Data science is a combination of various machine learning principles along with tools and algorithms to analyze raw data and conclude hidden patterns or predictions. Data science does not only provide predictive casual analytics and perspective analytics but also machine learning for making predictions and pattern discovery. With these complex and meaningful analytics, it finds the critical insights out of anything that can help to enhance the value. There are a huge number of blogs that talk about all these data science projects and helps to enlighten its users about the new technology. Data science is an evergrowing field of computer science, and it is difficult to keep pace with the trendy additions all the time. The below-mentioned blogs of data science will help you to keep updated and stay ahead in the competition. After acquiring Datascence.com back in 2018, Oracle started focusing on the utilization of Machine learning for its customers. Oracle always wanted to enable people to leverage the power of AI with the combination of big data and data analytics. This big data blog can be seen as a part of this goal as it emphasizes the impact of big data and AI on various applications of our regular life. Besides, how we can transform the data catalog to get more insight from a business alongside the extraction of business value is discussed in Oracle AI and Data Science Blog. If you are planning to start your career in this field, you can follow this blog as you will get everything that you must understand to become a data scientist in 2020. This Belgium based data science community is publishing big data-related content to minimize the gap between data science and common people since 2015. The blogs are available for free, and you will get all of them in their archives. They are intended to generate solutions for the challenges that we face in our day-to-day life through data analytics. They are focused on educating and empowering people while the scholar and professionals are also included among their target audience. It can be seen as a bridge between academics and business as it highlights the power of big data and the value it can add to any business. NGO workers, business leaders, data enthusiasts, university professors, and also Ph.D. students share their skills and experiences through this blog.

Schools and libraries have been closed for months, but some kids aren't going to get away with playing video games all summer. Kelly Passek -- a middle school librarian in Montgomery County, Virginia -- is sending out summer reading via drones. After using the quadcopters from Wing to get some home essentials, she realized that she could use the service to literally drop some knowledge on local students. Passek does have to resort to some manual labor to get books to kids, though. She takes requests via a Google Form, then packs up the books and drops them off at Wing's facility.

We can enable this toolkit by importing the mplot3d library, which comes with your standard Matplotlib installation via pip. Just be sure that your Matplotlib version is over 1.0. Now that our axes are created we can start plotting in 3D. This course is ranges for a beginner to an expert data scientist that want to learn how to visualize the data in 3 dimensions space, with the popular data visualization library Matplotlib. There is absolutely no pre knowledge requirement for this course.

This Artificial Intelligence for Business course or AI4B for short takes place in the future… Somewhere between 2030 and 2035… Executives taking this course will be helped think of the world 10-15 years into the future. This course is designed to help them integrate into strategy all the emerging technologies, and be aware that their convergence will make the next couple of decades the most disruptive ever. We will cover business scenarios that make use of emerging technologies such as AI, Machine Learning, Natural Language Processing, Computer Vision, Robotics, Drones, Augmented Reality, Virtual Reality, Blockchain, Chatbots, Driverless Systems, and Megacities. The goal is to take a trip into the future so we can figure out what new businesses are worth creating in the present and what steps need to be taken today in your existing businesses so they will thrive in this rapidly evolving environment. Another goal in addition to building your AI-muscle is developing your AI-flexibility.

The transition to online learning due to COVID-19 has exposed significant gaps in our school systems. While there have been many technological advancements in the past decade, the education industry has been slower to adapt. Education institutions now have the opportunity to explore the potential of learning supported by artificial intelligence. There are many social and economic factors that shape learning environments. Even though there are great teachers in some schools, many lack basic resources like textbooks and internet access.

Many important decisions in societies such as school admissions, hiring, or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute. Cases include quotas for women on corporate boards or ethnic quotas in elections. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes we show that quota-based debiasing based on a single attribute can worsen the representation of already underrepresented groups and decrease overall fairness of selection. We use several data sets from a broad range of domains from recidivism risk assessments to scientific citations to assess this effect in real-world settings. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we propose models that dynamically acquire new features to further improve the prediction assessment. To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire. We leverage a generative model, arbitrary conditional flow (ACFlow), to learn the arbitrary conditional distributions required for estimating the information metric. We also learn a Bayesian network to accelerate the acquisition process. Our model demonstrates superior performance over baselines evaluated in multiple settings.

Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node's latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy---defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo---that leverages the posterior distribution's functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district.

We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.

Google's new drone delivery service Wing will help bring library books to school children in Christiansburg, Virginia to help make up for the city's library closures during the COVID-19 pandemic. The new initiative is being overseen by Kelly Passek, a librarian for Montgomery County Public Schools, who first pitched the idea to Wing. Students in Christiansburg can submit a request for books in the school district's library system and Passek will pull the book from the stacks and send it out in one of Wing's custom delivery containers. Google's Wing drone delivery service will now bring library books to school children in Christiansburg, Virginia'I think kids are going to be just thrilled to learn that they are going to be the first in the world to receive a library book by drone,' Passek told The Washington Post. Passek initially got the idea after wondering about how the 600-plus students in the school district were fairing after the county closed school campuses and libraries.