids
 
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US Federal Government Contracting Please visit us at http://www.JenniferSchaus.com for a full list of our complimentary webinars and #govcon services including GSA Schedule; SBA 8(a) Cert; Proposal Writing; Sales & Marketing; Contract Administration and more. WE ARE A DOWNTOWN WASHINGTON DC BASED FEDERAL CONSULTING FIRM. Main Office Phone: 202-365-0598 Jennifer Schaus [email protected] THANK YOU for viewing our federal government contracting webinars. Federal Acquisition, FAR, Federal Acquisition Regulation, Procurement, Federal Procurement, Contracting, Federal Contracting, Federal Contract Government, Contracting, Federal Contracts, DFARS, Defense Acquisition Regulation, Federal Regulations, SAM, System For Award Management, CCR, FBO, Federal Business Opportunities, Fed Biz Opps, Federal business, Business of Government, Set-Aside, set asides, SBA, small business, small business administration, 8a, 8(a), 8-a, 8-A, wosb, woman owned small business, vosb, veteran owned small business, minority owned, small business certification, federal certification, gsa, gsa schedule, gsa proposal, gsa audit, gsa cav, gsa modification, general services administration, gsa advantage, gsaadvantage, veteran, capability statement, cap statement, proposal writers, proposal writing, teaming, partnering, jv, joint venture, teaming agreement, teaming agreements, naics, small business standards, cage, cage code, psc, product service code, wawf, wide area work flow, contract administration, contract compliance, fss, federal supply schedule, idiq, indefinite delivery indefinite quantity, mas, multiple award schedule, federal acquisition, disadvantaged, disadvantaged small business, minority owned business, ccr, orca, central contractor registration, irapt, capabilities statement, federal training, small business instructions to offeror 52.212-1, small business instructions, reps and certs, dhs, fema, dod, defense contracting, defense acquisition, contract training, government sales, government contracts, federal sales, public sector sales, public sector acquisition, 8a program, gsa listing, gsa listings, gsa e-library, gsa e-buy, foia request, foia, freedom of information act, debrief, bid protest, how to win federal contracts, federal market, federal marketing, army, navy, air force, pentagon, procurement office, contract officer, contracting with the government, contracting with the federal government, how to win contracts, set-aside contracts, set aside contracts, sba 8a, nist, cyber security, federal regulations, contract negotiations, nist cyber security framework, bid assistance, federal bid assistance, bid training, government bidding, rfp, rfq, sources sought, government bid proposal, basics of government contracting, federal contracts, contracts, federal contractor registration, gsa list, gsa listing, gsa contractor, fpds, federal procurement data system, win federal contracts, win government contracts, simplified acquisition, govcon, #govcon, federal fiscal year, use it or lose it, usa spending, fed biz, federal business, winning government contracts, far flow down clauses, flow down clauses, sub contractor, prime, prime contractor, prime and sub contracting, sub-contractor, sub-contracting, sub contracting, subcontracting, dcaa, defense contract audit agency, federal accounting, cost accounting, CLIN, cpsr, contract purchasing system review, contract purchasing system, basics of government contracting, basics of federal contracting, basics of government contracts, basics of federal contracts, basics of federal government contracts, government contracting 101, gov con 101, federal contracting 101, how to win government contracts, how to get started in government contracting, bpa, blanket purchase agreement, what does the federal government buy, what does the federal government purchase, federal contract bid training federal contract training, federal procurement training, how to sell to the government, how to sell to the federal government, inside guide to government contracting, guide to government contracting, gov con guide, federal contracting guide, a to z of government contracts, a to z of government contracting, simplified acquisition threshold, sat, become a government contractor, become 8a certified, how to become a government contractor, how to become a federal contractor, essentials of government contracting, what are federal set asides, federal set asides, federal set-asides, set aside contracts, set-aside contracts, register for federal contracts, contractor registration, veteran contracts, federal contracts for veterans, bid assistance training, federal contracting bootcamp, government contracts bootcamp, bootcamp for government contracting, government contracting weekly, government contracting for small business, government contracting for veterans, government contracting for minorities, set-aside contracting, cyber security and government contracting,
Big Business: Unlocking Value from Big Data with Analytics
 
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Executives and data scientists from Baidu, LinkedIn, and Foursquare discuss how to generate real value from Big Data, and the importance of business leaders developing a vision of how Big Data is used in their organization. Susan Athey, Professor of Economics at Stanford Graduate School of Business, moderated this panel on "Generating Value from Big Data and Analytics" with panelists Li Fan (Baidu), Simon Zhang (LinkedIn), and Tianhui Michael Li (Foursquare) at the fourth annual China 2.0 conference hosted by Stanford Graduate School of Business on October 3, 2013. Learn more about the fourth annual China 2.0 conference: http://sprie.gsb.stanford.edu/docs/china20_2013 China 2.0 is an initiative of the Stanford Graduate School of Business focusing on innovation and entrepreneurship in China. Learn more: http://www.china2.org/
Strata Summit 2011:  David Schwab, "Big Data: Strategies for Generating Money..."
 
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Startups are in it to make money—whether by breaking even, being acquired, or finding some other exit. But for data-driven startups, turning bits into dollars can be a challenge. There are privacy issues, data ownership concerns, and questions about how to monetize the business. This panel of investors and entrepreneurs will look at strategies for generating money in data-driven startups. David Schwab Sierra Ventures Dave joined Sierra in 1996 and has built the firms software investing practice. Dave's professional career began at Lockheed Corporation in the engineering department where he managed a group of software developers building guidance and control systems. Following his tenure there, he joined Sun Microsystems where he spent 5 years in sales and sales management positions. In 1991 Schwab co-founded Scopus Technology with a fellow Sun sales manager and two other executives. While at Scopus, he served as Vice President of Sales and Marketing. Scopus was taken public and subsequently acquired by Siebel System. Dave holds Masters and Eng. degrees in Aerospace Engineering from Stanford University and an MBA from Harvard University. Paul Kedrosky Kauffman Foundation Dr. Kedrosky is an investor, speaker, writer, media guy, and entrepreneur. In his spare time he is a dangerous Twitterer, analyst for CNBC television, and the editor of Infectious Greed, one of the most popular financial blogs available over the Interweb. In the dusty distance of long-ago, Dr K. founded what he is reasonably sure was the first hosted blogging site, GrokSoup. After having grown it to be one the largest such services on the Interweb (admittedly before there were other such services), he demonstrated his unerring ability to enter fast-growing markets before they take off, and exit before they have grown large enough to deliver an island-purchasing exit. The rest is history, or least an index entry in one book on blogging. Todd Papaioannou Battery Ventures Todd Papaioannou is an Entrepreneur in Residence at Battery Ventures, where he works alongside the Enterprise IT investment team to evaluate investments in Big Data, Analytics, and Cloud Computing infrastructure. Todd has more than 15 years of Internet and Enterprise software experience covering a variety of senior leadership roles in R&D, product and corporate strategy, marketing and professional services. Prior to joining Battery, Todd was most recently VP, Distinguished Fellow and Chief Cloud Architect for Yahoo!. There, he was responsible for driving the technical and strategic direction of the Yahoo! Cloud and Hadoop teams, and was identified as one of the Top 10 Cloud Computing Leaders of 2011 by TechTarget. Additionally, Todd was responsible for leading and defining the overall Yahoo! corporate technology strategy. Robert D Thomas IBM Software Group Rob Thomas is Vice President of Business Development in IBM's Information Management Software Division. He is based in Somers, NY, and brings extensive experience in management, business development, and consulting in the high technology and financial services industries. He has worked extensively with global businesses and his background includes experience in business and operational strategy, high technology development and engineering, manufacturing operations, and product design and development consulting. In his current role, Mr. Thomas leads business development for Information Management software, which includes IBM's enterprise data management and information integration products. He is responsible for mergers & acquisitions, channel strategy and sales, and major ISV and SI partnerships. Recently, Mr. Thomas led IBM's acquisition of Netezza, the leader in data warehousing and analytical appliances. Clint Johnson Alpine Data Labs Clint Johnson VP of Customer Solutions for Alpine Data Labs. Prior to joining Alpine, he was the SVP of Data Warehousing and Analytics for Zions Bancorporation, a commercial bank holding company headquartered in the western U.S. In that role Mr. Johnson developed and led the strategy to implement enterprise-scale platforms and technologies for reporting, analysis, and predictive modeling. Mr. Johnson is a Stanford University certified project manager and received his Master's degree in Operations Research from the Colorado School of Mines and his Bachelor's degree in Mathematics from Adams State College, Colorado. He and his family in the mountains of southern Colorado.
Views: 1078 O'Reilly
Health Information Viedo1
 
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Health informatics (also called health information systems, health care informatics, healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is a discipline at the intersection of information science, computer science, social science, behavioral science and health care. It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics tools include computers, clinical guidelines, formal medical terminologies, and information and communication systems. It is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, physical therapy and (bio)medical research, and alternative medicine too.[1]
Views: 17 general image
Kenneth Buetow - Understanding the Genetics of Common Disease  Using Big Data approaches to see the
 
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Watch on LabRoots at http://labroots.com/user/webinars/details/id/342 Disease definition, diagnosis, treatment, and prevention are being fundamentally altered by the capacity to routinely perform comprehensive, multidimensional molecular characterization in disease and the individual in which it has developed. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of the disease process. This systems-wide molecular analysis has identified a complex cacophony of inherited and acquired variation. Coherence emerges from these data when evaluated using biologic networks as analytic frameworks. These networks account for the individual heterogeneity in underlying etiology as well as the diversity of events necessary to generate a complex phenotype such as cancer. Emerging collections of analytic approaches permit analysis using genome-wide data sets and established biologic networks as models. The generation of this unprecedented amount of data presents us with the challenge of contextualizing that data and converting into actionable information. The integration and interpretation of this complex multidimensional information into the evidence exceeds the raw human cognitive capacity. Information systems have the capacity to provide the needed “tool” to tackle this challenge. Arizona State University’s (ASU) Complex Adaptive Systems team is building such an Evidence Engine in its Next Generation Cyber Capability (NGCC). The ASU NGCC – composed of networks, hardware, software, and people transforms “Big Data” to information and creates the evidence necessary to enable personalized medicine. These approaches are being applied to understand the origins and outcomes of obesity. Complex interactions of one’s molecular constitution and the environment result in many different morbid outcomes. Big Data approaches hold promise in identifying the emergent whole that results from the interaction of the diverse components.
Views: 229 LabRoots
Machine Learning with R | Machine Learning Algorithms | Data Science Training | Edureka
 
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( Data Science Training : https://www.edureka.co/data-science ) This "Machine Learning with R" video by Edureka will help you to understand the core concepts of Machine Learning followed by a very interesting case study on Pokemon Dataset in R. This tutorial will comprise of these topics: 1. Understanding Machine Learning 2. Applications of Machine Learning 3. Types of Machine Learning Algorithms 4. Case Study on the "Pokemon Dataset" to implement Machine Learning Algorithms Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 30268 edureka!
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 37329 DataCamp
Using Social Sector Data to Build Bridges - Lessons Learned from PolicyWise’s Data Initiatives
 
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Data collected by the myriad of government and non-profit agencies during the provision of health and social services represent a significant underutilized source of intelligence services. Data are typically collected based on immediate need, rather than with a harmonized vision between organizations on how the data can be combined and harnessed to improve service delivery. PolicyWise for Children & Families has spent the last ten years working with Alberta social-sector government ministries to link anonymized data through the Child and Youth Data Laboratory (CYDL) initiative. The CYDL focuses on understanding the experiences of Albertan children and youth as they develop over time. PolicyWise is now leveraging this expertise to help community organizations liberate data through SAGE, (Secondary Analysis to Generate Evidence), a collaborative data repository platform that aims to connect stakeholders through secondary use of data. Research data, community service data, and administrative data related to health and social well-being is managed and shared through SAGE. SAGE increases the value of existing data by providing the infrastructure, processes and governance to bring stakeholders together to use data in new ways and inform social policy and practice. This session will discuss the approach of the CYDL and some high-level results from a longitudinal study of government administrative data. The session will also share lessons from the field in engaging and building trust and capacity with researchers and community organizations to facilitate data sharing and collaboration through SAGE. Resources: CYDL homepage: https://policywise.com/initiatives/cydl/ CYDL reports: https://policywise.com/initiatives/cydl/p1/experiences-of-albertan-children-in-20082009-reports/ CYDL Program Overlap Matrix: https://visualization.policywise.com/P2matrix/ SAGE homepage: https://policywise.com/initiatives/sage/ PolicyWise Twitter handle: @PolicyWise PRESENTERS Jason Lau Jason is the Director of Data Operations at PolicyWise for Children & Families. He joined PolicyWise in March of 2015. He is responsible for the operations of the Child and Youth Data Laboratory (CYDL), Secondary Analysis to Generate Evidence (SAGE) and other operational aspects such as privacy and security policies and procedures, and supporting new data-driven projects that further the vision and mission of PolicyWise. Prior to joining PolicyWise, Jason was with Alberta Health developing partnerships in the research and innovation sector, as well as evidence-informed policies on health technologies. He has also served clinical operations roles in the pharmaceutical industry and earned a PhD in Medical Genetics from the University of Alberta. Hannah Lloyd-Jones Hannah is the Project Coordinator for SAGE (Secondary Analysis to Generate Evidence), a PolicyWise data repository platform which incentivises the secondary use of data related to health and social well-being. An advocate for sharing data, Hannah's background is in project management in international Higher Education, with experience in diverse projects from establishing an open access repository and running an undergraduate programme to strengthen interdisciplinary research skills at the University of Exeter, UK, to setting up an online research management system at the Universidad Católica, Chile. Hannah’s Twitter handle is @hannaneira
This Is My Story by Henry Basil - TRC March 29, 2014
 
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Free News Sharing and On-Line Art Gallery http://www.ciactivist.org FEATURE: The 2016 Fire and Rain art project that began in early January was inspired by news stories on wildfires that burned throughout Western Canada in 2015. Paintings were displayed outdoors publicly throughout Edmonton and their stories shared on YouTube. I used art from the beginning to defend freedom of expression on the Alberta Legislature grounds when it was verbally banned 3 times by Legislature officials. Some of the YouTubes published shared how the wildfires and flooding that followed affected Albertans, their communities and the environment. I hope my art and the stories shared will inspire us to contemplate the calamities in Alberta of 2016 as a collective and together help each other find ways and better solutions to save our planet and our children's future. Doug Brinkman
Views: 2386 Doug Brinkman
IHPI Seminar: White Coat, Black Box: Augmenting Clinical Care with AI in the Era of Deep Learning
 
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February 21, 2019 Speaker: Jenna Wiens, Ph.D., assistant professor engineering, Department of Electrical Engineering and Computer Science, U-M College of Engineering Jenna Wiens is a Morris Wellman Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. She is particularly interested in time-series analysis and transfer/multitask learning. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge.
Views: 297 Michigan Medicine
S-CAR Dissertation Defense: Mariam Kurtz- Land Acquisition for mining: A case study of Tanzania
 
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Dissertation Defense: Mariam Kurtz- Land Acquisition for mining: A case study of Tanzania November 29, 2017 Committee: Dr. Richard Rubenstein (Chair) Dr. Leslie Dwyer Dr. Mark Jacobs Dr. Gwendolyn Mikell Abstract: An analysis of land acquisition for mining in Kakola, Tanzania, may explain land acquisition taking place in Africa and other parts of the world. In recent years, millions of hectares of African land have been sold or leased out to transnational or domestic entities while local people were evicted from their land. International corporations have acquired more land in resource-rich, financially poor countries like Tanzania, resulting in many conflicts from local to global levels. This study uses primarily qualitative approaches, with a multi-sited ethnography to investigate how the global process of land acquisition for mining in Kakola contradicts the local people’s meaning of land, and affects their relationship to the land and to each other, in terms of their kinship network as well as economic, cultural, and power dynamics. How has the process of land alienation affected their land rights such as their access to resources and traditional mechanisms of conflict resolution such as the use of elders and the involvement of ancestors through ritual ceremonies? I have explored and compared pro-development studies and critical scholarship regarding the impact of land acquisition for mining, as well as assessing the accuracy of each perspective given what is happening in the field. The discovery of gold gave women direct access to land because land was transformed from Inclusive Clans Land Ownership (INCLO) to the nuclear family, which empowered widows to inherit the land from their husbands then lost it to mining corporations. The transformation of meaning and treatment of land also created a new system of landholding, which included both a commodified view of land, on the one hand, and the traditional view of land as sacred and a means of communal survival. Paradoxically, the people Kakola held the two contradictory views simultaneously. When the corporations alienated their land, the grievances of the local people were not just about economic loss but also spiritual, social structural, and cultural. However, the outrage of local people was caused by a long history of exploitation, poor and unenforced land tenure laws that failed to provide legal protection and empower them and their communities.
Building Reconciliation: Universities Answering the TRC Calls to Action
 
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The University of Saskatchewan brought together university presidents and Aboriginal leaders from across Canada November 18-19, 2015 to discuss how universities can play a role in closing the Aboriginal education gap. The plenary event included remarks from TRC Commissioner Justice Murray Sinclair, Assembly of First Nations Chief Perry Bellegarde, Northwest Territories Deputy Premier Jackson Lafferty, Federation of Saskatchewan Indian Nations Chief Bobby Cameron, and Métis Nation Saskatchewan president Robert Doucette. More information is here: http://www.usask.ca/trc2015/
Views: 1324 Usask
Stephen Purpura, Context Relevant // Data Driven #26 // April 2014 (Hosted by FirstMark Capital)
 
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Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark Capital. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
Views: 961 Data Driven NYC
AI Reality: Where are we now? Data for Good? - Bill Boorman
 
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At Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June 2016, recovering recruiter Bill Boorman took a look at the AI landscape now, defining fact from fiction and wishful thinking. From real matching, to predictive "guess" machines, Boorman takes a look at the impact of technology on HR workflow, and what has really changed, integration over emulation, and the possible today versus the fantasy of the future.
Views: 652 Textkernel