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Data Science


EntTelligence, Impression-Based Analytics, Launches With Steve Buck as Partner

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EntTelligence announces its launch which will deliver out-of-home (OOH) marketing analytics to the entertainment community. The company will harness data science, machine learning, and a 30,000-person Field Force armed with content listening technology to analyze and interpret OOH initiatives. Additionally, effective October 1st, former MarketCast, and Comscore executive Steve Buck will join as Partner and Chief Strategy Officer to the newly formed entity. EntTelligence CEO Rakesh Nigam says – "We are incredibly thrilled for our launch. As entertainment-based intelligence becomes ever more critical in a post-pandemic climate, our unique approach succinctly and effectively measures potential movie consumers. Moreover, we are equally excited to have Steve Buck, who has brought revolutionary currencies to market, join and lead our strategic initiatives."


Top 12 Data Science Skills to Learn in 2020

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"We're entering a new world in which data may be more important than software." If you want to stay competitive in this rapidly evolving domain, you need to regularly update your skills with the latest changes. In the following section, we will share the top Data Science skills that not only a practicing Data Scientist would benefit from, but also anyone who's passionate about working his way around large volumes of data. If you code anything at all, we're sure you must've heard about GitHub. GitHub is among the most commonly used tools by the developers today after Stack Overflow.


Why AI projects fail

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Seventy percent of companies have reported minimal or no impact from Artificial Intelligence projects, according to a survey by MIT and Boston Consulting Group. There are a number of reasons for this including a lack of focus on cultural change and training within an organization as it adapts to new working practices, but the most important factor is poor data. This encompasses everything from inadequate data architecture and discovery, to modelling, quality, and governance. If using the analogy that AI is the "icing on the cake", then data is the cake itself. At some point over the next 12 months, with the global recession constraining budgets for every organization, Chief Information Officers and Chief Data Officers will need to demonstrate a clear return on investment for their AI projects and provide evidence of measurable results.


AI gives IT an edge in improving customer experience

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Even prior to the pandemic, improving customer experience was becoming a major priority for IT. COVID-19 and the resulting shift in business models have only accelerated that strategic directive. Delivering a positive customer experience is even more important now, as businesses prepare for a post-pandemic world that will still involve lots of home-based workers, rising e-commerce transactions, and an unprecedented number of digital interactions between companies and their clients. Get the insights by signing up for our newsletters. From healthcare to retail, artificial intelligence is rising to the challenge.


Most Useful C/C++ ML Libraries Every Data Scientist Should Know

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C is ideal for dynamic load balancing, adaptive caching, and developing large big data frameworks, and libraries. Google's MapReduce, MongoDB, most of the deep learning libraries listed below have been implemented using C . Scylla known for its ultra-low latency and extremely high throughput is coded using C acts as a replacement to Apache Cassandra and Amazon DynamoDB. With some of the unique advantages of C as a programming language, (including memory management, performance characteristics, and systems programming), it definitely serves as one of the most efficient tools for developing fast scalable Data Science and Big Data libraries. Further, Julia (a compiled and interactive language – developed from MIT) is emerging as a potential competitor to Python in the field of scientific computing and data processing. Its fast processing speed, parallelism, static along with dynamic typing and C bindings for plugging in libraries, has eased the job for developers/data scientists to integrate and use C as a data science and big data library.


The Basics Behind Building Machine Learning Solutions

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After decades in research labs, machine learning is now getting enormous attention for real-world applications that harness the technology's formidable power to discern patterns in huge quantities and types of data at high speed: fraud detection, customer 360, facial recognition, workflow management, shopping personalization and much more. The payback of such initiatives can be big. But even greater opportunities lie in creating advanced analytic systems that use machine learning's unmatched ability to see, organize and leverage insights from ever-growing mounds of data to unlock the deep, transformative potential of Big Data and the Internet of Things. To get to the next level of machine learning, companies must develop a sound business case; implement machine learning algorithms for speed at scale; use systems equipped with processors with multiple integrated cores, faster memory subsystems, and develop architectures that can handle massive amounts data in real time. For many organizations, it is an ideal time to build on or begin machine-learning experience, deepen knowledge, and reap the benefits and competitive advantages this sophisticated data analytics technology can provide.


BI is dead; long live BI

ZDNet

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. The perception of legacy enterprise business intelligence (BI) platforms comes with some legitimate stigma and baggage. It's technology first, not business-led; the graphical user interface (GUI)-based user experience (UX) doesn't address ease of use for all business decision-makers; there are too many underutilized reports and dashboards floating around in the enterprise; and signals produced by BI applications aren't actionable, resulting in a disconnect between BI and tangible business outcomes. So, is enterprise BI dead? If I got $1,000 every time I heard the phrase "BI is dead" over my 30-plus-year career, I'd be a very rich man.


Global Big Data Conference

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Machine learning (ML) is quickly becoming a mainstay of the enterprise business world, yet entrepreneurs and small-business owners may shy away from investing in it. While you may not fully understand the ins and outs of ML or how it can benefit your small business, you can still make effective use of the technology without being an expert in it. We asked a panel of Forbes Technology Council members to share some smart ways entrepreneurs and small-business owners can leverage ML. Most ML models will require tons of data (the majority of them require supervised learning), which translates into a large effort that most entrepreneurs and small-business owners can't sustain. One approach is to leverage SaaS/PaaS services, such as the AWS portfolio of pre-trained artificial intelligence (AI) services: Comprehend, Rekognition, Lex, Personalize, Translate, Polly and others, each tailored to a specific domain.


Global Big Data Conference

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With a large percentage of the global workforce based remotely for the foreseeable future, more business than ever is being conducted over email. And while this modern convenience has been critical to the continued operation of many businesses in the current health crisis, it has also presented those businesses with new data security challenges. The unfamiliar environment of remote work -- not to mention its potential distractions, like children and pets -- leaves employees more vulnerable to misdirected emails and other mistakes that can lead to accidental data breaches. Scams aimed at both individuals and organizations (even healthcare facilities on the front lines of the pandemic have not been immune to their efforts) have also risen, attempting to capitalize on the situation. Accidental breaches are notoriously difficult to combat because they can be caused by something as simple as a typo in an email address.


Global Big Data Conference

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Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.