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Data Science


7 Types of AI Marketing Tools That Drive Leads

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People in all kinds of industries are debating artificial intelligence (AI) pros and cons. AI pros and cons are going to shape future generations – and, indeed, the whole future of our species. Today, however, science fiction is still just a story. When you're thinking about AI and what it can do, it's important to recognize its limitations. But it's also vital to remember just how quickly it has moved forward. Seeing both sides of the coin lets you really understand where AI is excellent and where it still has serious shortcomings. One thing is for sure: You should be using AI.


How AI proptech can instantly value properties around APAC

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Property technology, often abbreviated as proptech, is becoming increasingly commonplace around the Asia Pacific (APAC) in recent years, and certain factors are set to see proptech truly takeoff. An excellent example of proptech disrupting traditional property investor tools is the AI-powered platform, OfficeBlocks. According to the Urban Land Institute and PwC Emerging Trends in Real Estate Asia Pacific (APAC) 2020 report, awareness of and investment in proptech strategies are growing rapidly. And the recent world events have made things difficult for the property investor market, with travel restrictions and other restrictions limiting the possibilities. But using the Market Intelligence App within the OfficeBlocks platform, a photo of a property can be uploaded, and the industry-first AI and big data tool will send out the rental estimates and valuation of the property to an email address within minutes.


Alternative Python libraries for Data Science

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Dabl library has been created by Andreas Mueller, one of the core developers and maintainers of the scikit-learn machine learning library. The idea behind dabl is to make supervised machine learning more accessible to beginners and reduce boilerplate for common tasks. Dabl takes inspirations from scikit-learn and auto-sklearn. The library is being developed actively and hence isn't recommended for production use. Dabl can be used for automated preprocessing of the dataset, quick EDA as well as initial model building as part of a typical machine learning pipeline.


KDnuggets News 20:n41, Oct 28: Difference Between Junior and Senior Data Scientists; Ain't No Such a Thing as a Citizen Data Scientist - KDnuggets

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See our recent jobs in AI, Analytics, Data Science, Machine Learning You can post a free short entry on KDnuggets jobs page for an industry or academic job related to AI, Big Data, Data Science, or Machine Learning, email - see details at kdnuggets.com/jobs


Services Australia has a JobKeeper data exchange arrangement with the ATO

ZDNet

Services Australia has a data exchange program underway with the Australian Taxation Office (ATO) that flags people who are on the federal government's JobKeeper scheme. "There are some people who haven't declared JobKeeper payments as income on their record," Services Australia deputy CEO, customer service delivery Michelle Lees said. "Based on the data exchange information, we're aware there are approximately 135,000 people who were receiving a social security payment who were identified by an employer as being eligible for JobKeeper. It doesn't necessarily mean, in some instances when we contact them, they might actually say they haven't received a JobKeeper payment, whereby we'd refer that back to the ATO to follow up." Lees said in the event that there was a recalculation of entitlement required, because someone has updated their details, the program could flag that there was a provisional debt.


Global Big Data Conference

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We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI. Credible pilots and use cases have succeeded in many different sectors.


Global Big Data Conference

#artificialintelligence

It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.


Save hundreds on these Python, AI and data science courses

Engadget

In this age of big data, companies worldwide need to sift through the avalanche of information at their disposal to enhance their products, services and overall profitability. Many companies rely on programming languages like Python and the advancements made in artificial intelligence (AI) and data science to get that job done. Right now, you can save hundreds on The Ultimate Python & Artificial Intelligence Certification Bundle, featuring nine in-depth courses and 38 hours of video content that catches you up to speed on everything Python, AI and data science.


4 Powerful Use Cases for Data Science in Finance

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There are a plethora of success stories demonstrating how major financial players capitalise on their data. The coronavirus pandemic and the global measures that have followed have created a perfect economic storm. The financial sector stands at the front line of a growing credit crisis, with banks trying to manage disruption and maintain strict compliance amid social distancing guidelines which are at odds with their processes. Then there are the extraordinarily low interest rates and increasingly cash-insecure consumers to contend with. To navigate the immediate obstacles, financial institutions must assess short-to-medium-term financial risks and adapt to new ways of operating in a post-pandemic world.


Now is the Time for Intelligent Digital Banking Experiences

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Now more than ever, banks and credit unions need to reset their customer experience agenda to meet the needs of consumers who have adjusted their lives as a result of the pandemic. Not only have consumers moved in vast numbers, across all demographic segments, to digital channel use, but they expect organizations to leverage data and insights to provide better solutions that are personalized to their specific needs. Before COVID, the use of digital products, services and engagement tools in banking was increasing at a modest rate. About half of US banking consumers used mobile apps infrequently or not at all, with satisfaction levels being lower than branch interactions. But as the pandemic shut down branches nationwide, the transition to digital happened almost overnight, with consumers going through the needed learning curve as they quickly adapted to the'new normal'. But the transition to digital is not complete – either for the consumer or the financial institution.