#future scope of data science

[data-science-course-12]

The Future scope of Data Science 

Explore the promising future scope of data science, a rapidly growing field that empowers businesses with data-driven insights. Learn how data science professionals are in high demand across industries. Discover the best data science course in Rohini, Delhi, to evolve new skills. 

[mohitreal1995]

Future Scope of Data Science

Data science is the study of data to extract meaningful insights for businesses, utilizing tools like data visualization, machine learning, and statistical analysis. Data scientists clean and process data, analyze it for patterns, build models, and validate their accuracy. Essential skills include programming, statistics, machine learning, data visualization, problem-solving, and effective communication. The field's future scope is promising, with key areas like AI, IoT, personalization, healthcare, and cybersecurity benefiting from data science's data-driven approach. This growing demand makes data science a lucrative and essential career path, providing valuable skills and opportunities. Read in detail, Click on the link: https://kajaldigital.livepositively.com/future-scope-of-data-science/

[rjzimmerman]

Excerpt from this story from the New York Times:

For years, politicians, industry groups and environmentalists have argued over which bodies of water in the United States should fall under the jurisdiction of the Clean Water Act, a sweeping law, passed in 1972, that allows the Environmental Protection Agency to limit water pollution. While there’s consensus that the law applies to major rivers and lakes, there’s debate over whether federal protections should apply elsewhere, such as to nearby wetlands or streams that go dry for part of the year.

Environmentalists favor broad protections, arguing that these other bodies of water are important; homebuilders, some industry groups and conservatives oppose what they see as regulatory overreach.

In May 2023, the Supreme Court voted 5 to 4 to restrict the scope of the Clean Water Act, with the majority ruling that the law should apply only to “relatively permanent, standing or continuously flowing bodies of water,” as well as to wetlands that have “a continuous surface connection” to those waters.

That ruling effectively ended federal protections for up to 4.9 million miles of streams that flow only when it rains, according to officials at the E.P.A., which announced in August that it would follow the court’s guidance.

These temporary streams are often overlooked since they may look like unremarkable dry ditches for much of the year, said Jud Harvey, a senior research hydrologist for the United States Geological Survey, who wrote a separate commentary on the Science study. “But when it rains,” he said, “these streams convey a substantial amount of water” that ends up in rivers and lakes.

Mr. Brinkerhoff and his colleagues identified millions of ephemeral streams across the country and used detailed modeling to estimate how much water flows through them.

In the West, ephemeral streams flow only for four to 46 days per year on average, but contribute up to 79 percent of the downstream river flow, the study found. Ephemeral streams contribute roughly 55 percent of the flow in river basins across the contiguous United States, on average.

Mr. Harvey said he was surprised by the amount of water originating from ephemeral streams. “But it is a rigorous and detailed investigation using the best available data in the United States,” he said of the study.

Because so much water passes through these streams, the study notes, it matters greatly whether or not they are polluted. Sediments or excess phosphorus from fertilizer run off on farms could accumulate in dry channels until a heavy rainstorm picks up the pollutants and washes them into larger waterways.

Mr. Brinkerhoff said that the study did not try to quantify how much pollution was actually passing through those streams. That is a subject for future research. But, he said, these streams have a large influence on water quality.

[sophieinwonderland]

I wonder if there's a conflating of psychiatry and psychology happening in the beef anti-psych-pro-endo is having with you.

Like, one is a study of human cognition and behavior, the other is medical treatment of that subject.

Being anti-psych ourselves, we absolutely want to see the abolishing of psychiatry as it stands now, in favor of more durable and equitable systems of care. But the study you mused on could exist in such a world.

Idk what kind of world produces compassionate and consensual mental health support without having a robust psychology field. How does a community reduce harm unless they know what is and isn't harmful, or know what is and isn't harm? Thinking here of bigots weaponizing pseudoscience and inclusive language to promote and defend their bigotry. Or even just folks denying supports because they don't understand the need for said supports.

Maybe we're just fundamentally misunderstanding the two fields or the conversation or something. But it irks us that the anti-psych position is being anti-science.

This is a good point.

Anti-psych, as I understand, is generally short for anti-psychiatry not anti-psychology. And they seem to be pretty heavily conflating the two.

For context, I'm going to link to their own explanation of their views on psychology and psychiatry so I don't end up misconstruing anything.

I know that psychiatry is typically defined as involving medication as well as general "mental health", while psychology is more typically only things like therapy and other non-medicinal approaches to "mental health". In my opinion, however, anti-psych covers both, because both are used to harm neurodivergent people. Medication is both coercively prescribed under threat of institutionalization and withheld, and patients are not educated on the full scope of how the medication can both help and harm their bodyminds. Therapy is used to force neuroconformity - from well-known harmful examples such as ABA therapy, to lesser acknowledged examples such as CBT being used to condition patients into exhibiting "socially acceptable" thought and behavior patterns at the expense of their mental health; typically also blaming and punishing the patient when they fall short of the intended goals due to their own symptoms.

Now, maybe there can be some truth to this if we're solely discussing clinical psychology.

But psychology is a pretty broad field that goes way beyond "mental health."

There are experimental psychologists, neuropsychologists, forensic psychologists, engineering psychologists, educational psychologists, developmental psychologists, and many others.

Psychology is a massive field of study. I would even go so far as to say that most psychology ISN'T about mental health at all.

And most of the psychologists at this hypothetical school wouldn't be clinical psychologists. They would be developmental psychologists to study the development of the children in this environment.

This isn't really psychiatry, nor is it clinical psychology. It's just research. It's studying children in a unique environment with a different culture that doesn't exist anywhere else.

And I really agree with the point about pseudoscience. If you get in the way of research and prevent studies from being conducted, then that's all that you'll be left with. And if you take a position that children can't be studied because they can't possible be able to consent to the study, then that means we'll lack valuable data that can be used to help children.

Speaking of conducting psychological experiments on children, there was actually a really cool one conducted on children (with parents consent) showing they could create imaginary companions intentionally, and that those imaginary companions

https://onlinelibrary.wiley.com/doi/full/10.1002/icd.2390

And this study directly references tulpas as something to compare with in future studies.

These are cool findings that wouldn't be possible if we just decided children shouldn't be studied.

I need to do a more in-depth post on that study later.

[spacetimewithstuartgary]

How NASA's Lunar Trailblazer will make a looping voyage to the moon

Before arriving at the moon, the small satellite mission will use the gravity of the sun, Earth, and moon over several months to gradually line up for capture into lunar orbit.

NASA's Lunar Trailblazer arrived in Florida recently in advance of its launch later this month and has been integrated with a SpaceX Falcon 9 rocket. Shipped from Lockheed Martin Space in Littleton, Colorado, the small satellite is riding along on Intuitive Machines' IM-2 launch—part of NASA's CLPS (Commercial Lunar Payload Services) initiative—which is slated for no earlier than Thursday, Feb. 26, from Launch Complex 39A at the agency's Kennedy Space Center.

Approximately 48 minutes after launch, Lunar Trailblazer will separate from the rocket and begin its independent flight to the moon. The small satellite will discover where the moon's water is, what form it is in, and how it changes over time, producing the best-yet maps of water on the lunar surface.

Observations gathered during its two-year prime mission will contribute to the understanding of water cycles on airless bodies throughout the solar system while also supporting future human and robotic missions to the moon by identifying where water is located.

Key to achieving these goals are the spacecraft's two state-of-the-art science instruments: the High-resolution Volatiles and Minerals moon Mapper (HVM3) infrared spectrometer and the Lunar Thermal Mapper (LTM) infrared multispectral imager. The HVM3 instrument was provided by NASA's Jet Propulsion Laboratory in Southern California and LTM was built by the University of Oxford.

"The small team is international in scope, which is more typical of larger projects," said Andy Klesh, Lunar Trailblazer's project systems engineer at JPL. "And unlike the norm for small missions that may only have a very focused, singular purpose, Lunar Trailblazer has two high-fidelity instruments onboard. We are really punching above our weight."

Intricate navigation

Before it can use these instruments to collect science data, Lunar Trailblazer will for several months perform a series of moon flybys, thruster bursts, and looping orbits. These highly choreographed maneuvers will eventually position the spacecraft so it can map the surface in great detail.

Weighing only 440 pounds (200 kilograms) and measuring 11.5 feet (3.5 meters) wide when its solar panels are fully deployed, Lunar Trailblazer is about the size of a dishwasher and has a relatively small engine. To make its four-to-seven-month trip to the moon (depending on the launch date) as efficient as possible, the mission's design and navigation team has planned a trajectory that will use the gravity of the sun, Earth, and moon to guide the spacecraft—a technique called low-energy transfer.

"The initial boost provided by the rocket will send the spacecraft past the moon and into deep space, and its trajectory will then be naturally reshaped by gravity after several lunar flybys and loops around Earth. This will allow it to be captured into lunar orbit with minimal propulsion needs," said Gregory Lantoine, Lunar Trailblazer's mission design and navigation lead at JPL. "It's the most fuel-efficient way to get to where we need to go."

As it flies past the moon several times, the spacecraft will use small thruster bursts—aka trajectory correction maneuvers—to slowly change its orbit from highly elliptical to circular, bringing the satellite down to an altitude of about 60 miles (100 kilometers) above the moon's surface.

Arriving at the moon

Once in its science orbit, Lunar Trailblazer will glide over the moon's surface, making 12 orbits a day and observing the surface at a variety of different times of day over the course of the mission. The satellite will also be perfectly placed to peer into the permanently shadowed craters at the moon's South Pole, which harbor cold traps that never see direct sunlight. If Lunar Trailblazer finds significant quantities of ice at the base of the craters, those locations could be pinpointed as a resource for future lunar explorers.

The data the mission collects will be transmitted to NASA's Deep Space Network and delivered to Lunar Trailblazer's new operations center at Caltech's IPAC in Pasadena, California. Working alongside the mission's experienced team will be students from Caltech and nearby Pasadena City College who are involved in all aspects of the mission, from operations and communications to developing software.

Lunar Trailblazer was a selection of NASA's SIMPLEx (Small Innovative Missions for Planetary Exploration), which provides opportunities for low-cost science spacecraft to ride-share with selected primary missions. To maintain the lower overall cost, SIMPLEx missions have a higher risk posture and lighter requirements for oversight and management. This higher risk acceptance allows NASA to test pioneering technologies, and the definition of success for these missions includes the lessons learned from more experimental endeavors.

"We are a small mission with groundbreaking science goals, so we will succeed by embracing the flexibility that's built into our organization," said Lee Bennett, Lunar Trailblazer operations lead with IPAC. "Our international team consists of seasoned engineers, science team members from several institutions, and local students who are being given the opportunity to work on a NASA mission for the first time."

TOP IMAGE: NASA’s Lunar Trailblazer approaches the moon as it enters its science orbit in this artist’s concept. The small satellite will orbit about 60 miles (100 kilometers) above the lunar surface, producing the best-yet maps of water on the moon. Credit: Lockheed Martin Space

LOWER IMAGE: Lunar Trailblazer’s voyage to the moon will take between four and seven months, depending on the day it launches. This orbital diagram shows the low-energy transfer trajectory of the NASA mission should it launch on Feb. 26, the earliest date in its launch period. Credit: NASA/JPL-Caltech

[pandeypankaj]

What's the difference between Machine Learning and AI?

Machine Learning and Artificial Intelligence (AI) are often used interchangeably, but they represent distinct concepts within the broader field of data science. Machine Learning refers to algorithms that enable systems to learn from data and make predictions or decisions based on that learning. It's a subset of AI, focusing on statistical techniques and models that allow computers to perform specific tasks without explicit programming.

On the other hand, AI encompasses a broader scope, aiming to simulate human intelligence in machines. It includes Machine Learning as well as other disciplines like natural language processing, computer vision, and robotics, all working towards creating intelligent systems capable of reasoning, problem-solving, and understanding context.

Understanding this distinction is crucial for anyone interested in leveraging data-driven technologies effectively. Whether you're exploring career opportunities, enhancing business strategies, or simply curious about the future of technology, diving deeper into these concepts can provide invaluable insights.

In conclusion, while Machine Learning focuses on algorithms that learn from data to make decisions, Artificial Intelligence encompasses a broader range of technologies aiming to replicate human intelligence. Understanding these distinctions is key to navigating the evolving landscape of data science and technology. For those eager to deepen their knowledge and stay ahead in this dynamic field, exploring further resources and insights on can provide valuable perspectives and opportunities for growth 

[scopethings-blog]

Scope Computers

Unlock Your Future with Data Science!

Master data analysis, visualization 📊, and machine learning 🤖 with hands-on training and real-world projects 🚀. Gain in-demand skills to access high-paying careers 💼 and solve complex problems using data-driven insights. Start today and lead the digital revolution! 🌟

[elsa16744]

Big Data and AI: The Perfect Partnership for Future Innovations 

Innovation allows organizations to excel at differentiation, boosting competitive advantages. Amid the growth of industry-disrupting technologies, big data analytics and artificial intelligence (AI) professionals want to support brands seeking bold design, delivery, and functionality ideas. This post discusses the importance of big data and AI, explaining why they matter to future innovations and business development. 

Understanding Big Data and AI 

Big data is a vast data volume, and you will find mixed data structures because of continuous data collection involving multimedia data objects. A data object or asset can be a document, an audio track, a video clip, a photo, or identical objects with special file formats. Since big data services focus on sorting and exploring data objects’ attributes at an unprecedented scale, integrating AI tools is essential. 

Artificial intelligence helps computers simulate human-like thinking and idea synthesis capabilities. Most AI ecosystems leverage advanced statistical methods and machine learning models. Their developers train the AI tools to develop and document high-quality insights by processing unstructured and semi-structured data objects. 

As a result, the scope of big data broadens if you add AI integrations that can determine data context. Businesses can generate new ideas instead of recombining recorded data or automatically filter data via AI-assisted quality assurances. 

Why Are Big Data and AI Perfect for Future Innovations? 

1| They Accelerate Scientific Studies  

Material sciences, green technology projects, and rare disorder research projects have provided humans with exceptional lifestyle improvements. However, as markets mature, commoditization becomes inevitable. 

At the same time, new, untested ideas can fail, attracting regulators’ dismay, disrespecting consumers’ beliefs, or hurting the environment. Additionally, bold ideas must not alienate consumers due to inherent complexity. Therefore, private sector stakeholders must employ scientific methods to identify feasible, sustainable, and consumer-friendly product ideas for brand differentiation.  

AI-powered platforms and business analytics solutions help global corporations immediately acquire, filter, and document data assets for independent research projects. For instance, a pharmaceutical firm can use them during clinical drug formulations and trials, while a car manufacturer might discover efficient production tactics using AI and big data. 

2| Brands Can Objectively Evaluate Forward-Thinking Business Ideas 

Some business ideas that a few people thought were laughable or unrealistic a few decades ago have forced many brands and professionals to abandon conventional strategies. Consider how streaming platforms’ founders affected theatrical film releases. They have reduced the importance of box office revenues while increasing independent artists’ discoverability. 

Likewise, exploring real estate investment opportunities on a tiny mobile or ordering clothes online were bizarre practices, according to many non-believers. They also predicted socializing through virtual reality (VR) avatars inside a computer-generated three-dimensional space would attract only the tech-savvy young adults. 

Today, customers and investors who underestimated those innovations prefer religiously studying how disrupting startups perform. Brands care less about losing money than missing an opportunity to be a first mover for a niche consumer base. Similarly, rejecting an idea without testing it at least a few times has become a taboo. 

Nobody can be 100% sure which innovation will gain global momentum, but AI and big data might provide relevant hints. These technologies are best for conducting unlimited scenario analyses and testing ideas likely to satisfy tomorrow’s customer expectations. 

3| AI-Assisted Insight Explorations Gamifies Idea Synthesis 

Combining a few ideas is easy but finding meaningful and profitable ideas by sorting the best ones is daunting. Innovative individuals must embrace AI recommendations to reduce time spent on brainstorming, product repurposing, and multidisciplinary collaborations. Furthermore, they can challenge themselves to find ideas better than an AI tool. 

Gamification of brainstorming will facilitate a healthy pursuit of novel product features, marketing strategies, and customer journey personalization. Additionally, incentivizing employees to leverage AI and big data to experiment with designing methods provides unique insights for future innovations. 

4| You Can Optimize Supply Chain Components with Big Data and AI Programs 

AI can capture extensive data on supply chains and offer suggestions on alternative supplier relations. Therefore, businesses will revise supply and delivery planning to overcome the flaws in current practices. 

For instance, Gartner awarded Beijing’s JD.com the Technology Innovation Award in 2024 because they combined statistical forecasting. The awardee has developed an explainable artificial intelligence to enhance its supply chain. Other finalists in this award category were Google, Cisco, MTN Group, and Allina Health. 

5| Academia Can Embrace Adaptive Learning and Psychological Well-Being 

Communication barriers and trying to force all learners to follow the standard course material based on a fixed schedule have undermined educational institutions’ goals worldwide. Understandably, expecting teachers to customize courses and multimedia assets for each student is impractical and humanly infeasible. 

As a result, investors, policymakers, parents, and student bodies seek outcome-oriented educational innovations powered by AI and big data for a learner-friendly, inclusive future. For instance, some edtech providers use AI computer-aided learning and teaching ecosystems leveraging videoconferencing, curriculum personalization, and psycho-cognitive support. 

Adaptive learning applications build student profiles and segments like marketers’ consumer categorizations. Their AI integrations can determine the ideal pace for teaching, whether a student exhibits learning disabilities, and whether a college or school has adequate resources. 

Challenges in Promoting Innovations Based on Big Data and AI Use Cases 

Encouraging stakeholders to acknowledge the need for big data and AI might be challenging. After all, uninformed stakeholders are likely to distrust tech-enabled lifestyle changes. Therefore, increasing AI awareness and educating everyone on data ethics are essential. 

In some regions, the IT or network infrastructure necessary for big data is unavailable or prone to stability flaws. This issue requires more investments and talented data specialists to leverage AI tools or conduct predictive analyses. 

Today’s legal frameworks lack provisions for regulating AI, big data, and scenario analytics. So, brands are unsure whether expanding data scope will get public administrators’ approvals. Lawmakers must find a balanced approach to enable AI-powered big data innovations without neglecting consumer rights or “privacy by design” principles. 

Conclusion 

The future of enterprise, institutional, and policy innovations lies in responsible technology implementations. Despite the obstacles, AI enthusiasts are optimistic that more stakeholders will admire the potential of new, disruptive technologies. 

Remember, gamifying how your team finds new ideas or predicting the actual potential of a business model necessitates AI’s predictive insights. At the same time, big data will offer broader perspectives on global supply chains and how to optimize a company’s policies. 

Lastly, academic improvements and scientific research are integral to developing sustainable products, accomplishing educational objectives, and responding to global crises. As a result, the informed stakeholders agree that AI and big data are perfect for shaping future innovations.  

[startedwellthatsentence]

I read a lot of science fiction novels, especially near-future hard-ish sci fi about first contact. It’s one of my favorite genres.

It also absolutely underlines for me why scientific inquiry MUST be a team effort. The authors will, if they’re doing their job well, present a bunch of scientific evidence connected to a new phenomenon. Perhaps the MC will do a few rounds of different experiments, and then come to a conclusion at the end. Or sometimes they’ll experiment, theorize, experiment, theorize, experiment, theorize, etc. Sometimes they’ll even explain in detail why some logical conclusion actually isn’t correct (suck it nerdboys I’m ahead of you*).

I’m a social scientist, a linguist with a background in gender studies and cultural studies who works as a second language instructor. I have a pretty good grasp of the scientific concepts these books like to play with, but they aren’t my specialty. So when they are doing these experiments, I am coming to *different conclusions* and wanting them to *conduct different experiments*. “Your evidence could be explained by these three other models!” I scream in the group discord. “Your conclusions aren’t fully supported, you need to do more tests!” “Not only are your postulates Terracentric, they’re Anglocentric! There are other cultures on our own damn planet that exhibit this ‘unexplainable alien behavior’!!!”

And that’s a perfectly valid plot point for a lone scientist, that their myopic view is narrowing what they can see of the world and therefore limiting the scope of the data they collect and causing them to draw questionable conclusions. The problem is that the authors tend to then have them be correct about everything they theorized. They did the science and now we’re done and we can move on to the plot. Meanwhile I’m either bitching “an anthropologist and an ecologist would have wildly different takes on this???” Or (looking at you, Arrival), “why is a fucking translator of a previously-studied language doing this work at all? Why don’t we have a rogue formalist syntactician who studies signed languages? Or a fieldworker doing documentation and description in South India, Papua New Guinea, or the Amazon River Basin? Or all of them in a room together?”

This is one of the reasons that I enjoy Brandon Sanderson novels so much, I think. Sure, every single one of them has the same plot twist: “your [physical/magical/political/interpersonal/historical/cosmological] model of the world is wrong, the truth is _____.” But that definitely fulfills my itch for theoretical models of the world to actually work like models instead of laws. Contemporary descriptions of the world may match the results of experiments, but that doesn’t mean that they’re necessarily correct in their totality. Time To Orbit: Unknown by @derinthescarletpescatarian is also pretty good at having the characters come to conclusions with limited data and then facing the consequences of that.

This isn’t a full thought, just something that occurs to me frequently when reading new sci-fi. Put your scientists in teams so they can think of different questions and supply different answers. So that I don’t have to yell at the MCs all alone.

*Andy Weir in Project Hail Mary came across as particularly defensive in his scientific explanations, but never about the things that I was questioning.

[kiriti2009]

Exploring the Outer Solar System with NASA's New Horizons

NASA’s New Horizons spacecraft, launched in 2006 with a primary mission to explore Pluto, continues to push the boundaries of our understanding of the outer solar system. Recently, NASA announced an exciting update to extend New Horizons’ mission beyond its original scope, focusing on further exploration of the Kuiper Belt and beyond.

Mission Extension and Objectives

In fiscal year 2025, New Horizons will transition to a low-activity mode optimized for gathering unique heliophysics data. This strategic shift allows the spacecraft to conserve fuel and reduce operational complexity while awaiting potential future missions, particularly targeting Kuiper Belt objects. Although no specific object is currently targeted, the flexibility of New Horizons’ extended mission allows for readiness in case a suitable candidate is identified.

Nicola Fox, associate administrator for NASA’s Science Mission Directorate, highlighted the significance of this mission extension: "The New Horizons mission has a unique position in our solar system to answer important questions about our heliosphere and provide extraordinary opportunities for multidisciplinary science for NASA and the scientific community."

Funding and Management

NASA’s Planetary Science Division will primarily fund the extended mission, with joint management from NASA’s Heliophysics and Planetary Science Divisions. This decision reflects NASA’s commitment to maximizing scientific returns from existing missions while carefully managing resources across its portfolio.

Historical Achievements and Future Prospects

Since its launch, New Horizons has revolutionized our understanding of distant celestial bodies. Beyond Pluto, it conducted a historic flyby of Arrokoth, a double-lobed Kuiper Belt object, offering insights into the early formation of our solar system. Managed by the Johns Hopkins University Applied Physics Laboratory, the mission exemplifies international collaboration and scientific excellence in space exploration.

Conclusion

As New Horizons continues its journey into the unknown reaches of the outer solar system, it promises further discoveries that will shape our understanding of planetary formation and evolution. With ongoing support from NASA and the scientific community, New Horizons remains a beacon of exploration, pushing the boundaries of human knowledge further than ever before.

Stay tuned as we follow New Horizons on its extended mission, uncovering the mysteries of our solar system’s distant frontier.

[kshemageneralinsurance]

Kshema is India’s Digital Insurance Company For Agriculture & Food Sectors

Kshema is India’s digital Insurance company that caters predominantly to cultivators of the Agriculture & Food Sectors. Our mission is to enable resilience among cultivators across the country, from income shocks due to extreme climate events and other vagaries, with insurance.

I AGRI — our proprietary location-aware platform that assesses and prices risk adequately allows us to address pre-harvest risks at a farm level. Developed in partnership with leading global experts in Spatial data sciences, actuaries, financial engineers and remote sensing experts among others, the platform allows us the benefit of drawing evidence-based results to expand the scope of our services beyond traditional risk analyses.

• A D2C rural insurance company with emphasis on creating products for cultivators.

• Kshema would create insurance solutions, through geo-tagging for many location-specific pre-harvest risks faced by cultivators.

• We aim to incentivize sustainable cultivation practices contributing to a ‘Net Zero Emissions Planet’ and Sustainable development Goals in the future.

• A well-capitalized company led by promoters with a progressive vision and a growth mindset.

• Kshema is an equal opportunities provider. Whether it’s our employees, vendors or partners, we only consider their merit and the value they create.We are changing the world, one farmer at a time — if you are passionate about creating impact on a global scale — join our journey.

[data-science-course-12]

Learn about the future scope of Data Science

Explore the bright future of data science with a data science course in Rohini, Delhi. Discover the vast opportunities in the world of data-driven insights and become a skilled data scientist. Gain expertise in data analysis, machine learning, and statistical methods to make informed decisions. Embrace the digital revolution and stay ahead with the best data science course in Rohini, Delhi.

[mariacallous]

(JTA) — Claudia Goldin, a Jewish scholar at Harvard University, won the Nobel Prize for Economics for her work tracking the disparity in earnings for women in the labor market.

“Claudia Goldin has trawled the archives and collected over 200 years of data from the U.S., allowing her to demonstrate how and why gender differences in earnings and employment rates have changed over time, the Royal Swedish Academy of Sciences said Monday in its release.

Goldin, 77, shattered a glass ceiling in 1989 when she became the first tenured tenured women professor in Harvard’s Economics Department. “I think it will be very good for Harvard to have a tenured woman, and I’m pleased to be that woman,” Goldin said at the time, speaking the the campus student newspaper, the Crimson.

Her seminal work, “Understanding the Gender Gap — An Economic History of American Women” published in 1992, sought to prove that opportunity for women in the labor market was less a function of changing social mores or economic growth, than it was susceptible to a variety of factors, including a woman’s age, her education and expectations of mothers.

“Goldin showed that female participation in the labor market did not have an upward trend over this entire period, but instead forms a U-shaped curve,” the Academy said in its release, referring to the 200-year scope of Goldin’s research.

“The participation of married women decreased with the transition from an agrarian to an industrial society in the early nineteenth century, but then started to increase with the growth of the service sector in the early twentieth century,” it said.

At the press conference announcing the award, Jakob Svensson, the chairman of the prize committee, said Goldin helped elucidate why women remain “vastly underrepresented” in the labor market despite being better educated.

“Understanding women’s role in the labor market is important in society, not the least because if women do not have the same opportunity as men, or they participate on unequal terms, labour, skills and talent go wasted,” said Svensson, a professor of economics at Stockholm University. “Thanks to Professor Goldin’s groundbreaking research we know much more about the underlying facts driving women’s labor market outcomes and which barriers may need to be addressed in the future.”

An attempt by the Nobel committee to reach Goldin by phone at the press conference failed. Prize organizers said they had spoken with her earlier and that she was “surprised and glad.”

Goldin’s most recent work, “Career and Family: Women’s Century-Long Journey toward Equity,” published in 2021, discusses “why true equity for dual career couples remains frustratingly out of reach,” according to Goldin’s Harvard University biography pages.

“Antidiscrimination laws and unbiased managers, while valuable, are not enough,” says Goldin’s precis of her book. “‘Career and Family’ explains why we must make fundamental changes to the way we work and how we value caregiving if we are ever to achieve gender equality and couple equity.”

Goldin, a graduate of the Bronx High School of Science in New York City, has also looked into the historic role of Jews in the marketplace. The authors of a 2003 Boston University paper, “From Farmers to Merchants: A Human Capital Interpretation of Jewish Economic History,” consulted with Goldin.

A 2001 paper she cowrote for the National Bureau of Economic Research on the tendency of women to retain their surname when married, was based in part on an analysis of wedding announcements in the New York Times in 1991. A third of the religious ceremonies were Jewish, the authors noted, “not surprising given the location” of the newspaper. Religious ceremonies for Jews and for others, they found, were likelier to correlate to women changing their name.

Goldin devotes two pages on her Harvard University biography site to her golden retrievers: Prairie, who died in 2009, and Pika, who is 13. She lists his distinctions, as a therapy dog and as a winner of the Excellent Title in Performance Scent Dogs. Her most recent posting marks his “bark mitzvah.”

Also distinguished is her husband, Lawrence Katz, who is a professor of economics at Harvard.

Among the recipients of last year’s Economics Nobel was Ben Bernanke, the Jewish former chairman of the federal reserve.

[vivekavicky12]

Decoding Data Science: Unveiling the Vast Landscape and Career Opportunities

In the era of big data, Data Science has emerged as a transformative force, reshaping industries and driving innovation. This blog aims to unravel the essence of Data Science, exploring its multidisciplinary nature and shedding light on the extensive scope and promising career opportunities within this dynamic field. Whether you're a beginner or looking to specialize, understanding the types of data science courses available is crucial. Choosing the best Data Science Institute can further accelerate your journey into this thriving industry.

What is Data Science?

At its core, Data Science is the art and science of extracting valuable insights and knowledge from complex data sets. By employing a combination of scientific methods, algorithms, and domain-specific knowledge, Data Science transforms raw data into actionable intelligence. This multidisciplinary field encompasses statistics, mathematics, computer science, and more to analyze and interpret both structured and unstructured data.

Scope of Data Science:

Job Opportunities:

Data scientists are sought after across diverse industries such as finance, healthcare, technology, and e-commerce.

Roles include data analyst, machine learning engineer, data engineer, business intelligence analyst, and data scientist.

Educational Landscape:

The educational landscape for Data Science is expansive, with universities and online platforms offering a plethora of courses, degrees, and certifications.

Specialized programs cover machine learning, big data, data engineering, and business analytics to cater to varying skill levels.

Industry Integration:

Organizations are increasingly integrating data science into their operations, influencing decision-making processes.

Data-driven strategies impact areas like marketing, product development, and overall business strategy.

Government Initiatives:

Governments recognize the importance of data science in driving innovation and economic growth.

Initiatives and policies promote data literacy and skill development, aligning education with industry needs.

Diverse Applications:

Data science finds applications in diverse fields, including finance for fraud detection, healthcare for predictive analytics, marketing for customer segmentation, and agriculture for precision farming.

Its versatility is reflected in its broad spectrum of applications.

Competitive Salaries:

Skilled data science professionals command competitive salaries due to the specialized nature of their expertise.

Salaries vary based on factors like experience, location, and the specific role within the data science field.

Global Contribution:

Data scientists contribute globally, collaborating on projects addressing societal challenges, healthcare advancements, and environmental issues.

The global nature of data science fosters a culture of collaboration and knowledge exchange.

Continuous Innovation:

Data science stands at the forefront of technological innovation, driving advancements in artificial intelligence, machine learning models, and predictive analytics.

Professionals engage in cutting-edge research, contributing to the ongoing evolution of the field.

Career Opportunities:

Data Scientist

Data Analyst

Machine Learning Engineer

Data Engineer

Business Intelligence Analyst

Data Architect

Statistician

Quantitative Analyst

Research Scientist

Predictive Modeler

Data Science is not just a field; it's a dynamic force shaping the future of industries. With a vast scope and diverse career opportunities, it offers a compelling journey for those seeking to immerse themselves in the intersection of technology, analytics, and innovation. As organizations continue to recognize the value of data-driven insights, the demand for skilled data scientists is set to soar, making Data Science a promising and rewarding career path. Choosing the best Data Science courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[spacetimewithstuartgary]

NASA’s Perseverance Rover Looks Back While Climbing Slippery Slope

On its way up the side of Jezero Crater, the agency’s latest Red Planet off-roader peers all the way back to its landing site and scopes the path ahead.  

NASA’s Perseverance Mars rover is negotiating a steeply sloping route up Jezero Crater’s western wall with the aim of cresting the rim in early December. During the climb, the rover snapped not only a sweeping view of Jezero Crater’s interior, but also imagery of the tracks it left after some wheel slippage along the way. 

Stitched together from 44 frames acquired on Sept. 27, the 1,282nd Martian day of Perseverance’s mission, the image mosaic features many landmarks and Martian firsts that have made the rover’s 3½-year exploration of Jezero so memorable, including the rover’s landing site, the spot where it first found sedimentary rocks, the location of the first sample depot on another planet, and the final airfield for NASA’s Ingenuity Mars Helicopter. The rover captured the view near a location the team calls “Faraway Rock,” at about the halfway point in its climb up the crater wall.  

“The image not only shows our past and present, but also shows the biggest challenge to getting where we want to be in the future,” said Perseverance’s deputy project manager, Rick Welch of NASA’s Jet Propulsion Laboratory in Southern California. “If you look at the right side of the mosaic, you begin to get an idea what we’re dealing with. Mars didn’t want to make it easy for anyone to get to the top of this ridge.”

Visible on the right side of the mosaic is a slope of about 20 degrees. While Perseverance has climbed 20-degree inclines before (both NASA’s Curiosity and Opportunity rovers had crested hills at least 10 degrees steeper), this is the first time it’s traveled that steep a grade on such a slippery surface.

Soft, Fluffy

During much of the climb, the rover has been driving over loosely packed dust and sand with a thin, brittle crust. On several days, Perseverance covered only about 50% of the distance it would have on a less slippery surface, and on one occasion, it covered just 20% of the planned route.

“Mars rovers have driven over steeper terrain, and they’ve driven over more slippery terrain, but this is the first time one had to handle both — and on this scale,” said JPL’s Camden Miller, who was a rover planner, or “driver,” for Curiosity and now serves the same role on the Perseverance mission. “For every two steps forward Perseverance takes, we were taking at least one step back. The rover planners saw this was trending toward a long, hard slog, so we got together to think up some options.”

On Oct. 3, they sent commands for Perseverance to test strategies to reduce slippage. First, they had it drive backward up the slope (testing on Earth has shown that under certain conditions the rover’s “rocker-bogie” suspension system maintains better traction during backward driving). Then they tried cross-slope driving (switchbacking) and driving closer to the northern edge of “Summerland Trail,” the name the mission has given to the rover’s route up the crater rim.

Data from those efforts showed that while all three approaches enhanced traction, sticking close to the slope’s northern edge proved the most beneficial. The rover planners believe the presence of larger rocks closer to the surface made the difference.

“That’s the plan right now, but we may have to change things up the road,” said Miller. “No Mars rover mission has tried to climb up a mountain this big this fast. The science team wants to get to the top of the crater rim as soon as possible because of the scientific opportunities up there. It’s up to us rover planners to figure out a way to get them there.”

Tube Status

In a few weeks, Perseverance is expected to crest the crater rim at a location the science team calls “Lookout Hill.” From there, it will drive about another quarter-mile (450 meters) to “Witch Hazel Hill.” Orbital data shows that Witch Hazel Hill contains light-toned, layered bedrock. The team is looking forward to comparing this new site to “Bright Angel,” the area where Perseverance recently discovered and sampled the “Cheyava Falls” rock.

The rover landed on Mars carrying 43 tubes for collecting samples from the Martian surface. So far, Perseverance has sealed and cached 24 samples of rock and regolith (broken rock and dust), plus one atmospheric sample and three witness tubes. Early in the mission’s development, NASA set the requirement for the rover to be capable of caching at least 31 samples of rock, regolith, and witness tubes over the course of Perseverance’s mission at Jezero. The project added 12 tubes, bringing the total to 43. The extras were included in anticipation of the challenging conditions found at Mars that could result in some tubes not functioning as designed.

NASA decidedto retire two of the spare empty tubes because accessing them would pose a risk to the rover’s small internal robotic sample-handling arm needed for the task: A wire harness connected to the arm could catch on a fastener on the rover’s frame when reaching for the two empty sample tubes. 

With those spares now retired, Perseverance currently has 11 empty tubes for sampling rock and two empty witness tubes.

More About Perseverance

A key objective of Perseverance’s mission on Mars is astrobiology, including caching samples that may contain signs of ancient microbial life. The rover will characterize the planet’s geology and past climate, to help pave the way for human exploration of the Red Planet and as the first mission to collect and cache Martian rock and regolith.

NASA’s Mars Sample Return Program, in cooperation with ESA (European Space Agency), is designed to send spacecraft to Mars to collect these sealed samples from the surface and return them to Earth for in-depth analysis.

The Mars 2020 Perseverance mission is part of NASA’s Moon to Mars exploration approach, which includes Artemis missions to the Moon that will help prepare for human exploration of the Red Planet.

NASA’s Jet Propulsion Laboratory, which is managed for the agency by Caltech, built and manages operations of the Perseverance rover.

TOP IMAGE: This enhanced-color mosaic was taken on Sept. 27 by the Perseverance rover while climbing the western wall of Jezero Crater. Many of the landmarks visited by the rover during its 3½-year exploration of Mars can be seen. NASA/JPL-Caltech/ASU/MSSS

LOWER IMAGE: Tracks shown in this image indicate the slipperiness of the terrain Perseverance has encountered during its climb up the rim of Jezero Crater. The image was taken by one of rover’s navigation cameras on Oct. 11. NASA/JPL-Caltech

[transstadiauniversity]

Why Is B. Sc In Computer Science A Rewarding Career?

B.Sc in computer science is a degree that teaches students about  programming,  maintenance, development, and more. The degree teaches students to see computers as science and takes them into deeper aspects. Computer science is a subject for students with an in-depth understanding of and love for computers.

India is one of the biggest IT hubs in the world, and people across the world come to India for IT Solutions. Doing a B.Sc in computer science is a good move, as it will make you eligible for  work in the ever-growing IT industry of the country.

You can have an excellent and high-paying career.  You will get the experience for success and fulfillment. It is vital to learn about a course before you decide to enroll. Here you will  get to know about eligibility. careers, jobs, and more that will help you realise the potential of the B.Sc in computer science degree course.

Course highlights: B.Sc in computer science

The following are the highlights of the B.Sc in Computer Science course. it will give you a quick insight into all of the information with respect to a B.Sc in computer science. In terms of the CS course level, it is an undergraduate course.

 It takes about 3 years to complete and gets divided into 6 semesters.

You will need to pass your 12th exam in science with PCM subjects. The course fees in India will range between 1 lakh to 7 lahks.

Exams will be conducted each semester with an exam at the end of each semester. In most cases, admissions will be granted based on the 12th score.  some colleges will also have entrance examinations.

Eligibility criteria:  B.Sc in computer science

  You need to clear the following eligibility criteria to be considered eligible for the B.Sc in Computer Science degree. 

Students will need to complete their 12th standard in the science stream  which physics chemistry and maths PCM.

Some private colleges may grant admission to the students who have Sciences with physics chemistry and Biology.

Scope of the course:  B.Sc in computer science

B.Sc in computer science has immense scope in India.  Computer education has always had a lot of importance owing to the IT industry of the country. B.Sc in Computer Science degree has a lot of value and is most likely employment with a good salary and other benefits.

Computer education in India is so important that people with other degrees also do additional Computer Based certification courses to add value to their degrees.

Benefits of B.Sc in computer science 

Knowing the benefits of the degree course will help you understand how it can shape your career in the future and help you make up your mind about completing the course.

Career-oriented course 

It's a highly career-oriented degree.  Right after graduation, students can find employment and get started with their professional journey.

High pay scale

  B.Sc in Computer Science students will get an excellent starting salary figure when compared to other graduates. The figure shows a substantial growth over the years. B.Sc in Computer Science graduates will make good money throughout their careers.

If you have a B.Sc in Computer Science degree, you can wish to study further. you can do courses like MBA, MCA and PGDM  and get an amazing career.

Job security

B.Sc in computer science will allow graduates to find a job in the IT industry which is one of the fastest-growing industries in the nation. Jobs in the industry are secured.

 Technologically advanced degree 

Technology is going to be a part of everyday life.  Getting a technology-based like graduation degree in computer science will be advantageous in the long run.

Job opportunities

AI and Machine Learning specialists 

Artificial intelligence and machine learning are revolutionizing many industries. A job as a machine learning engineer is an option for B.Sc. graduates with knowledge of data modeling, machine learning methods, and programming. Starting pay of INR 6 to 9 lakhs per year is typical for machine learning engineers in India. Salary ranges between INR 15 and 25 lakh per year are possible with deep learning and neural network skills and abilities.

Software engineering 

Software development or engineering is one of the most popular job pathways for B.Sc. in Computer Science graduates. Software engineers are responsible for devising, maintaining, and developing apps and software programs. In India, the annual salary of an entry-level software developer may be between INR 3.5 and 6 lakhs. With experience, the pay can rise dramatically and, for senior jobs, can reach INR 15 lakhs or more.

Data analysts 

As organizations rely on data-driven insights for decision-making, data analysis is essential in today's corporate world. Data analysts are B.Sc. graduates with a focus on data analysis. They collect, organize, and analyze data to derive useful insights. In India, entry-level data analysts typically make between INR 4 and INR 7 lakhs annually. Salary ranges between INR 12 and 18 lakh per year are possible with experience and expertise in sophisticated analytics methodologies and data visualization technologies.

Key Takeaway 

The B.Sc in computer science is one of the finest courses that you may prefer to do after your 12th grade. You won’t just be eligible for a high-paying job. But, your contributions can also make a difference in the world and the quality of life. It is a degree with lucrative benefits and offers complete value for the time and money invested. 

TransStadia University may have a strong focus on industry integration, providing students with opportunities for internships, industry projects, and collaborations with companies. Such practical exposure can enhance students' skills and employability. The university's curriculum for the B.Sc. in Computer Science program may be designed to align with industry needs, emphasizing practical skills, project-based learning, and emerging technologies.

You can apply today if you want to enroll in B.Sc Computer Science.