Monthly Archives: January 2022

In classic UC San Diego fashion, an overheard conversation at a campus coffee cart has turned into an interdisciplinary project that's making computing-intensive coursework more exciting while saving well over $1 million dollars so far. The effort gives UC San Diego graduate and undergraduate studentsand their professorsbetter hardware and software ecosystems for exploring real-world, data-intensive and computing-intensive projects and problems in their courses.

Larry Smarr, Distinguished Professor Emeritus, Department of Computer Science and Engineering at the UC San Diego Jacobs School of Engineering.

It all started while UC San Diego computer science and engineering professor Larry Smarr was waiting for coffee in the "Bear" courtyard at the Jacobs School of Engineering a little more than three years ago. While standing in line, Smarr overheard a student say, "I can't get a job interview if I haven't run TensorFlow on a GPU on a real problem."

While this one student's conundrum may sound extremely technical and highly specific, Smarr heard a general need; and he saw an opportunity. In particular, Smarr realized that innovations coming out of a U.S. National Science Foundation (NSF) funded research project he leadsthe Pacific Research Platform (PRP)could be leveraged to create better computing infrastructure for university courses that rely heavily on machine learning, data visualizations, and other topics that require significant computer resources. This infrastructure would make it easier for professors to offer courses that challenge students to solve real-world data- and computation-intensive problems, including things like what he heard at the coffee cart: running TensorFlow on a GPU on a real problem.

Fast forward to 2022, and Smarr's spark of an idea has grown into a cross-campus collaboration called the UC San Diego Data Science/Machine Learning Platform or the UC San Diego JupyterHub. Through this platform, the inexpensive, high-performance computational building blocks combining hardware and software that Smarr and his PRP collaborators designed for use in computation-intensive research across the country are now also the backbone of dynamic computing ecosystems for UC San Diego students and professors who use machine learning, data visualization, and other computing- and data-intensive tools in their courses. The platform has been widely used in every division on campus, including with courses taught in biological sciences, cognitive science, computer science, data science, engineering, health sciences, marine sciences, medicine, music, physical sciences, public health and more.

Xuan Zhang (UC San Diego Chemistry PhD, '21) is one of the tens of thousands of UC San Diego students and young researchers who has used the UC San Diego Data Science/Machine Learning Platform extensively in courses.

It's a unique, collaborative project that leverages federally funded computing research innovations for classroom use. To make the jump from research to classroom applications, a creative and hardworking interdisciplinary team at UC San Diego came together. UC San Diego's IT Services / Academic Technology Services stepped up in a big way. Senior architect Adam Tilghman and chief programmer David Andersen led the implementation effort, with leadership and funding support from UC San Diego CIO Vince Kellen and Academic Technology Senior Director Valerie Polichar. The project has already helped the campus avoid well over $1 million dollars in cloud-computing spend, according to Kellen.

At the same time, the project gives the UC San Diego community tools to encourage the back-and-forth flow of students and ideas between classroom projects and follow-on research projects.

"Our students are getting access to the same level of computing capacity that normally only a researcher using an advanced system like a supercomputer would get. The students are exploring much more complex data problems because they can," said Smarr, who was also the founding director of the California Institute for Telecommunications and Information Technology (Calit2), a UC San Diego / UC Irvine partnership. Calit2 is now expanding to also include UC Riverside.

One of the many professors from all across campus using the UC San Diego Data Science / Machine Learning Platform for courses is Melissa Gymrek, who is a professor in both the Department of Computer Science and Engineering and the Department of Medicine's Division of Genetics.

Her students write and run code in a software environment called Jupyter Notebooks that runs on the UC San Diego platform. "They can write code in the notebook and press execute and see the results. They can build figures to visualize data. We focus a lot more now on data visualizations," said Gymrek.

UC San Diego ITS senior architect Adam Tilghman poses with some of the innovative computing hardware that has opened the door to more data-intensive and computing-intensive coursework for UC San Diego students. These PCs run a wide range of leading-edge software to help students program the system, record their results in Jupyter notebooks, and execute a variety of data analytic and machine learning algorithms on their problems.

One of the thousands of UC San Diego students who has used the platform extensively is Xuan Zhang. Through the data- and visualization-intensive coursework in CSE 284, Zhang realized that the higher order genetic structures at the center of her chemistry Ph.D. dissertationR-Loopscould be regulated by the short tandem repeats (STRs) that are at the center of much of the research in Gymrek's lab. Without the computing-infrastructure for real-world coursework problems, Zhang believes she would not have made the research connection.

After taking Gymrek's course, Zhang also realized that she could apply to obtain her own independent research profile on the UC San Diego Data Science / Machine Learning Platform in order to retain access to all her coursework and to keep building on it. (When Jupyter Notebooks are hosted on the commercial cloud, students generally lose access to their data-intensieve coursework when the class ends, unless they download the data themselves.)

"I thought it was just for the course, but then I realized that Jupyter Notebooks are available for research, without losing access through the UC San Diego Jupyterhub," said Zhang.

This educational infrastructure has added benefits for professors as well.

"With these Jupyter Notebooks, you can automatically embed the grading system. It saves a lot of work," said Gymrek. You can designate how many points a student gets if they get the code right, she explained. Before using this system, students sent PDFs of their problem sets which made grading more time intensive. "It was hard to go past a dozen students. Now, you can scale," said Gymrek. In fact, she has been able to expand access to her personal genomics graduate class to more than 50 students, up from a dozen before she had access to these new tools.

Direct uploading of assignments and grades to the campus learning management system, Canvas, is also now available.

The platform is truly transforming education. Unlike many learning technology innovations, classes in every division at UC San Diego have used the Data Science/Machine Learning Platform. Many thousands of students use it every year. Its innovation with real impact, preparing our students in manysometimes unexpectedfields to be leaders and innovators when they graduate, said Polichar.

"If you build your distributed supercomputer, like the PRP, on commodity hardware then you can ride Moore's Law," explained Smarr.

Following this commodity hardware strategy, Smarr and his PRP collaborators developed hardware designs where performance goes up while prices go down over time. The computational building blocks developed by the PRP, that were repurposed by UC San Diegos ITS, are rack-mounted PCs, containing multi-core CPUs, eight Graphics Processing Units (GPUs), and optimized for data-intensive projects, including accelerating machine learning on the GPUs. These PCs run a wide range of leading-edge software to help students program the system, record their results in Jupyter Notebooks, and to execute a variety of data analytic and machine learning algorithms on their problems.

Building on this commodity hardware approach to high performance computing has allowed UC San Diego to build a dynamic and innovative "on premises" ecosystem for data- and computing- intensive coursework, rather than relying solely on commercial cloud computing services.

The computing platform is made of rack-mounted PCs optimized for data-intensive projects, including accelerating machine learning on Graphics Processing Units (GPUs). The system now contains 126 GPUs, whose usage by students typically ramps up to 100% as each academic quarter progresses.

"The commercial cloud doesn't provide an ecosystem that gives students the same platform from course to course, or the same platform they have in their courses as they have in their research," said Tilghman. "This is especially true in the graduate area where students are starting work in a course context and then they continue that work in their research. It's that continuity, even starting as a lower division undergraduate, all the way up. I think that's one of the innovative advantages that we give at UC San Diego."

UC San Diego professors and students interested in learning more about the Data Science / Machine Learning Platformcan find additional details and contact information on their website.

"I've been at this for 50 years," said Smarr. "I don't know of many examples where I've seen such a close linking of research and education all the way around, in a circle."

This alignment of research and education feeds into UC San Diego's culture of innovation and relevance.

"It's essential for the nation that students all across campus learn and work on computing infrastructure that is relevant for their future, whether it's in industry, academia or the public sector," said Albert P. Pisano, dean of the UC San Diego Jacobs School of Engineering. "These information technology ecosystems being created and deployed on campus are critical for empowering our students to leverage innovations to serve society."

To view a a video providing an overview of the Pacific Research Platform (PRP) and a sampling of research projects the platform has enabled, visit the Pacific Research Platform website.

Larry Smarr serves as Principal Investigator on the PRP and allied grants (NSF Awards OAC-1541349, OAC-1826967, CNS-1730158, CNS-2100237) which are administered through the Qualcomm Institute, which is the UC San Diego Division of Calit2.

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From Coffee Cart to Educational Computing Platform - UC San Diego Health

AUSTIN, Texas, Jan. 24, 2022 /PRNewswire/ --Oracle achieved FedRAMPHigh Provisional Authority to Operate (P-ATO) from the Joint Authorization Board (JAB) for an expanded set of Oracle Cloud Infrastructure (OCI) services. With authorizations for Oracle Autonomous Database, Oracle Integration Cloud, and others, government customers can improve their operations with a growing array of cost-effective, highly secure services available in Oracle's US government regions.

"These newly authorized cloud services expand Oracle's suite of differentiated, highly secure, high-performing technology for government customers," said Rand Waldron, vice president, Global Government Sector, Oracle. "With these new tools, it will be even easier for government organizations to bring their most important systems to the cloud and take advantage of the unique efficiency, support and capability of Oracle to drive the success of their missions."

The following OCI services are now covered by the FedRAMP High JAB P-ATO:

This follows the authorization of more than three dozen OCI services in the past year, including OCI Compute Service, Oracle Digital Assistant, and Oracle Cloud VMWare Solution, as well as Oracle Cloud Marketplace. OCI delivers high-performance computing power that meets the needs of every type of enterprise application with scale-up architectures for agencies that want to adopt cloud technologies and easily migrate critical applications at their own pace. By simplifying management, scaling, and security with autonomous services such as Oracle Autonomous Database, OCI can reduce administrative costs by as much as 90 percent.

FedRAMP is the federal government program that sets the standard for assessing, authorizing, and monitoring cloud systems' security. By attaining authorization from FedRAMP, Oracle's government customers can be assured that Oracle's cloud offerings have been thoroughly reviewed and approved by leading technology officials.

To attain these authorizations, Oracle utilized an accredited Third-Party Assessment Organization to complete a readiness assessment of the cloud service offerings for the authorization process. The service offerings were then reviewed by representatives from the FedRAMP's Joint Advisory Board comprised of technical reviewers from the Department of Homeland Security (DHS), Department of Defense (DoD), and General Services Administration (GSA) for FedRAMP High authorization.

About OracleOracle offersintegrated suites of applications plus secure, autonomous infrastructure in the Oracle Cloud.For more information about Oracle (NYSE: ORCL), please visit us atoracle.com.

TrademarksOracle, Java, and MySQL are registered trademarks of Oracle Corporation.

SOURCE Oracle

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New Oracle Cloud Infrastructure Services Authorized for the US Government - PRNewswire

New Delhi: The Indian telecom industry has been at the heart of the technology driven disruption over the last decade. With over 986 million active wireless subscribers, the industry has an opportunity to also drive transformation for businesses across industries.

IANS spoke to Dr Sanjeev Chaube, EVP and Head, Big Data & Advanced Analytics at Vodafone Idea to get his insights on how Cloud has been at the core of their digital transformation and how they are leveraging intelligent technologies such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT) and analytics.

Dr Chaube believes that there is a critical need for telecom service providers to transform from 'Telco to Techno' and technologies such as 5G, Artificial Intelligence, Big Data Analytics, Cloud Computing, IoT / IIoT / AIoT, and Robotic Process Automation (RPA) will enable this digital transformation.

Q: What are the key strategic pillars of digital transformation at Indian Telcos as you look to transform from a connectivity provider to an orchestrator of value-added solutions that meet the needs of digitally-savvy, connected customers?

A: I believe businesses are fast turning to digital solutions to empower remote workforces, provide customers with better services, and create an immersive experience with increased visibility, resilience & agility. Moreover the need for eliminating traditional, hardware driven, and large expensive physical operations has been at the core of Digital Transformation worldwide across industries.

We need to understand that any transformation starts with data. Hence, a company captures customer data efficiently and leverages it to make smarter decisions. Goal should be to streamline key customer touchpoints to increase spends & reduce efforts.

The employees need to be well informed with relevant data, data driven decisioning culture, access to learning & development platforms empowering better organisational transformation.

Businesses need to shift from IT departments to consolidated enterprise platforms that can absorb & integrate latest technologies instantly. Datasets, processes, web and apps should be shared across the company and the data needs to be integrated to provide a single source of truth by building appropriate Data Lake, Data Warehouses/Marts over Cloud for massive storage & processing capacities.

However, the choice of technology stack whether Open-Source or Proprietary depends primarily on the Value vs Cost proposition. Also, the need for scope, scale, speed, quality & ease form the basis for finalising the tech stack.

Ensuring near Real-time Customer 360 view in form of Business Intelligence Reports and Dashboard for leadership and at different levels of management ensure better & contextual decision making.

Therefore, in my opinion, the key pillars and ingredients for effective Intelligent Digital Transformation Program are a) Artificial Intelligence/Machine Learning & Data Science for Data Driven Decision Making, b) Customer Relevance & Centricity Business Processes, c) Engage & Empower Employees aligned around customer, d) Digital First Strategy with leanest possible technology stack, e) Bring Speed, Scale, Simplicity & Values with Platform, f) Enhance Personalization and g) Single Source of Truth & Data Engineering

Q: How are you using Big Data, Advanced Analytics, Artificial Intelligence/Machine learning & Data Science to understand and cater to the needs of your customers better?

A: Big Data and Advanced Analytics is extensively adopted to improve customers experiences and business performance. Artificial Intelligence & Data Science techniques especially Machine/Deep Learning algorithms are significantly improving some of the crucial areas/services including Customer Segmentation, Targeted marketing, Personalised offerings & recommendations, Churn Prediction, Product development, Predictive Analytics, Call Drop Analysis, reducing fraud, Price optimization, Network experience optimization, Location based services etc.

Over the next five years, rise in mobile-phone penetration and decline in data costs will add 500 million new Internet users in India, creating opportunities for new businesses. Legacy data sets were all structured in nature, however over the last few years, with the explosion of data, unstructured data is gaining equal or more importance in the market. With the advent of 5G technology, the available data from voice, video, social media, messaging, IOT and all new 5G use case deployments are going to increase multifold from hereon.

This Big Data generated by telco's has all the features like massive volume, variety, velocity & veracity. It therefore becomes very critical for us to first organise and systematically store the data emerging from multiple applications within Data Ocean, Datalake, Data Warehouse /Data Marts be it using Cloudera Hadoop Ecosystem or Cloud infrastructure.

Secondly, this Big Data is then used to perform Data Analytics for decision making at different organisational levels from operations and strategy making perspective. All 5 stages of Data Science and Advanced Analytics lifecycle namely Descriptive, Diagnostic, Predictive, Prescriptive and Pre-Emptive Analytics are performed to extract information and patterns.

Machine learning use cases have great potential & value such as assisting with Customer Acquisition, Retention, Digital Engagement, Anomaly Detection, Root Cause Analysis, Predictive Maintenance, Ticket Classification, SLA Assurance and building intelligent networks with features like Self-Healing, Dynamic Optimization & Automated Network Designs. However, in order to work effectively, they require specific computational, pipeline and support infrastructure as well to support massive data & their parallel processing.

Predictive models whether Real-time or Batch processing basis business requirements leveraging structured data feeds such as demographic, usage, billing etc. or Unstructured data feeds like Chat, Text, Images, Video feeds are built to support business with insights beforehand.

Social media analytics is performed using Computer Vision, Natural Language Processing (NLP) & Text parsing techniques etc. to extract sentiments of customers across the country for decision making. Different Deep Learning Architectures of Neural networks ranging from Convolutional Neural Network, Recurrent Neural Networks, Self-Organizing Maps, Auto encoders, etc. are used appropriately as per desired intent.

Q: What did cloud technology allow you to do that you couldn't do before?

A: Cloud is a key enabler of Large-Scale Transformation: be it benefitting Consumer Analytics, Digital Adoption, and Network Monitoring & Management and bringing efficiency in Operations by facilitating faster decisions at scale and speed.

As on date, we are India's first Telco to build a full-fledged Big Data AI/ML Cloud Advanced Analytics Platform on AWS Cloud.

Before embarking on our cloud journey, our data sets were collected in silos across the country. Then, they were aggregated to run the various machine learning & deep learning algorithms.

Also, as the data sets were huge in volume, the available compute & GPU support for data consolidation in the legacy infrastructure was a challenge. All of these complexities added delays to our operations & hampered the productivity of the teams. With the new data science platform deployed over AWS, we are able to address all of these challenges & achieve much more.

Using the compute & memory power available on cloud, we can consolidate data & effectively run our machine / deep learning algorithms & also do parallel data processing with ease. The time to run these algorithms have already reduced by over 60 per cent, and with further optimization, we expect our data science platform to deliver more in the near future.

Additionally, availability of deep learning frameworks like Tensor flow, MXNet, Keras, Pytorch, Gluon, etc. along with pre-trained ML models as APIs help us experiment more with data sets, at scale & speed & eventually in turn improve our customer experience. This feature rich data science platform with its scalability, elasticity & pay-as-you-go commercial model helps consolidate operations & effectively manage costs.

In the future, we'll be able to integrate & effectively use this platform to address our challenges across new technology initiatives including 5G, Edge, IoT monetization, customer 360, Network Function Virtualization transition & so on.

Q: What are your views on the rapid worldwide progress in AIoT & IIoT Analytics for Connected Intelligence with Cloud Technologies?A: As per Fortune Business Insights, global IoT market size is projected to grow from USD 381.30 billion in 2021 to USD 1854.76 billion in 2028. While if we follow Researchandmarkets report, globally the AIoT market itself will reach $78.3 Billion by 2026. Also, the Global IIoT market which was valued at about $216.13 in 2020 and is expected to grow to about $1.1 trillion by 2028.

The above data clearly sets the roadmap and vision for telecom as a connectivity service provider not only for India but telco's globally.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision making. This is in line with the Indian government's plan to develop 100 smart city projects including smart airports, smart railway stations, smart public healthcare services etc., where the 5G, AI, IoT & Cloud technologies will play a vital role in developing these cities & services.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems. The AI-enabled edge device market will be the fastest-growing segment within the AIoT. AIoT automates data processing systems, converting raw IoT data into useful information.

AIoT solutions are the precursor to next-generation AI Decision as a Service (AIDaaS). With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimising system and network operations as well as extracting value from data.

These solutions will provide customer's ability to acquire leading wireless enabled AIoT products and reduce their time to market.

The key areas of AIoT solutions include Data Services, Predictive Asset Management, Intelligent Immersive Experience, Process Improvement, Next-Gen UI & UX and Intelligent Industrial Automation.

This Intelligent connectivity, Big Data & AI/ML technologies are expected to play a major role in key industrial areas such as Agricultural Productivity, Automotive, Transportation & logistics, Industrial & manufacturing operations, Healthcare, Public safety, and security.

Leveraged with AWS IoT Cloud services to collect, organise and analyse data from industrial & publicly installed sensors & devices at scale and for near real-time decision making, AIoT enables industries to seamlessly manage and control actions across the edge and cloud.

The key components for Artificial Intelligence + IoT (AIoT) Analytics consist of Sensors, IoT Gateway, Network, Storage & Analytics Engine for Data Analysis, Monitoring and Intelligent Automation for actions & notifications at the Edge.

Artificial Intelligence & Machine learning algorithms are leverage both at Cloud & Edge over emerging data from IoT sensors such as Visual/Optical Sensors, Temperature Sensors, Pressure Sensors, Infrared Sensors, Gas, Gyroscope Sensors, Accelerometers, Humidity Sensors, Level Sensors etc. to derive descriptive & predictive insights for effective & real-time monitoring, notifications and action fulfilment.

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We are India's 1st telco to build Big Data AI/ML Cloud Advanced Analytics Platform on AWS: Vodafone Idea - ETTelecom.com

Emerging Trends in Computer Science: There is a brighter outlook for the computer science field and its graduates in todays technology drive world. In recent years, the exponential growth of technology has steadily increased the demand for individuals versed with the latest knowledge and skills in areas ranging from cyber security to data structure. The field of computer science engineering deals with designing, implementing, and managing the information systems of the hardware and software processes.

There is a massive demand for computer science graduates to help transform areas ranging from data infrastructure to cyber security. Thus, growing demand results in creating more job opportunities. Computer science graduates can seek employment across various exciting and diverse sectors.

Pursuing a degree programme in computer science can offer you a range of specialisations that can lead to a remunerative career. The prospective students get the liberty to choose from the one that can best match their future goals. In this article, we will look at the emerging computer science trends.

Artificial Intelligence(AI) is an emerging technology that enables a machine to simulate human behaviour. At the same time, machine learning is a subset of AI that allows the device to learn from past data without explicit programming. The AI and ML are springing up because they aim at making computer systems act like humans to solve complex problems, thus, requiring less man force for every task.

AI and ML are growing significantly because of the growth in data, advanced algorithms, and computing power and storage capacity improvement. Thus, it is among the fastest-growing technologies that can dominate the systems and devices in the future.

Big Data is an emerging field that treats ways to analyse and systematically extract information in extensive data that otherwise becomes difficult to deal with. The application and popularity of big data can be estimated from its forecasted global market value, which is nearly 103 billion U.S. dollars by 2027.

Big data is an integral part of organisations because it harnesses their data and helps them to identify new business opportunities.

Cybersecurity is the application of technologies, processes, and control to protect the computer system, programme, network, data, and devices from cyber attacks. Since cyber security efficiently reduces the risk of cyber-attacks and protects against unauthorised technology, design, or network exploitation.

Blockchain technology is a decentralised and distributed ledger that records the origins of a digital asset. It is designed uniquely that the data present on the blockchain cannot be modified or altered, making it a legitimate disrupter for industries, including healthcare, payment, and cybersecurity.

Cloud computing delivers computer services, including storage, database, networking, software, analytics, and intelligence over the internet. It is a faster, innovative, flexible resource. Hence, flexibility and ease-of-use are the two prominent reasons responsible for the growth of cloud computing.

These are some of the emerging trends that dominate the field of computer science. A career in computer science can shape your entire professional life by enabling you to join the most in-demand jobs at the heart of the technology industry. When you opt for computer science engineering, you can get the opportunity to choose from a broad range of specialisations. Sign in now to pursue a well-rounded course for a remunerative career in the computer science domain!

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Emerging Trends in Computer Science {Jan} Read It! - DoD Buzz

Global Cognitive Cloud Computing Market research report 2021 is detailed industries reason about on the present state of the industry which studies advanced strategies for business growth and defines important factors such as top players, value, key regions, growth rate, SWOT analysis i.e., Strength, Weakness, Opportunities and Threat to the organization and others. The report provides the present scenario and the growth forecasts of the global Cognitive Cloud Computing Market. To analyze the market scope, the report presents an inclusive description of the market by the method of examining, combination and summary of data from numerous sources.

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The report offers to global Cognitive Cloud Computing market with respect to segmentation of the global market. Various macro- and microeconomic factors influencing growth of global market have been included in this report. Analysis of drivers, restraints, opportunities and trends of the global Cognitive Cloud Computing market have been propounded in this report. Opportunities available for prominent players operating in the global market have been propounded in this report. All this information included in the report makes it most-creditable and go-to forecast of global Cognitive Cloud Computing market.

Competitive analysis:

It covers inclusive and strategic inputs from global leaders to help users understand the strength and weaknesses of the key leaders. Expert analysts in the field are following players who are profiled as prominent leaders in the Cognitive Cloud Computing market. The report also covers the competitive strategy adopted by these market leaders to the market value. Their research and development process were explained well enough by experts in the global Cognitive Cloud Computing market to help users understand their working process.

The Top Major Players are: Google, Inc., IBM Corporation, Amazon Web Services, Inc., Apple, Inc., Attivio, Inc., Baidu, Inc., BMC Software, Inc., Clarifai, Inc, CognitiveScale, IPsoft Inc.

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Regional Classification

The Cognitive Cloud Computing market is divided into five areas, each with its own development possibilities and current trends: Latin America, North America, Asia Pacific (APAC), Europe, Middle East and Africa. The report was created through extensive research and analysis, as well as examination of numerous elements that may influence regional growth, such as each regions economic, political, environmental, technical, and social condition. It also includes a complete analysis of each regions recognized player, production and revenue, as well as the top influencing elements, critical data and data segmented both regionally and globally.

Method of Research

The purpose of this sections research is to examine the Cognitive Cloud Computing market over the course of the review period using several validated metrics based on Porters Five Force Model. As a result, a thorough examination of the market aids in identifying and emphasizing the markets primary strengths and weaknesses as it progresses. Furthermore, the study was created using a combination of primary and secondary research, including interviews, surveys, and observations from seasoned analysts, as well as reliable paid sources, trade magazines, and industry body databases. Beyond important points in the industrys value chain, the study includes a complete qualitative and quantitative assessment based on data gathered from industry analysts and market players.

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Global Cognitive Cloud Computing Market 2021 Trends and Forecast 2029 | Major Key player Google, Inc., IBM Corporation, Amazon Web Services, Inc.,...

Every generation has a unique development that creates demand in the career market. Before the computer age, technologies like commercial satellites and electroluminescent were trending. Courses that would give students career opportunities in these fields were marketable.

In the computer age, technologies are changing first and students are choosing courses that will make them marketable in a digital world. Courses such as digital marketing, cloud computing, and several soft skills are important in todays job market. Here are the hottest trends in education today.

Data science

The business field currently has a high demand for machine learning engineers, data scientists, and business analysts. On the other hand, colleges are at the forefront to offer related data science programs. They are offering courses in statistics, big data analytics, predictive analytics, and data visualization.

This is a course that former students who studied software engineering or marketing can enroll in for upskilling. Fresh students have big opportunities to take advantage of these courses to make themselves more employable. The data science options available are diploma programs, bachelors, masters, and Ph.D. programs.

Essay writing is a skill that keeps developing as students advance in their courses. When developing your writing skills, one important area to learn is the use of correct grammar in your paper. Your busy student life doesnt offer you chances to spend too much time correcting grammar. With the correct writing tool, you only need to scan your paper and complete grammar correction in minutes. Thehigh-quality essay fixer for studentsby GradesFixer is an important editing service that will correct your errors without altering the original meaning, unlike a writing tool. This is a useful site that you should always visit whenever you have a paper to write or edit.

Artificial Intelligence

Artificial Intelligence today is a trending technology in the business world. Businesses use AI for operational efficiencies, automating processes, making better decisions, improving productivity, and optimizing routine processes.

It is a highly marketable course that is trending in colleges and universities around the world. The current AI-related fields offered by universities are machine learning, deep learning, and natural language processing. Graduated students get career opportunities in data analytics, software engineering, data mining, researchers, AI engineers, and natural language processing.

Cloud computing

Cloud computing offers businesses virtualized computing resources so that they invest less in storage hardware and reduce expenses. Students who enroll in cloud computing courses learn cybersecurity, data governance, data storage, file sharing, SaaS, etc. The most trending careeropportunities in cloud computingare cloud engineers, cloud consultants, data engineers, cloud architects, etc.

Soft skills learning

For todays business to thrive, workers require a combination of both hard and soft skills for better service. There is a need for them to be good time managers, have emotional intelligence, be able to work under pressure, have critical thinking abilities, and solve conflicts.

This is an area that was not given priority in schools a few years ago but today, there are full soft skills courses offered in most schools. Students trained in soft skills stand a better chance of employment in todays demanding world. The commonest areas of training are negotiation skills, communication skills, problem solving and creativity, persuasion and influence, public speaking, presentation, and time management skills.

Project management and business intelligence

Business efficiencycannot be negotiatedtoday due to high competition. Workers must deliver in time, finish allocated tasks, meet deadlines, and drive the business goals forward. To achieve this goal, project managers play an important role. The trending program attached to this field is a business management program under courses such as marketing management, decision science, sales and distribution, accounting and finance, business economics, and organizational behavior.

Business intelligence is another area that a business cannot do without. Using data, managers get insights for actionable plans that can bring a turnaround in a business. This is an area that those trained in IT will effectively manage. Other important courses for this field are certified business intelligence professional and data analyst associate.

Software development

This is a highly trending course applicable in any business field. Students learn programming courses for developing, managing, and designing software. Coding is an important area in this field. Graduates can be employed as network administrators, software engineers, website developers, IT coordinators, and system architects.

Conclusion

The world is becoming more digitized today and this process is opening entirely new opportunities in the career field. There are new programs offered in universities that prepare students to fit into these job openings. These are trending courses such as AI and its related fields, cloud computing, data science, software development, soft skills, and business management. Almost every university is offering these courses that take between two to four years to complete.

Authors Bio

Robert Griffith works as an online tutor for a short-term course provider company that serves many niches and helps college students find part-time jobs. He is a content writer, writing and editing trainer and his expertise is in academic assignments. His free time is for horse riding, practicing mindfulness and watching nature documentaries.

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The Hottest Trends in Education What Students Choose - Headlines of Today - Headlines of Today