Data science courses are among the most popular globally, with a high likelihood of career prospects, according to the volume of internet searches for skill development or job-oriented courses. Data scientists are needed everywhere. The most fundamental prerequisite for developing any technology in this era of smart technology (which includes smartphones, televisions, watches, etc.) is data, and these data scientists serve as the foundation for machine learning and artificial intelligence specialists. A data scientist will also assist organizations in managing serious crises and assisting them in their resolution through the use of data-driven judgments. Data science is the study of analyzing and obtaining organized, unstructured, and noisy data from various sources. This analysis aids businesses in forecasting outcomes and making data-driven decisions. Data that adheres to a data model, has a clearly defined structure, follows a persistent order, and is simple for both humans and programmes to retrieve is said to be structured data. Unstructured data is not structured in a way that has been predefined, notwithstanding the possibility that it has a native, internal structure. The data is kept in its original format; there is no data model. Media, text, internet activity, monitoring photos, and more are typical instances of large datasets. Data Science – The MUST KNOW to become a successful Data Scientist! How can software engineers and data scientists work together? Corrupted data, a type of unstructured data, is another name for noisy data. It also includes any information that a user's system is unable to effectively analyze and interpret. If handled improperly, noisy data can have a negative impact on the outcomes of any data analysis and skew conclusions. Sometimes, statistical analysis is employed to remove noise from noisy data.

The foreign ministers of Japan and Mexico have agreed on the importance of promoting a rules-based international order, the Japanese government said Friday, as Russia's war in Ukraine continues. During their meeting in Mexico City on Thursday, Foreign Minister Yoshimasa Hayashi and his counterpart, Marcelo Ebrard, also confirmed that the two governments will cooperate closely toward the realization of a "free and open Indo-Pacific." The vision has been advocated by Japan and the United States as a counter to China's growing military influence in the region. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Drone advances in Ukraine have accelerated a long-anticipated technology trend that could soon bring the world's first fully autonomous fighting robots to the battlefield, inaugurating a new age of warfare. The longer the war lasts, the more likely it becomes that drones will be used to identify, select and attack targets without help from humans, according to military analysts, combatants and artificial intelligence researchers. That would mark a revolution in military technology as profound as the introduction of the machine gun. Ukraine already has semi-autonomous attack drones and counter-drone weapons endowed with AI. Russia also claims to possess AI weaponry, though the claims are unproven.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In December, the US Transportation Security Administration (TSA), an agency within Biden's Department of Homeland Security, acknowledged it has significantly expanded facial recognition technology at security checkpoints in airports across the United States. Under the expanded program, 16 of the nation's largest airports are now using face scans as a way to verify the identities of travelers, including in Atlanta, Boston, Denver, and Los Angeles. The TSA's initial test facial recognition program started under the Trump administration in 2017.

Riding the generative AI wave, Picsart, the developer behind various photo and video editing apps for the web and mobile devices, is introducing a new iOS app that transforms photos and drawings into digital art. Called SketchAI, the app lets users sketch a picture or upload an existing image and apply different artistic styles to it. SketchAI is easy enough to use. It features several pre-selected styles that can applied to creations, including ink drawing, pencil sketch, and the artist-inspired "Da Vinci" and "Van Gogh." In addition to sketching or uploading a photo, users can add a prompt describing an image (e.g.

Scientific discoveries and technological innovation play a vital role in addressing many of the challenges and crises that we face every year. The last year may have come and gone quickly, but scientists and researchers have worked painstakingly hard to advance our knowledge within a number of disciplines, industries, and projects around the world. Over the course of 2022, it's easy to lose track of all the amazing stories in science and technology. Below we dive a little deeper into some of the most interesting headlines, while providing links in case you want to explore these developments further. What happened: A new space telescope brings promise of exciting findings and beautiful images from the final frontier.

After all the dreaming, our technologies can still take unexpected turns, amazing and alarming us. As we agonize through another year about whether, as the Christmas carol says, "the wrong shall fail, the right prevail," I've traditionally started each new year with what I've called "a massive MapReduce on the year gone by" -- a lively lightning round of overlooked moments, in a final closing ceremony for the year gone by. But in asking what was truly significant about 2022, are we also highlighting events that foreshadow things to come? Besides technology playing a role in the world's geopolitical conflicts, there was also one unmistakable trend in 2022 that was both haunting and hilarious. It was the advances in both the performance and the accessibility of AI technology.

Over the last two decades, around 300 quasars have been discovered at $z\gtrsim6$, yet only one has identified as being strongly gravitationally lensed. We explore a new approach -- enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion -- which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer.Our search method consists of two main parts: (i) the preselection of the candidates based on their spectral energy distributions (SEDs) using catalog-level photometry and (ii) relative probabilities calculation of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy-quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of $\theta_\mathrm{E} \leq 1$ arcsec. Visual inspection is then performed for sources with CNN scores of $P_\mathrm{lens} > 0.1$, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs that can overcome the veto limitations of primarily dropout-based SED selection approaches.

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways. Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges. Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them. The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility. However, often the public is never informed of how these systems operate and resultant institutional decisions are made. A ``black box'' effect of automated decision-making systems reduces the public's perceptions of integrity and trustworthiness. The result is that the public loses the capacity to identify, challenge, and rectify unfairness or the costs associated with the loss of public goods or benefits. The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system. We formulate an automated decision-making system as a data science lifecycle (DSL) and examine the implications of fiduciary responsibility within the context of the DSL. Fiduciary responsibility within DSLs provides a methodology for addressing the public's lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public. We posit that fiduciary responsibility manifests in several contexts of a DSL, each of which requires its own mitigation of sources of mistrust. To instantiate fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive policing case study is examined.

Decision-makers often act in response to data-driven predictions, with the goal of achieving favorable outcomes. In such settings, predictions don't passively forecast the future; instead, predictions actively shape the distribution of outcomes they are meant to predict. This performative prediction setting raises new challenges for learning "optimal" decision rules. In particular, existing solution concepts do not address the apparent tension between the goals of forecasting outcomes accurately and steering individuals to achieve desirable outcomes. To contend with this concern, we introduce a new optimality concept -- performative omniprediction -- adapted from the supervised (non-performative) learning setting. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule with respect to many possibly-competing objectives. Our main result demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which we call outcome performativity. On a technical level, our results follow by carefully generalizing the notion of outcome indistinguishability to the outcome performative setting. From an appropriate notion of Performative OI, we recover many consequences known to hold in the supervised setting, such as omniprediction and universal adaptability.