We analyzed 2 million random keywords and their top 10 ranking pages to create a dead simple SEO strategy for faster Google rankings. So how long does it take to rank on Google? Subscribe ► https://www.youtube.com/AhrefsCom?sub_confirmation=1 Our data revealed that the average number one ranking page takes around 3 years, while only 5.7% of all studied pages ranked in the top 10 search results within 1 year for at least 1 keyword. But here’s the thing: the duration to rank on Google page one depends on numerous factors and can’t be generalized. In this video, you’ll learn: 1. How long it takes to rank in Google (and what it means for you as a blogger or website owner). 2. The main factors that helped accelerate pages to faster Google rankings. 3. A dead simple SEO strategy to rank your pages faster (even if your website is brand new). 4. How to find low-competition topics to write about. 5. 3 easy link building strategies you can rinse and repeat. 6. How to eventually start competing for high search volume and more competitive head terms. Be sure to subscribe for more actionable marketing and SEO tutorials. https://www.youtube.com/AhrefsCom?sub_confirmation=1 STAY TUNED: Ahrefs ► https://ahrefs.com/ YouTube ► https://www.youtube.com/AhrefsCom?sub_confirmation=1 Facebook ►https://www.facebook.com/Ahrefs Twitter ►https://twitter.com/ahrefs
Views: 21332 Ahrefs
https://kissmetrics.com - In this video we're going to show you how to get back on track if you've let your content marketing efforts slide. It's all about being data-driven! For more information, please read this blog article: http://blog.kissmetrics.com/optimize-your-content-marketing/
Views: 3947 Kissmetrics
In this part we will start discussing JXL data driven testing with Appium. For more articles and videos, please visit http://www.executeautomation.com
Views: 4857 Execute Automation
What is DATA-DRIVEN TESTING? What does DATA-DRIVEN TESTING mean? DATA-DRIVEN TESTING meaning - DATA-DRIVEN TESTING definition - DATA-DRIVEN TESTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data-driven testing (DDT) is a term used in the testing of computer software to describe testing done using a table of conditions directly as test inputs and verifiable outputs as well as the process where test environment settings and control are not hard-coded. In the simplest form the tester supplies the inputs from a row in the table and expects the outputs which occur in the same row. The table typically contains values which correspond to boundary or partition input spaces. In the control methodology, test configuration is "read" from a database. In the testing of software or programs, several methodologies are available for implementing this testing. Each of these methods co-exist because they differ in the effort required to create and subsequently maintain. The advantage of Data-driven testing is the ease to add additional inputs to the table when new partitions are discovered or added to the product or system under test. The cost aspect makes DDT cheap for automation but expensive for manual testing. Data-driven testing is the creation of test scripts to run together with their related data sets in a framework. The framework provides re-usable test logic to reduce maintenance and improve test coverage. Input and result (test criteria) data values can be stored in one or more central data sources or databases, the actual format and organisation can be implementation specific. The data comprises variables used for both input values and output verification values. In advanced (mature) automation environments data can be harvested from a running system using a purpose-built custom tool or sniffer, the DDT framework thus performs playback of harvested data producing a powerful automated regression testing tool. Navigation through the program, reading of the data sources, and logging of test status and information are all coded in the test script. Anything that has a potential to change (also called "variability," and includes elements such as environment, end points, test data, locations, etc.) is separated out from the test logic (scripts) and moved into an 'external asset'. This can be a configuration or test dataset. The logic executed in the script is dictated by the data values. Keyword-driven testing is similar except that the test case is contained in the set of data values and not embedded or "hard-coded" in the test script itself. The script is simply a "driver" (or delivery mechanism) for the data that is held in the data source.
Views: 938 The Audiopedia
Marshall University has partnered with Blackboard for over 20 years. Through this partnership, Marshall leverages Blackboard’s flexible solutions to create a connected experience for students, faculty, and the entire learning community. Marshall continuously improves the learning and campus experience through Blackboard data-driven insights. Full Story Video found here: https://youtu.be/H6rNshp_TtQ E-Learn Article found here: https://elearnmagazine.com/expanding-access-education-power-technology/
Views: 386 Blackboard Inc.
In this video we will discuss following topics of Katalon studio and understand how it works. 1. Working with different browsers 2. Working data driven testing using Excel sheet 3. Parameterize the values from Excel sheet (via script tab) 4. Working with different sets of data from excel sheet via iterations 5. Few error handling and timeout stuffs #ExecuteAutomation #Katalon #KatalonStudio Please hit like and share your comments about the video !!! For more videos and articles visit http://www.executeautomation.com Subscribe: ExecuteAutomation channel in Youtube !!! For more videos and articles please visit http://executeautomation.com/blog/ For complete course released from ExecuteAutomation visit https://www.udemy.com/user/karthik-kk For complete playlist of Katalon studio https://www.youtube.com/playlist?list=PL6tu16kXT9Po015vNjMIvbhZPAA6O-mT4
Views: 22161 Execute Automation
The Bartlett School of Planning Public Lecture 25th February. For as long as data have been generated about cities various kinds of data-informed urbanism have been occurring. In this paper, Professor Kitchin argues that a new era is presently unfolding wherein data-informed urbanism is increasingly being complemented and replaced by data-driven, networked urbanism. Cities are becoming ever more instrumented and networked, their systems interlinked and integrated, and vast troves of big urban data are being generated and used to manage and control urban life in real-time. Data-driven, networked urbanism, he contends, is the key mode of production for what have widely been termed smart cities. In this paper he provides a critical overview of data-driven, networked urbanism and smart cities focusing in particular on the relationship between data and the city (rather than network infrastructure or computational or urban issues), and critically examines a number of urban data issues including: the politics of urban data; data ownership, data control, data coverage and access; data security and data integrity; data protection and privacy, dataveillance, and data uses such as social sorting and anticipatory governance; and technical data issues such as data quality, veracity of data models and data analytics, and data integration and interoperability. Professor Kitchin concludes that whilst data-driven, networked urbanism purports to produce a commonsensical, pragmatic, neutral, apolitical, evidence-based form of responsive urban governance, it is nonetheless selective, crafted, flawed, normative and politically-inflected. Consequently, whilst data-driven, networked urbanism provides a set of solutions for urban problems, it does so within limitations and in the service of particular interests. Rob Kitchin is professor and ERC Advanced Investigator at the National University of Ireland Maynooth. He is principal investigator of the Programmable City project, the Dublin Dashboard, the All-Island Research Observatory, and the Digital Repository of Ireland, and the author of Code/Space: Software and Everyday Life (MIT Press, 2011) and The Data Revolution (Sage, 2014). He was the 2013 recipient of the Royal Irish Academy's Gold Medal for the Social Sciences. The Bartlett School of Planning Public Lecture Series is supported by Capita’s property and infrastructure business, a leading multidisciplinary consultancy that provides real estate, design, project delivery, infrastructure, and business transformation services. www.capita.co.uk/property
How researchers communicate their results is arguably as important as the results themselves. And yet, research communication has changed very little over the last 350 years. In this talk I will discuss how we're introducing a new model and mode of communication for researchers at Authorea. Authorea, a collaborative editor for researchers, allows authors to write rich data-driven manuscripts on the web–articles that natively offer readers a dynamic, interactive experience with an article’s full text, images, data, visualizations, and code–paving the road to increased data sharing, data reuse, research reproducibility, and Open Science. Alberto Pepe is the co-founder of Authorea, an online platform to write research together. He is a “recovering academic” with previous Ph.D. and Postdoc work in Astrophysics and Information Science. He holds degrees and fellowships from Harvard, UCLA, CERN, and University College London. He was born and raised in the wine-making town of Manduria, in Puglia, Southern Italy.
Views: 1026 Plotly
In this video, we will discuss how we can perform Data Driven Testing using CSV in TestProject for iOS/Android mobile application automation This video is part of YouTube course with complete playlist available herehttps://www.youtube.com/playlist?list=PL6tu16kXT9PrUJ842VaGcSNqIN7THFUlN #executeautomation #testproject #ios #mobileautomation #youtube TestProject is a yet to become a generally available tool for the public which is currently in beta testing stage and is available on request for invite signup option in https://testproject.io/ For more videos and articles please visit http://executeautomation.com/ For complete course released from ExecuteAutomation visit https://www.udemy.com/user/karthik-kk
Views: 238 Execute Automation
PyData Seattle 2015 Education has seen the rise of a new trend in the last few years: Learning Analytics. This talk will weave through the complex interacting issues and concerns involving learning analytics, at a high level. The goal is to whet the appetite and motivate reflection on how data scientists can work with educators and learning scientists in this swelling field. Higher education has used analytics for a long time to guide administrative decisions. Universities are already adept at developing data-driven admissions strategies and increasingly they are using analytics in fund-raising. Learning analytics is a newer trend. Its core goal is to improve teaching, learning and student success through data. This is very appealing, but it's also fraught with complex interactions among many concerns and with disciplinary gaps between the various players. Faculty have always collected data on students' performance on assessments and responses on surveys for the purposes of grading and complying with accreditation, sometimes also for improving teaching methods and more rarely for research on how students learn. To call it Learning Analytics, though, requires scale and some form of systemic effort. Some early university efforts in analytics developed predictive models to identify at-risk first-year students, aiming to improve freshman retention (e.g., Purdue's "Signals" project). Others built alert systems in support of student advising, with the goal of increasing graduation rates (e.g., Arizona State University's "eAdvisor" system). Experts now segregate these efforts out of learning analytics, proper, because retention and graduation are not the same as learning. The goal, in that case, is to improve the function of the educational system, while learning analytics should be guided by educational research and be aimed at enhancing learning. To elucidate what is learning analytics, it looks like we first need to answer: what is learning? What is knowledge? And can more data lead to better learning? That is perhaps the zeroth assumption of learning analytics—and it needs to be tested. There are assumptions behind any data system that go as far back as selecting what to track, where it will be tracked, how it will be collected, stored and delivered. Most analytics is based on log data in the Learning Management System (LMS). This "learning in a box" model is inadequate, but the diverse ecosystem of apps and services used by faculty and students poses a huge interoperability problem. The billion-dollar education industry of LMS platforms, textbook publishers and testing companies all want a part in the prospect of "changing education" through analytics. They're all marketing their dazzling dashboards in a worrying wave of ed-tech solutionism. Meanwhile, students' every move gets tracked and logged, often without their knowledge or consent, adding ethical and legal issues of privacy for the quantified student. Slides available here: http://figshare.com/articles/Data_driven_Education_and_the_Quantified_Student/1495511
Views: 2879 PyData
#myHRfuture talks to Rosa Lee (SVP, HR at Bosch) about how to build a data-driven culture in #HR and why People Analytics is a critical part of any forward-thinking HR organisation. The myHRfuture Academy is the leading destination for HR professionals looking for online courses that focus on the future of the HR profession. Our course content is designed by leading HR professionals that are innovators and experts in their fields. They regularly run workshops or present at industry conferences and come from some of the world’s largest companies. We know that you're busy and that in-person training or conferences can be expensive and time-consuming, that's why we've created bite-sized training courses that are broken into short video clips and are designed for you to get incredible insights at the pace that you want, so you can learn wherever and whenever you want. Read, watch and learn with @myHRfuture https://www.myhrfuture.com https://www.myhrfuture.com/academy Follow myHRfuture to stay up-to-date with news, articles and jobs : https://www.myhrfuture.com/resources/ https://www.myhrfuture.com/blog/ https://twitter.com/myHRfuture https://www.linkedin.com/company/myhrfuture/
Views: 503 myHRfuture
From Strata + Hadoop World 2015 in Singapore: With the recent advances of big data and machine learning technologies, there has never been a better time for developing telecom data products. However there are various challenges associated with researching and developing telecom data products at scale. A good telecom data product can only be prototyped after proper data research, which includes many steps, such as data cleaning, data aggregation, data modeling, and data interpretation. All steps must be tightly coupled with domain knowledge and may be iterated for multiple rounds. After a data product is prototyped, it needs to be carefully engineered and developed and constantly reevaluated and retuned. This tight coupling of data knowledge, domain knowledge, and business knowledge and strong dependency to new operational and business data makes the development of good telecom data product extremely challenging, especially with the strict regulation of telecom data. This keynote is sponsored by Huawei. About Sanqi Li (Huawei): Dr. Li currently serves as CTO of Products and Solutions at Huawei. Prior to this, Dr. Li served as CTO at Carrier Network Business Group, IT product line and Core Network product line as well as the president of Data Center & Media Network business unit. Before joining Huawei in 2009, Dr. Li held several senior leadership roles and served as a full-tenured professor at ECE department, University of Texas, Austin in US. He co-founded high-tech companies such as GaoHong Telecommunication Technology Inc. in China (acquired by Datang Telecom Group in 2000) and Santera Systems Inc. in US (acquired by Tekelec in 2003) and also co-led and architected original spin off of Santera Softswitch to form Spatial Wireless (acquired by Alcatel in 2004). In 2003-2007 he served as CTO at Tekelec. In his distinguished career, Dr. Li provided technical consulting services to companies like Cisco, AT&T, Verizon, KT and Samsung, and served on the advisory boards of several high-tech startups. Dr. Li had 25 patents filed/granted and over 160 papers published in international academic archival journals and best-in-class refereed international conference proceedings. Over 20 Ph.D. students graduated under his supervision. Watch more from Strata + Hadoop Singapore 2015: https://goo.gl/lBSB0b Visit the O’Reilly data website: oreilly.com/data Don't miss an upload! Subscribe! http://goo.gl/szEauh Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 517 O'Reilly
Understanding customer behaviour is crucial for any retail business that wants to succeed in the digital age. By knowing who buys what, retailers can truly stand out from the crowd of competitors. Read more: https://trendxs.io/diynext/article/leading-retailers-are-betting-big-on-data-driven-technologies
Views: 3 TrendXS
WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore ABOUT THE TALK: Traffic from social media channels is of remarkable importance to most media publishers, including Forbes. We want our articles to be read by more and more people, and traffic from social media channels is where that opportunity is. In this talk, we will discuss Forbes’ techniques for exploring different social media phenomena and how we create products from that exploration. We will discuss in detail the Forbes Bot Initiative—a system for productizing data science models that involves the creation of many small and independent agents (i.e. “bots”)—including its key software components. That discussion will build up to a key idea: that data science models excel when teams are able to quickly experiment, constantly evaluating model results against business indicators. ABOUT THE SPEAKER: Luis Capelo is a programmer specialized in the design and development of data products. Luis is the Head of Data Products at Forbes, where he spends time thinking about bots and content optimization problems. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 231 Data Council
We present a dynamic controller to physically simulate under-actuated three-dimensional full-body biped locomotion. Our data-driven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-the-fly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively. Yoonsang Lee, Sungeun Kim, Jehee Lee, Data-Driven Biped Control, ACM Transactions on Graphics (SIGGRAPH 2010), Vol. 29, No. 4, Article 129, July 2010
Views: 200 Yoonsang Lee
This video is the 5th in the series about creating a Master Data Management System in SQL. In this video, I define (and normalize) the way we create tables in our system by implementing a data-driven table creator. Articles applicable to this series: http://www.elricsims.com/wordpress/from-the-beginning/
Views: 40 Elric Sims
All referenced content can be found here: http://www.leapfrogbi.com/2017/12/31/blind_justice/ Does race or gender impact sentencing lengths? To what extent? The United States Sentencing Commission (USSC) published a report which explores the correlation between demographic factors and federal sentence lengths. In this video, we review the findings in depth, look for bias in referencing articles, and draw our own conclusions. Data-driven decision making in action. This is the first in a series of content focused on data-driven decision making. LeapFrogBI is passionate about building highly valuable business intelligence solutions. Value is only achieved when decision makers use business intelligence solutions to reach their goals such as growth and operational efficiency. This series explores the challenges related to data driven decision making and offers solution to common obstacles.
Views: 305 LeapFrogBI
Author Jess Hemerly expands on her article "Public Policy Considerations for Data-Driven Innovation," in which she discusses how regulation could preclude some of the economic and societal benefits of data-driven innovation. Interview conducted by Katina Michael of the University of Wollongong, from Computer's June 2013 issue: http://www.computer.org/csdl/mags/co/2013/06/index.html. Visit Computer: http://www.computer.org/computer.
Views: 590 ieeeComputerSociety
http://www.gootenberg.fr/en/ Public Relations is the art of improving brand awareness or corporate reputation without paid advertising They also called PR. What’s the use? Through public relations, your brand is featured in press articles and on broadcast media, your brand is liked and followed on social media. You can thus establish a community of allies, customers or influencers, who recognize your brand attributes and support its development. But … Many companies have questions about the way to evaluate the efficiency of PR actions. Though, today, there are indicators which allow an unquestionable measure of this efficiency. For example • Audience, which is the number of people who were exposed to your messages • Share of voice, the equivalent of market share in terms of media presence, • Search engine optimization, which corresponds to the value of contents that are displayed on the 1st page of Google search results. Efficient PR actions, built on performance indicators, that’s what we call Data driven PR at Gootenberg. Our team would be delighted to support you on this way. You will then see that PR is more than just blah blah blah.
Views: 41 Gootenberg
What if you could make use of Machine Learning to achieve data-driven Product Design? Predit can improve the workflow for Product Designers, combining human intuition with machine given insight based on a huge number of feature combinations. When new articles are created, Predit algorithms provide the designer with a forecast of customer appeal, uncovering hidden correlations between product features to help them take informed decisions ----------------------------------------------------------------------------------------------------------- [email protected] www.predit.it Music: www.bensound.com
Views: 47 Predit
Creating a successful marketing strategy all starts with defining your objectives. But what’s the best way to identify your marketing objectives? Should you choose goals based on the platforms (Google, Facebook, etc.), you plan to utilize for reaching new customers? Or should you choose to experiment with some marketing tactics that seem reasonable, commit to them, and go for it? If you tried either of those approaches, it’s likely that you didn’t get the results you were hoping to achieve. Trust me; we’ve all been there. Why don’t these approaches work? Because neither of these approaches is strategic, they are an assembly of tactics being passed off as a marketing strategy. You probably know by now that marketing is the mechanism that drives customers to your business. But a good marketing strategy isn’t about playing a game of copycat. Good marketing is about connecting with potential customers and making them familiar with your solution, then convincing them to go forward with your solution over all others. So if you want your business to grow, it’s important to be precise and well thought out with your marketing objectives. In this post, I will teach you how to use my four-stage marketing model, ACES, to establish your data driven marketing objectives. We’ll also look at how to use your objectives to guide your marketing strategy. And I’ll show you how you can track and measure your progress towards achieving your goals. Download the ACES Framework and Dashboard - https://www.datadrivenu.com/resources/aces/ Get my Year-In-Review Marketing Report Template - https://www.datadrivenu.com/resources/year-end-review/ his is what it means to be “data driven” with your marketing objectives Ok, so far we’ve discussed what not to do. Now let’s talk about how to set marketing targets that you can actually achieve. In general, data driven marketing objectives satisfy four criteria. They: - Account for how each stage of your marketing process impacts expected results - Align with your budget, team, tactics, and tools - Are connected to outcomes that your business finds valuable - Allow you to track progress and make adjustments based on initial results - How your marketing funnel will impact your objectives A marketing funnel is a way to picture the journey your audience takes on their way to (potentially) becoming your customer. In the AIDA model, your marketing message attempts to gain your audience’s attention. Then your prospective customer shows interest in your message or offer. That interest elevates to desire. And finally, if you’re successful, your audience takes action on your offer. The AIDA model is an excellent representation of the customer journey, but AIDA doesn’t always put your marketing into context. It’s also a 20th-century model that makes you fill in the blanks with your marketing targets, actions, tactics, and objectives. Without context or targets, AIDA is just another empty vessel. It’s time to take things to the 21st century with the ACES Framework. Using the ACES framework to define your marketing objectives I developed the ACES framework to help marketers match their objectives to their KPIs and their KPIs to their marketing strategy. Marketing objectives - ACES Dashboard In general, most businesses engage in four focused areas marketing: (A)wareness, (C)apturing Intent, (E)ducating and Nurturing customers, and making (S)ales – ACES. Read full article - https://www.jeffalytics.com/marketing-objectives/
Views: 356 Jeffalytics
Today we’re going to break down the 3 major data analytics mistakes that lead to misleading results in your data driven marketing, this can ultimately affect the growth of a business! Data is becoming more and more available, so data driven marketing is becoming easier and more essential! But without good data analytics, your conclusions can become misleading! Even with good quality data, we find people make frequent data mistakes in their rapid experimentation process! These are due to both inexperience with data analysis and a pressure to report significant findings to the rest of the business. How to Prepare Data for Machine Learning and A.I. video 👉https://youtu.be/TK-2189UcKk Link to data fallacies article 👉https://www.geckoboard.com/assets/data-fallacies-to-avoid.pdf The 3 Major Data Analytics Mistakes to Avoid in Data Driven Marketing: 1. Data Dredging Data dredging is repeatedly testing the new hypothesis against the same sample data until you finally find some significant results. This is common in cases where we test lots of different variations of a website or product, often with ab testing copy, calls to actions etc. When following the conventional level of significance, which is 5%, the risk is that 5 random variations could be significant. This is called the false positive risk or error of type 1. 2. False Causality The second data analytics mistake to avoid is false causality. Remember, Correlation is not equal to causation! This is why we run ab testing and multivariate testing. We always want to compare the performance of new ideas with the business as usual control group. At Growth Tribe, our growth process encompasses both machine learning steps to predict customer behavior in the pirate funnel but also the causal inference is given by experimentation. This is basically the data science definition of growth hacking! Machine learning can help us find the correlations but you should also test if there is a causal effect between out idea and the growth metric! 3. Overfitting in Machine Learning The last data analytics mistake is overfitting in machine learning. More complex algorithms can become overly tailored to the data. It can lose its generalization power to predict future cases. Different to statistical analysis, in machine learning, we have to keep a smaller portion of the sample to test how the model will perform when looking at new cases. If the accuracy is lower in the test set than compared to the training set then the model is probably suffering from overfitting. To prevent overfitting in machine learning we can apply regularization techniques. ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/school/growt... Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Video URL: https://youtu.be/cmTdTtQR3G0
Views: 2337 Growth Tribe
In our 4th Weekly Data Analysis Video Podcast, Quadrant CEO Mike Davie highlights bad actors who harm decision-making by deliberately modifying data, best-known as data tampering. Of course, it doesn’t always have to be a malicious act, it could be due to incompetence or lack of knowledge and skills. Still, the consequences will be the same and can cause huge problems especially in governmental or financial. Did you know that, despite a number of new data protection regulations coming into effect, some 4.5 billion data records were compromised in the first half of 2018, according to the Breach Level Index report? In the analysis, Mike refers to the following articles: Data breaches resulted in 4.5bn compromised records in the first half of 2018 https://www.verdict.co.uk/data-breaches-gemalto-breach-level-index/ Japan’s Kobe Steel Indicted Over Quality Scandal https://www.wsj.com/articles/japans-kobe-steel-indicted-over-quality-scandal-1531997704 Statistics Canada retracts July jobs report because of 'error' https://www.cbc.ca/news/business/statistics-canada-retracts-july-jobs-report-because-of-error-1.2734470 Thanks for watching! Social Media: LinkedIn: https://www.linkedin.com/company/quadrantprotocol Twitter: https://twitter.com/explorequadrant Facebook: https://www.facebook.com/quadrantprotocol Read our latest news on our Blog: https://medium.com/quadrantprotocol JOIN OUR TELEGRAM COMMUNITY: https://t.me/quadrantprotocol For media inquiries or if you would like to invite Mike to speak at your conference please email us at [email protected]
Views: 97 Quadrant Protocol
Ben Wellington uses data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating. Never has a fire hydrant been so interesting as in this talk. Ben Wellington is a computer scientist and data analyst whose blog, I Quant NY, uses New York City open data to tell stories about everything from parking ticket geography to finding the sweet spot in MetroCard pricing. His articles have gone viral and, in some cases, led to policy changes. Wellington teaches a course on NYC open data at the Pratt Institute and is a contributor to Forbes and other publications. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 161808 TEDx Talks
This is a sampler of Acculation's IG feed that received a fair amount of press attention in late 2017 and 2018 in International press: data-driven social media, data-driven, AI and algorithmically generated social media content, known as multi-media aka multi-platform aka crossmedia aka transmedia, including glitch art, 3D printed objects that started out as blog postings and social media postings, VR objects, VR virtual fashion shots that started out as blog content, glitched classics, space, etc. This is best viewed with closed-captioning turned on, which provides commentary. This AI and data-driven technology to create and repurpose social media content algorithmically across multiple platforms (and then back again to the original social media channels in revised form) was featured in articles in the US Chamber of Commerce Foundations Institution of Management (IOM) blog, CMS Wire, The Innovation Enterprise Blog, as well as international articles in foreign languages such as Spanish (we lost track of the press coverage). If you'd like to learn more, or engage Acculations expertise to put this technology to work for your organization, check out the related blog posting, which links to some of the press articles: https://www.acculation.com/blog/2016/12/01/data-driven-social-media-video/
Views: 591 Acculation, Inc.
Video abstract for the article 'Swarming, schooling, milling: phase diagram of a data-driven fish school model' by Daniel S Calovi, Ugo Lopez, Sandrine Ngo, Clément Sire, Hugues Chaté and Guy Theraulaz (Daniel S Calovi et al 2014 New J. Phys. 16 015026). Read the full article in New Journal of http://iopscience.iop.org/1367-2630/16/1/015026. Part of Focus on Swarming in Biological and Related Systems
Views: 541 NewJournalofPhysics
Speakers: Dan Haight, Darkhorse Analytics; Elli Max, Ogilvy Worldwide; Avery Johnson, Kaden Ave Communications. Moderated by Kyle Murray, School of Business. The Geddes Lecture Series features leaders from both the private and public sectors, in addition to bringing some of Alberta's world-class researchers and their findings to the community, with the goal of promoting awareness and discussion on timely, impactful, and relevant business topics. More info at: http://uab.ca/geddes
Views: 139 UAlbertaBiz
Data can provide powerful insights into why your people leave. Our Chief Scientist introduces four steps to follow when using data to investigate employee churn. Read the full article here: https://blog.cultureamp.com//a-data-driven-approach-to-understanding-employee-turnover
Views: 161 Culture Amp
Lois Greco from the Wells Fargo Regional Foundation introduces the article, "Investing in Community Change: An Evaluation of a Decade of Data-Driven Grantmaking," written by Greco; Maggie Grieve, Success Measures at NeighborWorks America; and Ira Goldstein, Ph.D., The Reinvestment Fund published in The Foundation Review Vol. 7 Issue 3. Read the open access article at: http://scholarworks.gvsu.edu/tfr/vol7/iss3/6/
Views: 153 Dorothy A. Johnson Center for Philanthropy
Supplementary video S1 for https://www.sciencedirect.com/science/article/pii/S1053811918300065
Views: 20 SlicerDMRI
Event Driven Systems pass and persist events. They have evolved from the publisher subscriber model, and the design allows many advantages to specific scenarios. These events are immutable and can be replayed to allow the systems to take snapshots of it's behavior. This allows services to 'self heal' as explained. A lot of transaction issues are alleviated once idempotency and retrial logic is added to a system. The system can retry messages an infinite number of times to the recipient till there is a message acceptance and acknowledgement from the receiver. Event driven systems are closely related to event sources and CQRS. Greg Young and Martin Fowler have been talking about these systems for a while. Events are persisted in something like a message queue, and hence the responsibility to retrial and persistence is moved to it. These abstractions enable the programmer to focus on the business logic of a system and add subscribers to events with minimum coupling with other services. Decoupling the system is one of the advantages of event driven systems. One major disadvantage of this system is that it is difficult to reason about the flow of a request. Services can independently register for an event and consume it without the publisher being aware of it. We talk about different applications using an event driven architecture such as Git and Gaming Systems. We then discuss the advantages and disadvantages of such an architecture. References: Martin Fowler: https://www.youtube.com/watch?v=STKCRSUsyP0 Martin Fowler Blog: https://martinfowler.com/articles/201701-event-driven.html Wikipedia: https://en.wikipedia.org/wiki/Event-driven_architecture Chris Richardson: http://microservices.io/patterns/data/event-driven-architecture.html (I will be talking about this soon! ) You can find me at: https://www.facebook.com/gkcs0/ https://www.quora.com/profile/Gaurav-Sen-6 https://www.linkedin.com/in/gaurav-sen-56b6a941/
Views: 25098 Gaurav Sen
Video summary of the tool showcased in the paper Benito Santos, A., Theron, R., Losada, A., Sampaio, J. E., & Lago-Peñas, C. (2018). Data-driven visual performance analysis in soccer: an exploratory prototype. Frontiers in Psychology, 9, 2416. https://doi.org/10.3389/fpsyg.2018.02416 https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02416/full
Views: 32 Roberto Theron
This episode presents a review of the current approaches to data management and application within the drug development and research setting, highlighting major critical challenges and emerging solutions for organisations that are determined to harness big data and machine learning. Data-driven companies that use integrated and advanced analytics outperform their competitors in every sector outside of the pharmaceutical industry. To compete in an increasingly crowded commercial environment, pharmaceutical and life science companies must gain a greater understanding of the wide-ranging implications of big data and machine learning. These innovations can be applied effectively to drive drug discovery, power research and ensure a sustainable future. Original article by Dr Satnam Surae If you'd like to view the original article then follow the link below: https://www.ddw-online.com/summer-2018-teasers/p322394-data-driven-transformation-in-drug-discovery.html You can also download the original article pdf here: https://www.ddw-online.com/media/32/131038/(8)-data-driven-transformation-in-drug-discovery.pdf For more information on Drug Discovery World, head to: https://www.ddw-online.com
Views: 7 Drug Discovery World
While data is an indispensable part of the business environment, many organisations are still not harnessing its potential. Kelly Preston, Data Analytics Manager at SilverBridge, believes that data-driven services will empower insurers to do so, thereby addressing customer needs more effectively. Read the full article here: https://silverbridge.co.za/unlock-opportunities-data-driven-services/
Views: 100 SilverBridge
Links to stories shown: http://www.wildcat.arizona.edu/article/2016/09/wheres-the-party-at-just-follow-the-red-tags http://www.wildcat.arizona.edu/article/2017/01/n-parking-and-transportation-tickets-data-story?_h=d049f181-c0f0-4401-8c7e-3528c28faaba http://www.wildcat.arizona.edu/article/2016/11/proposition-205-data-tells-the-story http://www.wildcat.arizona.edu/article/2017/01/stats-tell-the-story-of-the-sean-miller-era
Views: 7 Sean Alexander Furrier
Everyone is talking about “big data” these days. But, most small businesses I encounter don’t use much data at all when they make decisions. Some focus on how much money is in the bank and that’s it. Read more on my article: https://1mpul.se/2kAEnYt
Views: 18 Impulse Creative
In an interview with Marketing ID’s Brian Anderson, Bombora Co-founder and SVP of Data Sales Mike Burton discussed the growing role marketing analytics has in the content creation process. The pair also discuss how data impacts every stage of the content lifecycle — everything from targeting and segmenting to the nurturing process.
Views: 265 Marketing Insights & Data
Judi asks, "In a recent NY Times article on analytics in media, they make a distinction between being data informed as opposed to data driven, prizing human judgement over data and not letting data like pageviews dictate content strategy. What are your thoughts?" Data-informed and data-driven to me are largely semantics; both indicate we are making decisions using data. I use the example of the GPS for what it means to be data-driven. Most of the time, we don't ask our GPS to tell us our destination, just how to get there. A select few times, we'll use an app to suggest destinations, but human judgement still matters most. The source article referenced: https://www.nytimes.com/2018/05/23/technology/personaltech/metrics-media.html Got a question for You Ask, I'll Answer? Submit it here: http://www.christopherspenn.com/newsletter/you-ask-ill-answer/ Subscribe to my weekly newsletter: http://www.christopherspenn.com/newsletter Please subscribe to my YouTube channel for more marketing and analytics videos! https://www.youtube.com/user/christopherspenn Need help with your company's data and analytics? Let me know: https://braintrustinsights.com/
Views: 15 Christopher Penn
Zigmund Rubel, Founder at Aditazz.com, addresses KA Connect Activating Knowledge: Data-Driven Design Zigmund Rubel, Founder, Aditazz Mr. Rubel is in the process of founding Aditazz, a business with a vision to leverage computer technology to improve the design and project delivery of healthcare buildings. Prior to starting Aditazz, Mr. Rubel was a Principal at Anshen + Allen Architects. Mr. Rubel is a nationally recognized contributor to the advancement of Integrated Project Delivery. He contributed to the AIA California Council's Definition Document on IPD as well as AIA National's IPD The Guide. He has spoken extensively about IPD as well as writing several articles on it. KA Connect is a community of AEC professionals driven to transform the way the industry organizes information and shares knowledge.
Views: 1035 MmurrayAditazz
For Adwords Management visit http://ethicalmarketingservice.co.uk Twitter: https://twitter.com/EMS_TG Facebook: https://goo.gl/4qZcAY Google+: https://goo.gl/nzleFr Instagram: https://goo.gl/qy7Fqt Pinterest: https://goo.gl/qLc4DI Soundcloud: https://goo.gl/lNcXT2 Social Media Management: http://ethicalmarketingservice.com Article Link: https://adwords.googleblog.com/2017/04/data-driven-attribution-results.html
Views: 688 Ethical Marketing Service
Attention Marketers: Does your web developer know about Facebook Partner Integrations? It's a tool within Facebook Business Manager that makes it super easy for the non-technical marketing person to easily integrate their website conversions, eCommerce or Lead Generation, or even custom events like button clicks and scroll-to-stops, modal window opens, etc. - and, perhaps the best, CRM lead generation integration, ALL BACK TO FACEBOOK ADS! We're talking a three to five minute set-up process to get your Shopify Store, WordPress WooCommerce Store, even Magento, BigCommerce stores hooked up directly into your Facebook Ads reporting dashboard. It even includes CRMs like InfusionSoft, SalesForce and Zoho, and of course, Google Tag Manager. Not only does this allow you to SEE THE DATA that is most valuable to you, but it allows you to RUN future Facebook Ads that are optimized to the actions that you want. Here is a video that explains it all.
Views: 90 Paul Hickey
Join The Higherside Chats podcast as host, Greg Carlwood, talks our data-drive world, sex crimes, and the Manson Murders cover-up with returning guest, Neil Sanders. There are a handful of crimes that have captivated the attention of the American public, and there is no denying, the 1969 Manson "family" murders are certainly one of them. Ingrained in the collective subconscious decades later, the unusual circumstances surrounding these events have piqued the interest of researchers, including today's guest, Neil Sanders. As an author of books such as "Your Thoughts Are Not Your Own" Sanders has studied the use of mind control, mass manipulation and perception management carried out by alphabet agencies and inflicted upon the public through marketing, movies, and music. His latest book, "Now's The Only Thing That Is Real", is a re-examination of the Manson murders, myths and motives, and today he joins The Higherside to discuss about some of the key components in America's most heinous murder spree. Become a Plus Member at www.TheHighersideChatsPlus.com/subscribe to hear a second hour of all THC episodes. This week's included: - the details surrounding Jack Nicholson as one of the Tate Murder cleaners - homemade celebrity porn found at the Polanski home - the “snuff film” accusations of Hunter S. Thompson - the Manson crew's operation striping down cars in the desert and why Neil finds that telling - the Net Neutrality Fallacy - artists and the churn and burn culture of the digital age A few valuable resources from the interview: Neil Sanders on The Higherside Chats "Dark Secrets Of The Hollywood Mind Control Machine": https://www.thehighersidechats.com/neil-sanders-mind-control-interview/ "The one weird court case linking Trump, Clinton, and a billionaire pedophile": https://www.politico.com/story/2017/05/04/jeffrey-epstein-trump-lawsuit-sex-trafficking-237983 "Bill Clinton ditched Secret Service on multiple 'Lolita Express' flights": https://www.washingtontimes.com/news/2016/may/14/bill-clinton-ditched-secret-service-on-multiple-lo/ "The Decline and Fall of an Ultra Rich Online Gaming Empire" a detailed look at the connection between Steve Bannon, and pedophiles Marc Collins-Rector & Brock Pierce: https://www.wired.com/2008/11/ff-ige/ "What Did Cambridge Analytica Actually Do For Trump's Campaign?": https://www.wired.com/story/what-did-cambridge-analytica-really-do-for-trumps-campaign/ "Trump Campaign Distances Itself From Cambridge Analytica After Assange Connections Surface": https://www.vanityfair.com/news/2017/10/trump-campaign-distances-itself-from-cambridge-analytica-after-assange-connection-surfaces "The Fixer: MGM's Eddie Mannix and the lives he ruined": http://www.slate.com/articles/podcasts/you_must_remember_this/2015/11/eddie_mannix_mgm_s_fixer_and_the_scandal_of_patricia_douglas.html Nikolas Schreck's "The Manson File": https://www.amazon.com/Manson-File-Nikolas-Schreck/dp/094169304X Want more Neil Sanders? Check out his website: https://neilsandersmindcontrol.com/ Find him on Facebook: https://www.facebook.com/NeilSandersMC Or check him out on Youtube: https://www.youtube.com/channel/UCAhY_ejguYk7eNafFbV7sew Want to hear more THC? Become a plus member and gain access to the additional hour as well as the THC forums at: http://www.thehighersidechatsplus.com/subscribe/ If you want to stay connected to The Higherside Chats, join us on social media: Facebook: https://www.facebook.com/TheHighersideChatsPodcast/ Twitter: https://twitter.com/HighersideChats Youtube: https://www.youtube.com/user/TheHighersideChats/ Reddit: https://reddit.com/r/highersidechats/ Review us on iTunes: https://itunes.apple.com/podcast/id419458838?mt And be sure to check out The Higherside Clothing: https://thehighersideclothing.com
Views: 12702 TheHighersideChats
In this episode, Christopher Jon Sprigman, Professor of Law at NYU School of Law, discusses his new article "The Second Digital Disruption: Data, Algorithms & Authorship in the 21st Century," which he co-authored with Kal Raustiala. Sprigman describes his consequentialist approach to copyright law and policy, and uses it to focus on recent developments in the production of works of authorship. Specifically, he explains how the pornography industry has collected and managed data about the preferences of its customers to provide them with the content they want to consume, and determine what kinds of content to produce. He reflects on how the pornography industry's use of data is migrating throughout the copyright sector. And he speculates on what this new "disruption" may mean for authorship and creativity in the future. Sprigman is on Twitter at @CJSprigman.
Views: 5 Brian Frye