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Businesses are going through a major change where business operations are becoming predominantly data-intensive. As per studies , more than 2.5 quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. A large part of this enormous growth of data is fuelled by digital economies that rely on a multitude of processes, technologies, systems, etc. to perform B2B operations.
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Data overload is a growing problem for enterprise businesses. Analysis teams must often work manually to navigate seas of data and generate the specific insights their colleagues request.It can take multiple analysts weeks to gather, integrate and process the data they need. As a result, the insights they uncover may no longer useful by the time they’re generated.
The goal of data governance is to ensure the quality, availability, integrity, security, and usability within an organization. The way that you go about this is up to you. Many traditional approaches to data governance seem to struggle in practice; I suspect it is partly because of the cultural impedance mismatch, but also partly because […].
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Billionaire Tilman Fertitta walks into the room. You can’t believe this heavyweight, the CEO and sole owner of multiple restaurant franchises, has given you the time of day. Tilman sits down, settles himself, and glances at the clock. “Well friend,” he says, “I have about three or four minutes before I have to get out of here. What do you wanna know?”.
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Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.
Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. A complementary Domino project is available. Introduction.
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What does a new device, downloading of an app, signing up on a platform all have in common……the “accepting” of t’s & c’s. I, like many other unsuspecting individuals, blindly and unknowingly say “ok” to the t’s & c’s without fully understanding what they really mean or how my data will be used, let alone knowing the potential impact it could have on decisions I don’t make….
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
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GAP's AI-Driven QA Accelerators revolutionize software testing by automating repetitive tasks and enhancing test coverage. From generating test cases and Cypress code to AI-powered code reviews and detailed defect reports, our platform streamlines QA processes, saving time and resources. Accelerate API testing with Pytest-based cases and boost accuracy while reducing human error.
Data science is one of the most promising career paths of the 21st-century. Over the past year, job openings for data scientists increased by 56%. People that pursue a career in data science can expect excellent job security and very competitive salaries. However, a background in data analytics, Hadoop technology or related competencies doesn’t guarantee success in this field.
Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Fenner. There is also a complementary Domino project available. Introduction. Many data scientists deliver value to their organizations by mapping , developing, and deploying an appropriate ML solution to address a business problem.
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Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.
Dear CFO, We heard that you put FP&A on your list of top priorities to work on. We understand that you’re not satisfied with the return on investment you’re getting from the department. You told us that there is too much data, reporting, and analysis and too few real insights that change decisions for the better. You also told us that you don’t feel adequately supported by the FP&A department on a strategic level to be a business partner to the CEO.
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