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In September, Stanford announced two major changes to its undergraduate education offerings: the former product design major was rebranded to the new design major, and the former data science minor would now be offered as both a B.A. and B.S. degree.

Current and prospective students from the programs shared their thoughts with The Daily.

New Design Major

The design major now belongs under the d.schools interdisciplinary programs (IDPs), and is categorized as a Bachelor of Science (B.S.) degree in Design. Previously, the product design major resulted in the conferral of a B.S. in Engineering. However, students may still choose to complete the product design engineering subplan if they matriculated before the 2022-2023 academic year.

The design major now has three methods tracks: Physical Design and Manufacturing, AI and Digital User Experience, and Human Behavior and Multi-stakeholder Research. From there, students also select one Domain Focus area, which may be Climate and Environment, Living Matter, Healthcare and Health Technology Innovation, Oceans and Global Development, and Poverty. While not possible in the 2022-23 academic year, students will be able to propose their own Domain Focus area as an honors option in the future.

Sydney Yeh 26 said that the major is a great way to use my creative skills, apply it to technology and move with the current times.

She also believes that the shift from product design to more broad design offerings is beneficial. [While] people are pretty split [on this issue], I think its a good change because theres more variety in what you can specialize in, Yeh said. Before, it was mostly physical design and designing products.

Yeh intends to pursue the digital design track, as she is interested in designing apps and interfaces. She says the design major effectively weaves together her interests in art and computer science. Originally, I was going to combine art and CS and design my own major, but found that the design major fits my goals, Yeh said.

Hannah Kang 26, another prospective design major, echoed Yehs sentiments about combining interests in computer science and art. [The major allows me] to integrate the art aspect and the STEM aspect that I know for sure that Stanford is excelling in, Kang said.

Kang also expressed her appreciation for the CS requirements of the design major, saying, Im trying to take more CS classes so that I can have at least the most fundamental CS knowledge [and can] seek ways to use my engineering skills to create something.

Sosi Day 25, a design major on the human behavior track, praised the collaborative and multidisciplinary aspects of design. Theres a lot of communal learning, she said. Its also very creative, and it engages a lot of different parts of my brain. A lot of it is artistic, but theres also problem solving skills involved.

Day said that as someone who seeks to apply design thinking to other issues beyond manufacturing, the change in major has been a positive one for her. I never considered doing a product design major last year, but now that theyve added two new tracks, its changed my mind, she said.

New Data Science Major

The new data science major was also announced this year. Whereas previously, students could only minor in data science, undergraduates now have the option of majoring on either the B.S. or B.A. track.

Professor Chiara Sabatti, associate director of Data Sciences B.S. track, said that the B.A. has similar foundational requirements to the B.S., but has a concentration of interest in applying data science methods to solve problems in the social sciences.

According to Sabatti, the B.S. track is closely aligned with the former mathematical and computational science (MCS) major, which was phased out this year. She explained that the change to a data science major with more broad offerings was to more closely match with MCS graduates career paths, saying that [the changes] are in response to the needs of the students and the demands of society.

Professor Emmanuel Candes, the Barnum-Simons Chair of math and statistics, said that the formal name change from MCS to data science occurred last spring, though the process of changing the curriculum and developing the B.S. and B.A. paths began in 2019.

Candes echoed Sabattis reflections about students career paths, saying, we realized that more and more of our graduates [of Mathematical and Computational Science] were entering the workforce as data scientists, and it seems like the [new] name represents more of a reality.

The major program has shifted to accommodate this growing interest in data, according to Sabatti.

The structure of the program has changed to make sure that we prepare students for this sustained interest in data science, Sabatti said. For example, theres some extra requirements in computing, because the data sets that people need to work with require substantial use of computational devices, [and] theres some extra classes on inference and how you actually extract information from this data.

Similar to the new design major, many prospective data science majors say the interdisciplinary offerings of the major are enticing.

I like [data science] because its an intersection between technical fields and humanities-focused fields, said Caroline Wei 26, a prospective B.A. data science major on the Technology and Society pathway. What makes data science so powerful is it gives you the option to draw conclusions about society and present that to the rest of the world.

Similarly, Savannah Voth 26, another prospective data science major, shared the humanities and technical skills she feels the major helps her build. The data science B.A. allows me to use quantitative skills and apply it to the humanities and social sciences, she said.

Voth expressed some concerns regarding the ability to connect required coursework with data science more directly.

One issue is that the requirements include classes in statistics and classes in areas you want to apply data science to, but there arent as many opportunities to connect them, Voth said. It would be cool if for each pathway, there was at least one class that is about data science applied to that topic.

Despite this concern, Voth praised the openness of the majors coursework. I like how [the requirements] are very flexible and you can choose which area to focus on through the pathways.

Wei highlighted the effectiveness of the core requirements in building skills and perspectives, saying, The ethics [requirement] is relevant since you have to know how to handle data in an ethical way, the compsci core combines the major aspects of technical fields..and the social science core helps you see why those technical skills are important.

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Students share perspectives on new design and data science majors - The Stanford Daily

If youre a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops. If you have ML models running in production, you probably use ML monitoring to identify data drift and other model risks.

Data science teams use these essential ML practices and platforms to collaborate on model development, to configure infrastructure, to deploy ML models to different environments, and to maintain models at scale. Others who are seeking to increase the number of models in production, improve the quality of predictions, and reduce the costs in ML model maintenance will likely need these ML life cycle management tools, too.

Unfortunately, explaining these practices and tools to business stakeholders and budget decision-makers isnt easy. Its all technical jargon to leaders who want to understand the return on investment and business impact of machine learning and artificial intelligence investments and would prefer staying out of the technical and operational weeds.

Data scientists, developers, and technology leaders recognize that getting buy-in requires defining and simplifying the jargon so stakeholders understand the importance of key disciplines. Following up on a previous article abouthow to explain devops jargon to business executives, I thought I would write a similar one to clarify several critical ML practices that business leaders should understand.

As a developer or data scientist, you have an engineering process for taking new ideas from concept to delivering business value. That process includes defining the problem statement, developing and testing models, deploying models to production environments, monitoring models in production, and enabling maintenance and improvements. We call this a life cycle process, knowing that deployment is the first step to realizing the business value and that once in production, models arent static and will require ongoing support.

Business leaders may not understand the term life cycle. Many still perceive software development and data science work as one-time investments, which is one reason why many organizations suffer from tech debt and data qualityissues.

Explaining the life cycle with technical terms about model development, training, deployment, and monitoring will make a business executives eyes glaze over. Marcus Merrell, vice president of technology strategy at Sauce Labs, suggests providing leaders with a real-world analogy.

Machine learning is somewhat analogous to farming: The crops we know today are the ideal outcome of previous generations noticing patterns, experimenting with combinations, and sharing information with other farmers to create better variations using accumulated knowledge, he says. Machine learning is much the same process of observation, cascading conclusions, and compounding knowledge as your algorithm gets trained.

What I like about this analogy is that it illustrates generative learning from one crop year to the next but can also factor in real-time adjustments that might occur during a growing season because of weather, supply chain, or other factors. Where possible, it may be beneficial to find analogies in your industry or a domain your business leaders understand.

Most developers and data scientists think of MLops as the equivalent of devops for machine learning. Automating infrastructure, deployment, and other engineering processes improves collaborations and helps teams focus more energy on business objectives instead of manually performing technical tasks.

But all this is in the weeds for business executives who need a simple definition of MLops, especially when teams need budget for tools or time to establish best practices.

MLops, or machine learning operations, is the practice of collaboration and communication between data science, IT, and the business to help manage the end-to-end life cycle of machine learning projects, says Alon Gubkin, CTO and cofounder of Aporia. MLops is about bringing together different teams and departments within an organization to ensure that machine learning models are deployed and maintained effectively.

Thibaut Gourdel, technical product marketing manager at Talend, suggests adding some detail for the more data-driven business leaders. He says, MLops promotes the use of agile software principles applied to ML projects, such as version control of data and models as well as continuous data validation, testing, and ML deployment to improve repeatability and reliability of models, in addition to your teams productivity.

Whenever you can use words that convey a picture, its much easier to connect the term with an example or a story. An executive understands what drift is from examples such as a boat drifting off course because of the wind, but they may struggle to translate it to the world of data, statistical distributions, and model accuracy.

Data drift occurs when the data the model sees in production no longer resembles the historical data it was trained on, says Krishnaram Kenthapadi, chief AI officer and scientist at Fiddler AI. It can be abrupt, like the shopping behavior changes brought on by the COVID-19 pandemic. Regardless of how the drift occurs, its critical to identify these shifts quickly to maintain model accuracy and reduce business impact.

Gubkin provides a second example of when data drift is a more gradual shift from the data the model was trained on. Data drift is like a companys products becoming less popular over time because consumer preferences have changed.

David Talby, CTO of John Snow Labs, shared a generalized analogy. Model drift happens when accuracy degrades due to the changing production environment in which it operates, he says. Much like a new cars value declines the instant you drive it off the lot, a model does the same, as the predictable research environment it was trained on behaves differently in production. Regardless of how well its operating, a model will always need maintenance as the world around it changes.

The important message that data science leaders must convey is that because data isnt static, models must be reviewed for accuracy and be retrained on more recent and relevant data.

How does a manufacturer measure quality before their products are boxed and shipped to retailers and customers? Manufacturers use different tools to identify defects, including when an assembly line is beginning to show deviations from acceptable output quality. If we think of an ML model as a small manufacturing plant producing forecasts, then it makes sense that data science teams need ML monitoring tools to check for performance and quality issues.Katie Roberts, data science solution architect at Neo4j, says, ML monitoring is a set of techniques used during production to detect issues that may negatively impact model performance, resulting in poor-quality insights.

Manufacturing and quality control is an easy analogy, and here are two recommendations to provide ML model monitoring specifics: As companies accelerate investment in AI/ML initiatives, AI models will increase drastically from tens to thousands. Each needs to be stored securely and monitored continuously to ensure accuracy, says Hillary Ashton, chief product officer at Teradata.

MLops focuses on multidisciplinary teams collaborating on developing, deploying, and maintaining models. But how should leaders decide what models to invest in, which ones require maintenance, and where to create transparency around the costs and benefits of artificial intelligence and machine learning?

These are governance concerns and part of what modelops practices and platforms aim to address. Business leaders want modelops but wont fully understand the need and what it delivers until its partially implemented.

Thats a problem, especially for enterprises that seek investment in modelops platforms. Nitin Rakesh, CEO and managing director of Mphasis suggests explaining modelops this way. By focusing on modelops, organizations can ensure machine learning models are deployed and maintained to maximize value and ensure governance for different versions.

Ashton suggests including one example practice. Modelops allows data scientists to identify and remediate data quality risks, automatically detect when models degrade, and schedule model retraining, she says.

There are still many new ML and AI capabilities, algorithms, and technologies with confusing jargon that will seep into a business leaders vocabulary. When data specialists and technologists take time to explain the terminology in language business leaders understand, they are more likely to get collaborative support and buy-in for new investments.

Link:

How to explain the machine learning life cycle to business execs - InfoWorld

This computer-generated image, based on data from NASAs Magellan spacecraft, shows Maat Mons, a large volcano (8 kilometres high) on Venus.Credit: NASA/JPL

Scientists have found some of the strongest evidence yet that there is volcanic activity on Venus. Because the planet is a close neighbour to Earth and originally had water on its surface, one big question has been why its landscape is hellish while Earths is habitable. Learning more about its volcanic activity could help explain its evolution and Earths.

How three missions to Venus could solve the planets biggest mysteries

Scientists have known that Venus is covered in volcanoes, but whether or not any of them is still active has been long debated. Now, researchers have discovered that at least one of them probably is, by examining radar images of the planets surface collected by NASAs Magellan spacecraft between 1990 and 1992. They determined that a volcanic vent located in Venuss Atla Regio area, which contains two of the planets largest volcanoes, changed shape between two images taken eight months apart, suggesting an eruption or flow of magma beneath the vent. The scientists reported their findings on 15 March in Science1 and presented them at the Lunar and Planetary Science Conference in the Woodlands, Texas, on the same day.

This is a striking find, says Darby Dyar, an astronomer at Mount Holyoke University in South Hadley, Massachusetts. It brings the space-research community one step closer to figuring out how Venus works, adds Dyar, who is also deputy principal investigator of the VERITAS mission to Venus, which is being overseen by NASAs Jet Propulsion Laboratory (JPL) in Pasadena, California, and aims to map the planets surface sometime after 2030. The whole subject of whether there is active volcanism on the surface of Venus suffers from a lack of data, she adds.

Gathering evidence that the planet is volcanically active wasnt easy. Venuss thick atmosphere 100 times the mass of Earths and high temperatures 450 C make it difficult for rovers and other probes to explore the surface. So far, the most reliable data scientists have collected have come from the Magellan spacecraft.

Robert Herrick, a geophysicist at the University of Alaska Fairbanks, and Scott Hensley, a radar scientist at JPL who is also part of the VERITAS team, analysed full-resolution radar images captured by Magellan of areas with suspected volcanic activity.

Venus is Earths evil twin and space agencies can no longer resist its pull

The challenge was that Magellan imaged the planet in three different cycles over its 24-month mission. During each cycle, it pointed its radar at a different angle to Venuss surface. For scientists to look for changes on the surface over time, they had to superimpose the images at various angles and find overlaps in the terrain to line them up.

Herrick compares the problem to flying from multiple directions through the Grand Canyon in Arizona and then trying to map its surface while looking at opposite canyon walls. Trying to find the same things in those images gets a little more challenging, he says.

The low resolution of the Magellan images added another layer of complexity. Youre looking at the surface, where a football field is a single pixel, he adds.

This worries Scott King, a geophysicist at Virginia Tech University in Blacksburg who studies Venus. He questions whether the images are strong enough evidence to convince sceptics that Venus is volcanically active. Proof is in the eye of the beholder, he says.

Herrick and Hensley acknowledge this limitation in their data. But they also say they are not aware of any equivalent volcanic event on Earth that could cause the changes they observed, though they cannot rule out the possibility that something else might have been responsible.

King doesnt find it hard to believe that the planet has volcanic activity. He hopes, though, that upcoming missions to Venus, including VERITAS, will provide the data needed to convince everyone.

VERITAS, however, has been delayed so King may be waiting longer than originally thought. NASA had planned to launch the mission in 2028, but the agency had to reallocate funding to address the delay of Psyche, another mission that will study a metal-rich asteroid orbiting the Sun between Mars and Jupiter. NASA currently does not have funds planned in the coming years for VERITAS, and if it restores funding, the mission would launch no earlier than 2031.

Prospects for life on Venus fade but arent dead yet

Launching VERITAS after 2030 could cause problems for other missions, Dyar says. Ideally, the topographic data collected by VERITAS would have provided NASAs DAVINCI and the European Space Agencys EnVision with information to help them better target areas theyre planning to explore. DAVINCI, set to launch in 2029, aims to drop a probe into Venuss atmosphere, and EnVision, set to launch in the early 2030s, is meant to take high-resolution radar images of the planets surface.

Studying Venus could not only help researchers understand more about how Earth works, but it could help them learn more about exoplanets beyond the Solar System. Were discovering hundreds, thousands of exoplanets, Dyar says. And many of those seem to be Venus-like, she adds.

Many space missions have been targeting Mars recently, even though overall, Venus is much more Earth-like than the red planet. Herrick hopes the new findings will motivate people to turn their eyes towards Venus and launch VERITAS on time. Venus is truly Earths sibling, he says.

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Volcanoes on Venus? 'Striking' finding hints at modern-day activity - Nature.com

Speech analytics, compositional knowledge discovery, music performance, vocal identification, behavioral analytics, and even audiovisual video for military, health, and environmental management are demanding sound analysis tasks. Acoustic data analysis is the process of examining and comprehending audio signals that have been digitally recorded. And data science is crucial in today's field of sound analysis. Sound can be analyzed using data science, and the results can be shown graphically. In sound analysis, data science has a wide variety of applications.

As a result, allow me to walk you through a few of the typical situations in which we use data science for sound analysis throughout this post. Here, we'll discuss how data science affects and predicts the future of the music business. We will also discuss how data science and good analysis are applied to security. With this certification program, you will discover how data science functions and how it can help you in any sector.

Since roughly ten years ago, the discipline of music retrieval has been prospering, demonstrating many elements of recommended applications like analysis, participation, repeatability, and techniques and technologies for information transmission. As a result, it presents particular challenges for data science, which can be solved by combining data science with artificial intelligence approaches. Supporting effective Data Science processes in other domains is also appropriate.

The music industry rapidly utilizes data analytics to pinpoint vocal range, instrument settings, and performance problems. Given its precision and specificity, it can quickly display every single pitch movement manually recorded in such a computer into charts. Maintaining pitch and vocal quality is essential for creating the best songs in the music industry. Using data science, we can alter the pitch exactly how we want. The guitars can be tuned, and we can make beautiful music.

The potential of data science to analyze the effect of changes and determine the most advantageous outcomes for decisions is well established. This could be helpful for researching sounds or perhaps the music industry. If one uses data science, understanding music analysis is straightforward. By listening to sounds, they can forecast. Knowing a tone or piece of music's individuality allows us to predict whether it will bring gains or losses.

We may also heavily utilize social media to ascertain the audience's interest in current musical trends. To generate a new musical fad with the help of the music industry. Hence, it's not a large leap to suggest that your success is essential to the music industry's financial model.

Speech recognition is one of the most often utilized Data Science applications. It is used everywhere these days. Whether we use computers, electronics, or smart homes, voice-activated applications are omnipresent.

Customer satisfaction is the primary and foremost goal of voice recognition. Voice recognition technology has improved object command, search, and communication. But technology now serves as more than just a means of entertainment; it is also essential for security. It is a means to verify a user's identification and authorization before granting access to and using a system. To control locks, lighting, switches, other devices, and other objects connected to a computer, connected devices use voice control software. The effectiveness of these voice instructions is voice quality dependent.

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Using Data Science in Speech and Sound Analysis - NASSCOM Community