POTSDAM — Every day, companies use data in innovative ways to inform their decisions—in everything from targeted marketing to autonomous driving vehicles. Data scientists are the people who analyze, review and present that valuable information to predict outcomes, and provide data-enabled insights critical to making important decisions.

Clarkson University now offers a master of science degree in applied data science, one of today’s most in-demand fields.

“By renaming our program to applied data science, we now more accurately reflect the focus of what students learn in courses on the management, analysis, interpretation, and manipulation of large, complex data sets,” said Operations and Information Systems Professor Boris Jukic, director of the program. “This degree offers a multi-disciplinary education that differentiates Clarkson graduates in the global job marketplace. The skills they gain here allow them to critically evaluate and address real-world problems from a data-enabled perspective.”

The program combines skills and methodologies from applied statistics, business, computer science, and software engineering in statistics, mathematics, computer science, and software engineering. Students learn to integrate knowledge discovery, ranging from traditional statistical methods to various data mining techniques with machine learning, data design and management, as well as necessary programming and math skills. With a solid foundation in the essentials, students have the flexibility to pursue what they find most interesting and provide value to virtually any organization.

According to Dr. Boris Jukic, the new program “breaks down the barriers between traditional departments. To be a true data professional, one must possess a certain level of competence in each of the key disciplines.”

Clarkson University’s Master of Science in Applied Data Science blends business, mathematics, and computer science to prepare students for data-related careers in virtually every sector. The 36-credit program equips graduates to turn big data into smart data and unleash the extraordinary potential of new knowledge.

Due to its interdisciplinary nature, this master’s attracts students with diverse backgrounds, including technology and engineering, but also liberal arts and social science. The ideal applicant, Professor Jukic noted, is one who is comfortable with spreadsheet analysis, favors evidence-based decision-making, and loves solving problems.

The program is available both campus-based and online, and there are full and part-time options. For those interested in an intensive experience, it is possible to complete the MS in Applied Data Science in an accelerated, one-year plan. However, the program is also offered in a more traditional two-year format. For more information go to www.clarkson.edu/graduate/data-science

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Artificial Intelligence is widening its reach to different strata of our life. From the personalized ads to health care facilities, the presence of AI is felt everywhere. Therefore, it is only imperative that we have a thorough understanding of a technology that is growing to become a major part of our lives. This underscores the necessity to be well-versed in the ethics of artificial intelligence, the relevance of which is unquestionable.

What are Ethics?

Ethics of AI refers to those values and principles that act as a torch while we traverse through the hazy paths of advancing technologies like machine translation, facial recognition, or streaming services that run on recommendation algorithms. According to Anna-Mari Rusanen(University lecturer, cognitive science, University of Helsinki), the need for AI literacy becomes all the more important considering the fact that “we are all already parts of various AI systems, and they are developing rapidly.”

As the impact, artificial intelligence has on us is on the rise, it is important to have clarity about what we are dealing with and the ethical questions that come with it.

1. Identification Of Technology

It is important to make a distinction between the aspects of technology that requires reaction and response. Artificial intelligence is a part of even minute segments of our life. It is present wherever there is a requirement of efficiency and accuracy to manage monumental amounts of data. The field of health care is being taken up a notch by the technology that is capable of detecting even the most subtle flaws or errors that will miss the human eye. While on the defense landscape, speculations and discussions are on the table regarding the competence and capability of artificial intelligence in combat. The part it plays on social media platforms is evident without special mention.

This is where we must draw a line between risks and benefits, the inherent risks that can often lead to violation of privacy or human rights. We must be wise and knowledgeable to understand when the technology violates the line of control of privacy and interferes with the inner chambers of our lives.

Technology as we already know is not inherently good or bad. It is about how we use it and for what purpose. Therefore, as users, it is our responsibility to be on guard and ensure that technology doesn’t break the ethical walls in a bid to spread its branches all over the world and our world.

2. The Changing Ethics

Artificial intelligence is a technology with immense scope and possibilities. When the AI filter is added to any field, it enhances the clarity of functions, boosting it to progress and grow. However, it is also necessary to decide what kind of technologies must be used and to ensure that the technologies chosen are in sync with human rights and principles. A new solution can even be a deciding factor in the relationship between a state and its citizen.

According to Rusanen,

“Politicians and the people creating actual AI solutions must consider surprisingly profound questions regarding things like the nature of the default citizen around whom AI-based public services are organized.”

The draft on AI regulations to be released by the EU is proof that politicians have realized this important factor. These regulations will give a clear list of what can be done and what cannot be done using the technology with a firm emphasis on human rights. A clear line of distinction will be drawn between what is useful and what is abusive thereby placing AI within a suitable ethical framework.

3.Ethics Are Not Mere Icing On The Cake

It is important for organizations to realize that ethics is not just namesake signing off a document at the end of a deal. Rather they are concrete frameworks that must be in place. Companies must have a clear picture regarding how AI-centered technologies they use will impact the customers.

In the words of Rusanen,

“Developers must think about to whom their product or service is relevant, how the rights of these people are protected, and what consequences they may face. Everyone working with artificial intelligence must acknowledge what the ethical principles actually mean for their daily work. This must be done at each design meeting and it must guide the process from its very beginning. It’s not enough for an ethical advisor to sign off on the end result.”

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REVA University invites applications for a 2-year Master of Science in Deep Learning and Artificial Intelligence in association with CloudxLab. This UGC approved degree is a globally valid WES recognized Master’s course. The first batch is scheduled to start on 1 May 2021. This course is primarily targeted at professionals and executives with at least 2 years of relevant work experience.

The course will cover machine learning and deep learning. In deep learning, it covers Artificial Neural Networks, Computer Vision, Reinforcement Learning, Generative Adversarial Networks, and Recommendation Engines. IT will also cover libraries like Tensorflow 2, Keras, Numpy, Pandas as part of this 2 years Master’s program. As part of the course, the students will also be required to work on a dissertation project in their 4th semester. Candidates with a basic understanding of programming can apply for this course. Admission into the course is subject to the performance of candidates in a written test as well as in personal interview rounds.

“This course bridges the gap between our conventional academic degrees and industry requirements. It is an experiential hands-on course with an aim to upskill our country’s workforce to the current industrial standards”, says Dr. P Shyama Raju, Chancellor, REVA University.

Sandeep Giri, CEO and Founder at CloudxLab said that “CloudxLab is happy to provide a platform for REVA University in launching one of the most advanced and futuristic Masters course available in the market today.” This 2-years Master’s program is ideal for someone who wants to become a Machine learning Engineer or Data Scientist, or would like to pursue a PhD or even migrate to a different country, said a statement from the institute. Those who wish to know more about the program are advised to visit the official website for more information.

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NIIT University (NU) has introduced four new bachelor’s degree programmes in the new academic year 2021 — B.Tech in Communication and Computer Engineering, B.Tech in Data Science, B.Tech in Cyber Security, and Bachelors in Business Administration. 

To establish a vision to bring about innovation in higher education and learning in emerging areas of the knowledge society, these programmes focus on training students for employability and incorporating new-age skills for the future of work. According to the official release, these programmes will make students future-ready for the new world of work, through multidisciplinary courses.

The course curriculum has been specifically designed to bring in a holistic learning environment while keeping students at par with technological advancements. Thus, these programmes will ensure that the students become ready for employment in today’s dynamic environment, right after their graduation. The four core principles that NIIT University works on include — industry-linkage, technology, research-driven and seamless education.

When asked about these new programmes, Prof. Parimal Mandke, the Acting President of NIIT University stated in the official release that in the current era, it has become imperative for every individual to learn to adapt and change with times to ensure relevancy. 

He added, “Keeping in mind the growing demand for new-age skills, these new programmes offer the best in learning pedagogy, through our outstanding faculty, while enabling students to prepare for the future and offer recession-proof careers in today’s evolving work environment.”

Programmes at NIIT University — 

B.Tech in Data Science degree (four-year undergraduate programme) — built-in line with the growing demand for data science experts worldwide. The programme aims to deploy data sets models to solve real-world problems. The programme emphasises providing industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

B.Tech in Communication and Computer Engineering (four-year undergraduate programme) — dedicated to carry out ICT based management and integrate telecom systems with associated software in networking, enabling students to gain expertise in digital and distribution controls, latest devices in internet-based communication networks, AT, CS, cloud and edge computing, etc.

B.Tech in Cybersecurity degree (four-year undergraduate programme ) — designed for those students who are keen to explore and exploit the latest trends in cybersecurity technologies. It aims to bridge the skill gap between the manpower needs and available talent pool by developing trained professionals for this fast-growing area.

The Bachelors in Business Administration degree (three-year undergraduate programme) — designed for students to build a theoretical foundation and acquire new-age skills in business analytics, digital marketing and finance, banking and insurance technology. This programme aims to develop entrepreneurial skills in students with NU’s specialisation in industry-relevant skills and knowledge.

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The Arkansas Higher Education Coordinating Board (AHECB) has approved Arkansas State University’s proposal to offer a new bachelor of science degree program in data science and data analytics.

The need for data scientists and data analysts within the state and nation is growing extremely fast, as recognized in 2017 by Gov. Asa Hutchinson when he constituted a blue ribbon commission to report on “the economic competitiveness of Arkansas in data analytics and computing.”

The outcomes of the commission’s work recognized universities’ potential to contribute significantly to developing a data science and data analytics (DSDA) workforce in the state.

“We are excited to offer this new BS degree program to our students,” said Dr. Alan Utter, ASU provost and executive vice chancellor for academic affairs. “The data science and data analytics major will allow our students to compete for employment opportunities and graduate education in a wide variety of disciplines.”

Some of the special features of the DSDA degree are a capstone design experience, a credit-bearing internship experience, a 120-credit hour program where 85% of the courses already exist, and a program core that will cover content in statistics, computer science and coding, visualization, data governance and ethics.

Students will be able to choose a domain from a wide range of disciplines. Some of the domain studies in development are in the areas of health care, social sciences, engineering, computer science and geospatial technologies.

“I consider it a privilege to have facilitated an effort to develop the BS in data science and data analytics. It was an intense, collaborative effort executed over almost a year to develop a program with mostly existing courses,” said Dr. Abhijit Bhattacharyya, dean of the College of Engineering and Computer Science.

“This is a testament to the finest traditions in the university on collaborative endeavors and the recognition that data science/data analytics (DSDA) is a fast-evolving new discipline in the state and the nation. The convergence in priorities for DSDA within the state among government, industry and academia to develop the DSDA workforce also acted as a powerful motivation to develop the program,” he said.

Dr. Jason Causey of the Department of Computer Science will serve as the program’s interim director.

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Back in 2019, Healthcare IT News reported on a unique privacy case involving Google and the University of Chicago Medical Center – which had been named as defendants in a class action suit alleging that they’d failed to properly de-identify data used for machine learning research and predictive analytics projects.

The suit’s plaintiff, Daniel Dinerstein, who was a patient at UChicago in 2015, alleged that, while Google and UCMC claimed the medical records used were de-identified, such a claim was “misleading.”

Given that the data provided to Google by the university “included detailed datestamps and copious free-text notes,” he alleged, the tech giant’s expertise in data mining and artificial intelligence made it “uniquely able to determine the identity of almost every medical record the university released.”

On September 4, Judge Rebecca R. Pallmeyer of the U.S. District Court for the Northern District of Illinois granted the University of Chicago and Google’s motions to dismiss the suit.

“Plaintiff suggests that the risk of re-identification was in fact substantial because of the information Google already possesses about individuals through the other services it provides,” Pallmeyer writes in her decision.

“Specifically, the amended complaint refers to Google as ‘one of the largest and most comprehensive data mining companies in the world, drawing data from thousands of sources and compiling information about individuals’ personal traits (gender, age, sexuality, race), personal habits, purchases, and associations.’ Google has ‘create[d] detailed profiles of millions of Americans,’ including public and nonpublic information, and ‘possess[es] detailed geolocation information that it can use to pinpoint and match exactly when certain people entered and visited the University’s hospital,’ according to the amended complaint,” she explained.

“In fact, for a user of Google applications like Mr. Dinerstein, Google can track the specific University hospital buildings or departments he visited and the time of his visits. Plaintiff alleges that the combination of such geolocation information and the EHRs, which include the date and time of hospital services, ‘creates a perfect formulation of data points for Google to identify who the patients in those records really are.’ The amended complaint does not allege, however, that Google has in fact used its extensive data to re-identify any EHRs.”

De-identification, re-identification

The use of de-identified data has been common for years, of course. But so have challenges around keeping it that way. As far back as 2010, the Office of the National Coordinator for Health IT was studying how to manage the privacy risks presented by health information that had been stripped of personal identifiers – the potential for “re-identification.”

The contours of this University of Chicago case are similar in some respects to the so-called “Project Nightingale” initiative between Google and Ascension, which got lots of mainstream media attention this past November, amid concerns over how the Mountain View, California, company was using patient data to help inform its design of new AI and machine learning software for Ascension.

In many respects, the collaboration “is not unlike arrangements that happen every day in America between hospitals and other covered entities and contractors performing services on their behalf,” Deven McGraw, former deputy director for health information privacy at the HHS Office for Civil Rights and now chief regulatory officer at health data startup Ciitizen, said at the time. “Many hospitals have hundreds of business associates, all with extensive access to PHI.

But Google isn’t just any vendor, McGraw acknowledged. It “has access to so much other data about individuals,” she said, and therefore understood concerns that “it may not be possible for data to be truly de-identified in their hands, given all of the data to which they have access.”

As long as Google “fulfills its privacy and security obligations under HIPAA with regard to the protected health information provided by Ascension, there is no HIPAA issue on the face of things,” added healthcare attorney Matthew Fisher, partner at Westborough, Massachusetts-based Mirick, O’Connell, DeMallie & Lougee. “However, given the enormous amount of data held by Google, a maybe not so academic question exists of whether data can be de-identified when in Google’s possession.”

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To facilitate human capacity-building in data science and computational thinking in SA, the continent and abroad, Stellenbosch University (SU) has established the African Data Science Academy (ADSA).

The academy will develop and present open courses offered by SU’s School for Data Science and Computational Thinking, as well as bespoke courses developed for industry and academic partners.

Professor Kanshukan Rajaratnam, director for the School for Data Science and Computational Thinking, invites academia and industry to partner with the ADSA for their training needs and would like to impact both researchers and professionals who want to build their skills in data science-related fields.

“The African Data Science Academy is a way for the School for Data Science and Computational Thinking to build capacity in this area, not just in South Africa and Africa, but across the world. It is an exciting moment where we can bring our world-class academics and internationally known Stellenbosch University quality to the rest of the world.

“ADSA does not just offer another online course, but adds value through interactions with our presenters, and delegates have the opportunity find out how to apply the principles in their own fields.”

The Stellenbosch-based university established its school last July, with the vision to be a world-class institution for data science and computational thinking in and for Africa, and enhance SA’s competitiveness in the fourth industrial revolution.

With the growing demand for data scientists in business and research fields, SA is under immense pressure to address this skills gap. Government has committed to train one million young people in data science and related skills by 2030, to ensure the country has the necessary human capital to meet the demands of the digital economy.

ADSA will, next month, be offering two accredited online short courses in partnership with the African Doctoral Academy at SU. Both courses will take place online from 20 to 24 July.

The first course is an introduction to statistics with R. R is a statistical software program particularly powerful in data analysis and graphical representation. This course offers an introduction in the application of the programming language R to statistical analysis, according to a statement.

The second course is an introduction to data science.

Rajaratnam says the short courses were designed with “high-touch activities in mind”, such as reviews, quizzes, assignments and group work, as well as live or virtual question and answer sessions with the course presenters.

“The courses are taught by Stellenbosch University’s own world-class lecturers from the Department of Statistics and Actuarial Science (introduction to statistics with R) and from the Department of Industrial Engineering (introduction to data science). The fact that we are offering these courses online allows for students in any part of the world to take these courses and allows us to take Stellenbosch University’s world expert academics to the rest of the world.”

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Great Learning is inviting applications for a Master’s program in Data Science and Machine Learning, in partnership with PES University. The ‘MTech in Data Science and Machine Learning’ is 21-month long and has been designed by industry experts and faculty from Great Learning and will be delivered at PES University’s Electronic City campus in Bangalore.

The program will enable participants to gain in-depth understanding of data science and machine learning techniques and tools like Python, SQL, Tableau and Hadoop that are being widely used by industry today. The program blends academic learning and industry exposure through industry internships, capstone projects and dissertation.

The MTech in Data Science and Machine Learning is offered in two modes – Full time and Executive (weekend classes). The participants of the inaugural batch are from companies Accenture, Deloitte, HCL, Oracle, Jindal Steel, Goldman Sachs, etc.

Upon successful completion of the program, students will receive their MTech degree from PES University as well as placement assistance from Great Learning. Great Learning has partnered with over 300 companies and as a result, alumni from Great Learning programs work with some of India’s leading corporates and startups like EXL and EY.

According to recent estimates, India is all set to become one of the top three markets for Data Science by 2020 with over 2.7 million job vacancies. The job roles that are in demand in the sector include Data Scientists, Machine Learning Engineers, Data Analysts and Analytics Consultants. The average salary being offered for beginners is around Rs. 6 lakh per annum.

Speaking about the partnership, Hari Krishnan Nair, Co-founder, Great Learning said: “We are excited to partner with a top ranked university like PES to offer a Masters’ degree program in Data Science and Machine Learning. Every company that we are engaged with is looking to hire Data Science and Machine Learning talent but there is a massive shortfall in the availability of skilled talent to fill in these vacant jobs. Through our partnership with PES University, we aim to open the doors for all those who are interested in a career in data science through a high quality M Tech program. This program will equip the learners with the latest skills and ensure they are job ready by the time they complete the program.”

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In particular, the more potential inputs you have to an algorithm, the more out of control your scaling problem gets when analyzing its problem space. This is where MACH, a research project authored by Rice University’s Tharun Medini and Anshumali Shrivastava, comes in. MACH is an acronym for Merged Average Classifiers via Hashing, and according to lead researcher Shrivastava, “[its] training times are about 7-10 times faster, and… memory footprints are 2-4 times smaller” than those of previous large-scale deep learning techniques.

In describing the scale of extreme classification problems, Medini refers to online shopping search queries, noting that “there are easily more than 100 million products online.” This is, if anything, conservative—one data company claimed Amazon US alone sold 606 million separate products, with the entire company offering more than three billion products worldwide. Another company reckons the US product count at 353 million. Medini continues, “a neural network that takes search input and predicts from 100 million outputs, or products, will typically end up with about 2,000 parameters per product. So you multiply those, and the final layer of the neural network is 200 billion parameters … [and] I’m talking about a very, very dead simple neural network model.”

At this scale, a supercomputer would likely need terabytes of working memory just to store the model. The memory problem gets even worse when you bring GPUs into the picture. GPUs can process neural network workloads orders of magnitude faster than general purpose CPUs can, but each GPU has a relatively small amount of RAM—even the most expensive Nvidia Tesla GPUs only have 32GB of RAM. Medini says, “training such a model is prohibitive due to massive inter-GPU communication.”

Instead of training on the entire 100 million outcomes—product purchases, in this example—Mach divides them into three “buckets,” each containing 33.3 million randomly selected outcomes. Now, MACH creates another “world,” and in that world, the 100 million outcomes are again randomly sorted into three buckets. Crucially, the random sorting is separate in World One and World Two—they each have the same 100 million outcomes, but their random distribution into buckets is different for each world.

With each world instantiated, a search is fed to both a “world one” classifier and a “world two” classifier, with only three possible outcomes apiece. “What is this person thinking about?” asks Shrivastava. “The most probable class is something that is common between these two buckets.”

At this point, there are nine possible outcomes—three buckets in World One times three buckets in World Two. But MACH only needed to create six classes—World One’s three buckets plus World Two’s three buckets—to model that nine-outcome search space. This advantage improves as more “worlds” are created; a three-world approach produces 27 outcomes from only nine created classes, a four-world setup gives 81 outcomes from 12 classes, and so forth. “I am paying a cost linearly, and I am getting an exponential improvement,” Shrivastava says.

Better yet, MACH lends itself better to distributed computing on smaller individual instances. The worlds “don’t even have to talk to one another,” Medini says. “In principle, you could train each [world] on a single GPU, which is something you could never do with a non-independent approach.” In the real world, the researchers applied MACH to a 49 million product Amazon training database, randomly sorting it into 10,000 buckets in each of 32 separate worlds. That reduced the required parameters in the model more than an order of magnitude—and according to Medini, training the model required both less time and less memory than some of the best reported training times on models with comparable parameters.

Of course, this wouldn’t be an Ars article on deep learning if we didn’t close it out with a cynical reminder about unintended consequences. The unspoken reality is that the neural network isn’t actually learning to show shoppers what they asked for. Instead, it’s learning how to turn queries into purchases. The neural network doesn’t know or care what the human was actually searching for; it just has an idea what that human is most likely to buy—and without sufficient oversight, systems trained to increase outcome probabilities this way can end up suggesting baby products to women who’ve suffered miscarriages, or worse.

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Bangalore: Vijaybhoomi University in the Greater Mumbai region promoted by the 25 year old leading business school in the country, IFIM Business School has established a first of its kind arrangement with INSOFE, a leader in Data Science education and corporate training, to establish an independent professional school for data sciences under the aegis of the
University to offer next-gen degree programs such as BS (Data Science), BE (Artificial Intelligence) and MS (Data Science). These programs have specially designed by INSOFE to meet the impending demand of more than 90,000 data science professionals as required in the country.

The BS (Data Science) program is probably the best qualification for those who are planning to make a career in the IT and ITeS world. With the world going digital, algorithms and data analytics skills are becoming paramount. With most IT and ITeS companies pivoting their businesses to cater to the digital transformation requirements of the world, trained data scientists are becoming the most demanded professionals in the industry. And why not? By pursuing a 3 year Bachelor of Science program, the user learns the tools for building algorithms and also learns to apply the same to a business vertical. Graduating students from this program can expect to get high paying jobs not only in IT and ITeS companies but also be in high demand by the new age platform companies that have been established on the foundation of digital and big data analytics. The students are also empowered to deep dive and complete their masters in data science by completing a co-term of one year. Implying that a student will be able to finish his bachelors and masters degree in 4 years.

The BE (Artificial Intelligence) program is best suited for those who are keen to work in research laboratories and world-class product companies to solve complex engineering tasks of data sciences and artificial intelligence. With extremely rigorous training in mathematics and software programming, students graduating from this program will be able to design, develop and maintain revolutionary platforms for various applications of AI, Machine Learning and Deep Learning. The degree also provides an ideal platform for students to pursue a global Master’s and Ph.D. degree in AI.

The MS (Data Science) program is a two year program targeted at engineering / science / math graduates who wish to develop a career in Data Science. It combines three crucial aspects of AI application; Engineering, Mathematics and Business. The student spends one term studying Data Science, one term studying Business Management and one term overseas getting exposed to real world product engineering problems in data science. Then they do an industry project and thesis. They will be looked at by the industry to help them leap forward in AI adoption.

The INSOFE School of Data Science at Vijaybhoomi University will be set up in the Vijaybhoomi University campus at Karjat, Greater Mumbai. The school will provide state of the art infrastructure for laboratory work and will also engage with industry to provide the best internships that India has to offer. The courses at this school will be delivered by the acclaimed faculty of INSOFE, faculty from IFIM Business School and Vijaybhoomi University. The students will also experience master classes from leading faculties in the world and will also have an opportunity to pursue a term overseas along with dual degree options with global highly ranked universities.

Speaking on this association, Sanjay Padode, President Vijaybhoomi University , said, “Vijaybhoomi University is the first “Liberal Professional University” in the country. The liberal framework of the curriculum enables the student to explore and discover their own life goals. Further the curriculum goes on to help the student build relevant knowledge and develop skills that are highly desirable but in short supply and much required by society. Partnering with INSOFE is a testimony to the University’s commitment of providing professional education and designed to provide each student with a great launching pad for ‘xgboosting” their career”

Dakshinamurthy V Kolluru, CEO of INSOFE, said, “We have observed that technology and management skills grow along two different pathways. Through the implementation of technology across various industries, there is a driving need to bridge the gap between technology and management skills. By delivering these new-age programmes to the students of VijayBhoomi University and IFIM Business School, we aim to enrich students to become future-ready professionals who would have technology as well as managerial skills as their forte.”

INSOFE School of Data Science will be the first private professional training and education school established in a University for conducting research and delivering rigorous academic inputs to develop future ready professionals in the area of data science. The degree programs offered by this school will be the best in this category and the objective of these programs would be to develop continuously employable professionals who are trained to be solution seekers and solution providers leveraging the new technologies of Artificial Intelligence and Data Science to make our society a better place to live whilst developing their individual successful careers.

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