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Global Big Data Conference

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A Columbia University research team affiliated with the Data Science Institute (DSI) has received a Facebook Probability and Programming research award to develop static analysis methods that will enhance the usability and accuracy of probabilistic programming. The team includes Jeannette M. Wing, DSI's Avanessians Director and Professor of Computer Science; Andrew Gelman, Professor of Statistics and Political Science and DSI member; and Ryan Bernstein, a doctoral student in computer science who is co-advised by Wing and Gelman. The three will conduct a static analysis of Stan, an open-source probabilistic language program developed mainly at Columbia that describes statistical models. Their analysis will make it easier for users to reliably design statistical and machine learning models in high-level programming languages, according to Gelman, who is a co-principal investigator on the award. "Stan is used in applications ranging from drug development [for Novartis] to political polling and forecasting [for YouGov and The Economist]," Gelman said.


What is Data Science? A Complete Data Science Tutorial for Beginners - DataFlair

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Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science. So, let's start Data Science Tutorial. "Data Science is about extraction, preparation, analysis, visualization, and maintenance of information.


AI: The New Order of Business - InformationWeek

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As we begin to slowly emerge from behind the shadow of COVID-19, the virus has brought new meaning to words like adaptability, mitigation and recovery for business. Although we've had no choice but to scramble to operate in a new world of distributed labor forces, we have at our disposal dynamic technologies and innovations that have helped us through -- not the least of which is artificial intelligence. With automation as a foundation, we are seeing a growing number of organizations bringing AI to bear on areas of the business that are individually distinct but transcend industries. Each of these operations is vital to the health and success of the company and even more so in times of disruption. Perhaps one of the more overlooked operations has been the IT infrastructure.


2020 AWS SageMaker, AI and Machine Learning Specialty Exam

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Timed Practice Exam is coming soon! New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. AWS Artificial Intelligence material is now live! Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.


How machine learning finds anomalies to catch financial cybercriminals

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In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.


How machine learning combats financial cybercrime

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In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.


Boosting the Assembly and Deployment of Artificial Intelligence Solutions with KNIME Visual Data Science Tools Amazon Web Services

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With rapid advancements in machine learning (ML) techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. A study conducted by PwC Global concludes that by the end of this decade, the total positive impact of artificial intelligence (AI) on the global economy could be above $15 trillion, driven mostly by enhancements in consumer products. To make that happen, however, businesses must make strategic investments in the type of technology that moves AI projects into production (productionizing) and helps customers deploy them. Unfortunately, PwC's survey reveals the percentage of executives planning to deploy AI has gone down from 20 percent a year ago to only 4 percent at the beginning of 2020. The primary reason for this decrease is the gap between the growing volume of data and data-driven modeling capabilities, and the necessary skills and toolsets.


11 Best Online Statistics Courses and Tutorials 2020 JA Directives

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Are you looking for the Best Online Statistics Courses? Get everything you'd want to know about descriptive and inferential statistics with these statistics training's. Learning statistics is a must for a data scientist. If you want to learn computer science, you will need to know the statistics as well. Do you know, What is the importance of statistics?


How To Tell Your Kids About Data

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Of late, few things make me cringe more than the mischaracterization of the word "data," particularly with regards to its variants in Data Science, Data Analytics, Big Data, and the like. However, I don't fault the perpetrators: data's influence on your life is increasing at a rate much faster than that at which efforts are being made to educate the majority of people on what it is and how it is translated into actionable insights. At this point, most of us realize that properly-leveraged data has many lucrative use-cases. In turn, businesses and organizations are seeking to tap into this movement without always knowing what its tangible benefits will be for their specific operations. I don't mean to echo any Revere-esque rhetoric to the tune of "The Data are coming!


Variational Bayes In Private Settings (VIPS)

Journal of Artificial Intelligence Research

Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB's approximate posterior distributions for models in the CE family, by perturbing the expected sufficient statistics of the complete-data likelihood. For a broadly-used class of non-CE models, those with binomial likelihoods, we show how to bring such models into the CE family, such that inferences in the modified model resemble the private variational Bayes algorithm as closely as possible, using the Pólya-Gamma data augmentation scheme. The iterative nature of variational Bayes presents a further challenge since iterations increase the amount of noise needed. We overcome this by combining: (1) an improved composition method for differential privacy, called the moments accountant, which provides a tight bound on the privacy cost of multiple VB iterations and thus significantly decreases the amount of additive noise; and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning. We empirically demonstrate the effectiveness of our method in CE and non-CE models including latent Dirichlet allocation, Bayesian logistic regression, and sigmoid belief networks, evaluated on real-world datasets.