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Exam Code: C2170-051 Practice exam 2022 by Killexams.com team
IBM i2 Analyst-s Notebook V8.9
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By Sumanth Palepu

A decade ago, if anyone had talked about self-driven cars, there would be disbelief. From year 2023, Hyundai Motors will be doing this. The automobile giant has collaborated with Motional, the global leader in driverless technology, to deploy the Level 4 autonomous vehicles across major U.S. cities starting in 2023.

There’s a team of data scientists behind this innovation that has worked with other teams to make this a reality. And a clear example of how data science is a stepping-stone for the future. The best part? Someone with a non-technical degree can also become a data scientist. The criteria being a knowledge of mathematics, especially statistics. Both the Indian and the global markets are witnessing a huge spike in the number of students and young professionals from the tech and non-tech fields who have chosen to deep dive into data science.

According to a report by the US Bureau of Labour Statistics, the rise of data science will create roughly 11.5 million job openings by 2026. Data science has also topped LinkedIn’s Emerging Jobs Report for three years running.

This is owing to the emergence of new technologies like AI, big data analytics, blockchain, IoT and more, making data science a vital part of multiple business functions. Moreover, with the emergence of Metaverse, it is expected that data science will be put under spotlight creating a pool of opportunities for skilled professionals. Even world’s tech giants like – Google, Amazon, Apple, Microsoft, and Facebook — are the biggest employers of data scientists and engineers today.

Demystifying a Data Scientist

A data scientist is an analyst who combines social science with technical expertise to not just manage data but to also predict trends. This expert prepares data for analysis through various steps like cleansing, aggregating, and manipulating the data. Though algorithms are fed in the machines, a data scientist must understand how those algorithms function, select the right one and even tweak them if required.

Anyone with an undergraduate or post-graduate degree in science or commerce or a basic knowledge of maths and statistics, can apply to be a data scientist.

Data Science Course: Quality Matters

Though there are a plethora of courses available, it’s important to choose a quality course. GUS Edology has collaborated with IBM to provide a customised IBM ICE Data Science Program, that teaches a learner how to use methods, processes, algorithms, and systems to extract useful business information from structured and unstructured data and apply this knowledge for making strategic decisions. This intermediate certification course is aimed at young professionals with two years of work experience.

The USP of this course is that it’s taught by instructors from IBM or authorised by IBM, and there’s a high probability of employability and an opportunity to do real life industry-based projects. The skills taught include Data Analysis, Excel for Business Analytics, Data Visualisation. This course aids the learner in acquiring a good knowledge in using excel for all business requirements, have an in-depth understanding of data Science, and apply it to solve business problems using analytical tools, effectively.

Skills, Career Prospects and Future

Besides being adept at debugging, storytelling, competence in SQL and Python, understanding of statistics, organizations provide equal emphasis to soft skills in their prospective employees. These include critical thinking and reasoning, creativity, adaptability, good communication and decision-making skills, with the ability to multitask and work in teams.

The career prospects in this field include Data Analyst, Business Analyst, Financial Analyst, Business Intelligence Analyst.

Impact of Metaverse on Data Science

With metaverse on the horizon, businesses will have to deal with a humungous data explosion that will need streamlining and analysis. Consequently, this will create a greater push for data scientists and their actionable insights will be even more critical for businesses and organizations.

The Future

Data science is one of the critical steppingstones to the future and the figures speak for themselves. It’s estimated that over 43 percent retailers and fashion brands will invest in customer-based data science to map trends, predict consumer behavior over the next five years. This is just one field. Today data scientists and analysts are taking over the world from aviation, media, advertising, branding, oil and gas, petroleum, corporate sector to even political parties. The future is bright and growing exponentially. The only question is, are you upskilling to be a part of it?

The author is business head, GUS Edology. Views expressed are personal.

Mon, 18 Jul 2022 04:07:00 -0500 en text/html https://www.financialexpress.com/education-2/data-science-the-new-kid-on-the-block/2596686/
Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Partnerships & Use Cases

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Improve future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Improve quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Improve the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : Dow Analyst Moves: IBM No result found, try new keyword!T he latest tally of analyst opinions from the major brokerage houses shows that among the 30 stocks making up the Dow Jones Industrial Average, International Business Machines is the #22 analyst pick ... Mon, 01 Aug 2022 03:46:00 -0500 text/html https://www.nasdaq.com/articles/dow-analyst-moves%3A-ibm-2 Killexams : Global Microlearning Market to Boost the Growth during the Forecast Period 2022–2030: IBM, Bigtincan, SwissVBS, iSpring Solutions, Epignosis

The new report on “Microlearning Market Report 2022 by Key Players, Types, Applications, Countries, Market Size, Forecast to 2030” offered by Market Research, Inc. includes a comprehensive analysis of the market size, geographical landscape along with the revenue estimation of the industry. In addition, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the “Microlearning Market”.

Microlearning is an educational strategy that breaks complex syllabus down into short-form, stand-alone units of study that can be viewed as many times as necessary, whenever and wherever the learner has the need.

Click the link to get a demo Copy of the Report: https://www.marketresearchinc.com/request-sample.php?id=16108

This market study covers and analyzes the potential of the global Microlearning industry, providing geometric information about market dynamics, growth factors, major challenges, PEST analysis and market entry strategy analysis, opportunities and forecasts. One of the major highpoints of the report is to provide companies in the industry with a strategic analysis of the impact of COVID-19 on Microlearning market.

Microlearning Market: Competition Landscape

The Microlearning market report includes information on the product presentations, sustainability and prospects of leading player including: Saba Software, Axonify, IBM, Bigtincan, SwissVBS, iSpring Solutions, Epignosis, Cornerstone OnDemand, and Qstream.

Microlearning Market: Segmentation

Microlearning Market: Product Segment Analysis

Microlearning Market: Application Segment Analysis

  • Retail
  • Manufacturing and Logistics
  • BFSI
  • Telecom and IT
  • Healthcare and Life Sciences

Microlearning Market: Regional Analysis

All the regional segmentation has been studied based on exact and future trends and the market is forecasted throughout the prediction period. The countries covered in the regional analysis of the Global Microlearning market report are North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and Latin America.

Key Benefits of the report:

  • This study presents the analytical description of the global Microlearning industry along with the current trends and future estimations to determine the imminent investment pockets.
  • The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the global Microlearning market share.
  • The current market is quantitatively analyzed from 2022 to 2030 to highlight the global Microlearning market growth scenario.
  • Porter’s five forces analysis illustrates the potency of buyers & sellers in the market.
  • The report provides a detailed global Microlearning market analysis based on competitive intensity and how the competition will take shape in the coming years.

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Major Points Covered in TOC:

Market Summary: It incorporates six sections, research scope, major producers covered, market segments by type, Microlearning market segments by application, study goals, and years considered.

Market Landscape: Here, the global Microlearning Market is dissected, by value, income, deals, and piece of the pie by organization, market rate, cutthroat circumstances landscape, and most exact patterns, consolidation, development, and segments of the overall industry of top organizations.

Profiles of Companies: Here, driving players of the worldwide Microlearning market are considered dependent on deals region, key items, net income, cost, and creation.

Market Status and Outlook by Region: In this segment, the report examines about net edge, deals, income and creation, portion of the overall industry, CAGR and market size by locale. Here, the worldwide Microlearning Market is profoundly examined based on areas and nations like North America, Europe, Asia Pacific, Latin America and the MEA.

Application: This segment of the exploration study shows how extraordinary end-client/application sections add to the worldwide Microlearning Market.

Market Forecast: Production Side: In this piece of the report, the creators have zeroed in on creation and creation esteem conjecture, key makers gauge and creation and creation esteem estimate by type.

Research Findings and Conclusion: This is one of the last segments of the report where the discoveries of the investigators and the finish of the exploration study are given.

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Tue, 12 Jul 2022 19:09:00 -0500 Industry Global News 24 en-US text/html https://www.digitaljournal.com/pr/global-microlearning-market-to-boost-the-growth-during-the-forecast-period-2022-2030-ibm-bigtincan-swissvbs-ispring-solutions-epignosis
Killexams : Simplilearn: SIMPLILEARN COLLABORATES WITH THE UNIVERSITY OF MINNESOTA'S CARLSON SCHOOL OF MANAGEMENT TO LAUNCH BUSINESS ANALYTICS BOOTCAMP
  • The program is best suited for early-mid career professionals with 2+ years of work experience.
  • Upon program completion, learners will receive a certificate from Carlson School of Management and Simplilearn.
  • The program includes masterclasses delivered by distinguished faculty from the University of Minnesota and industry experts from IBM.
  • The bootcamp will also include career assistance from Simplilearn.

SAN FRANCISCO, Aug. 2, 2022 /PRNewswire/ -- Simplilearn, a global digital skills training provider, announced its partnership with the University of Minnesota's Carlson School of Management for a Bootcamp in Business Analytics. The program will provide a well-planned, high-level understanding of business analytics and the real-world application of analytics across multiple domains. Anyone who has completed their bachelor's degree (in any background) is eligible for this program. The bootcamp is best suited for early-mid career professionals having 2+ years of formal work experience, such as IT professionals, Banking and Finance professionals, Marketing Managers, Supply Chain Network Managers, Analysts, and Consultants.

The six-month program will be based on a blended format of online self-learning and live virtual classes. The key features of the program include:

  • Program completion certificate from the Carlson School of Management and Simplilearn
  • Membership to University of Minnesota's Alumni Association
  • Masterclasses delivered by Carlson School of Management faculty and industry experts from IBM
  • Industry-recognized IBM certificates for IBM courses
  • Ask-Me-Anything sessions & hackathons conducted by IBM
  • More than twelve industry-relevant projects
  • Simplilearn career assistance
  • Integrated labs

The program curriculum consists of R Programming for Data Science, SQL, Business Analytics with Excel, Data Analytics with Python, Capstone Projects, and other modules.

Speaking about the program, Mr. Anand Narayanan, Chief Product Officer, Simplilearn, said, "In today's business-driven environment, every organization is devising ways to make their decision-making more insightful and impactful. As a result, the role of business analytics continues to grow in importance. Business Analytics provides companies the ability to derive deeper insights and create stronger business recommendations for their own success. Given the industry relevance of the program, we have partnered with the University of Minnesota's Carlson School of Management to curate this Business Analytics Bootcamp that will provide learners an extensive knowledge of the course and widen growth and professional opportunities."

Speaking on the partnership with Simplilearn, Dr. Ravi Bapna, Curtis L. Carlson Chair in Business Analytics and Information Systems at the Carlson School of Management, University of Minnesota said, "Business Analytics can help companies make better, more informed decisions and achieve various goals. Its ability to navigate crises has further increased its importance to the business. Business analytics has certainly changed the dynamics of working in the digital economy and how companies operate."

Simplilearn conducts more than 3000 live classes, with an average of 70,000 learners who together spend more than 500,000 hours each month on the platform. Simplilearn's programs allow learners to upskill and get certified in popular domains. In 2020, Simplilearn introduced a free skills development program called SkillUp. SkillUp lets learners explore in-demand syllabus in top professional and technology fields for free, helping them make the right learning and career decisions.

About the Carlson School of Management, University of Minnesota

The Curtis L. Carlson School of Management at the University of Minnesota is a recognized leader in business education and research. Established in 1919 and located in Minneapolis, the Carlson School is committed to developing leaders who believe business is a force for good through experiential learning, international education, and the school's strong ties to the dynamic Twin Cities business community.

With 13 degree programs that are ranked consistently among the world's best, the school offers bachelor's, master's, and doctoral degrees, as well as executive education programs hosted both domestically and abroad. Today, the Carlson School has nearly 60,000 alumni in more than 100 countries.

About Simplilearn

Founded in 2010 and based in San Francisco, California, and Bangalore, India, Simplilearn, a Blackstone company is the world's #1 online Bootcamp provider for digital economy skills training. Simplilearn offers access to world-class work-ready training to individuals and businesses around the world. The Bootcamps are designed and delivered with world-renowned universities, top corporations, and leading industry bodies via live online classes featuring top industry practitioners, sought-after trainers, and global leaders. From college students and early career professionals to managers, executives, small businesses, and big corporations, Simplilearn role-based, skill-focused, industry-recognized, and globally relevant training programs are ideal upskilling solutions for diverse career or/and business goals.

For more information, please visit www.simplilearn.com/

Logo: https://mma.prnewswire.com/media/1100016/Simplilearn_Logo.jpg

Tue, 02 Aug 2022 03:57:00 -0500 de text/html https://www.finanznachrichten.de/nachrichten-2022-08/56706058-simplilearn-simplilearn-collaborates-with-the-university-of-minnesota-s-carlson-school-of-management-to-launch-business-analytics-bootcamp-008.htm
Killexams : Skillable Named To Training Industry's 2022 Top 20 Online Learning Library List No result found, try new keyword!Their Top 20 Online Learning Library list is based on thorough analysis of the ... “I'm proud of our team's efforts in creating content that both challenges learners and complements their ... Fri, 01 Jul 2022 04:12:00 -0500 https://menafn.com/1104464953/Skillable-Named-To-Training-Industrys-2022-Top-20-Online-Learning-Library-List Killexams : Learning Management Systems Market Value for Higher Education is Set to Grow by USD 5.42 Billion, Progressing at a CAGR of 22.75% from 2021 to 2026

NEW YORK, July 29, 2022 /PRNewswire/ -- The Learning Management Systems Market for Higher Education is segmented by Deployment (on-cloud and on-premise) and Geography (North America, Europe, APAC, South America, and Middle East and Africa). Moreover, the Y-O-Y growth rate of 2022 for the market is estimated at 21.67%. The report also provides a detailed analysis of drivers & opportunities, top winning strategies, competitive scenario, future market trends, market size & estimations, and major investment pockets.

Technavio has announced its latest market research report titled Learning Management Systems Market for Higher Education by Deployment and Geography - Forecast and Analysis 2022-2026

For more additional information about the Learning Management Systems Market for Higher Education. BROWSE SUMMARY OF THE RESEARCH REPORT

Learning Management Systems Market for Higher Education Overview

LMS is a software application that enables the documentation, administration, tracking, reporting, and delivery of educational courses or training programs. It is also used to monitor the learner's progress so that actions can be taken accordingly. Streamlining of the learning process, the need for cost-effective, centralized learning solutions, and rising emphasis on aspects such as personalized learning and formative assessments have paved the way for higher penetration of LMS in the academic segment.

Vendor Insights

The learning management systems market for higher education report offers information on several market vendors, including Adobe Inc., AlphaLearn, Blackboard Inc., Cornerstone OnDemand Inc., D2L Corp., Docebo Inc., Epignosis, Instructure Inc., International Business Machines Corp., John Wiley and Sons Inc., Jzero Solutions Ltd., Learning Technologies Group Plc, Oracle Corp., Paradiso Solutions, PowerSchool Holdings Inc., SAP SE, Byteparity Technologies LLP, and Kochar Infotech Ltd among others. Moreover, the market is fragmented and the vendors are deploying organic and inorganic growth strategies to compete in the market.

  • Blackboard Inc. - The company offers learning management systems that work alongside by helping to leverage innovative technologies and services which include student retention to brand reputation management.

  • D2L Corp. - The company offers learning management systems that provides people with virtual and in-person instructor-led training sessions, eLearning, and playlists that work for organizations of all sizes.

  • Docebo Inc. - The company offers learning management systems that provides highly configurable interfaces that let the user define graphical navigation where users can set up an interface that meets their individual needs.

Find additional highlights on the vendors and their product offerings. REQUEST demo REPORT (INCLUDING GRAPHS & TABLES) OF THIS MARKET

Segmentation Analysis & Forecasts

  • By Deployment (on-cloud and on-premise)

  • By Geography (North America, Europe, APAC, South America, and Middle East and Africa)

DOWNLOAD demo COPY OF THIS REPORT Using Business Email ID to Gain Further Insights on the Market Contribution & Share of Various Segments & Regions on Higher Priority

Key Learning Management Systems Market for Higher Education Drivers:

Premium Learning Management Systems Market for Higher Education Trends:

Major Learning Management Systems Market for Higher Education Challenges:

Find additional information about market Drivers, Trends, & Challenges available with Technavio. READ demo REPORT OF THIS MARKET

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Here are Some Similar Topics-

Next Gen Learning Management System (LMS) Market for Higher Education Growth, Size, Trends, Analysis Report by Type, Application, Region and Segment Forecast 2021-2025: The market value is set to grow by USD 4.05 billion, progressing at a CAGR of 30% from 2020 to 2025, as per the latest report by Technavio. 56% of the market's growth will originate from North America during the forecast period. US and Canada are the key markets for next gen learning management system (LMS) for higher education in North America. FIND MORE RESEARCH INSIGHTS HERE

Higher Education M-learning Market Growth, Size, Trends, Analysis Report by Type, Application, Region and Segment Forecast 2021-2025: The market value is set to grow by USD 3.44 billion, progressing at a CAGR of over 22% from 2020 to 2025, as per the latest report by Technavio. The higher education M-learning market vendors should focus on grabbing business opportunities from the non-learning applications segment as it accounted for the largest market share in the base year. FIND MORE RESEARCH INSIGHTS HERE

Learning Management Systems Market for Higher Education Scope

Report Coverage

Details

Page number

120

Base year

2021

Forecast period

2022-2026

Growth momentum & CAGR

Accelerate at a CAGR of 22.75%

Market growth 2022-2026

$ 5.42 billion

Market structure

Fragmented

YoY growth (%)

21.67

Regional analysis

North America, Europe, APAC, South America, and Middle East and Africa

Performing market contribution

APAC at 30%

Key consumer countries

US, Canada, China, UK, and Germany

Competitive landscape

Leading companies, competitive strategies, consumer engagement scope

Companies profiled

Adobe Inc., AlphaLearn, Blackboard Inc., Cornerstone OnDemand Inc., D2L Corp., Docebo Inc., Epignosis, Instructure Inc., International Business Machines Corp., John Wiley and Sons Inc., Jzero Solutions Ltd., Learning Technologies Group Plc, Oracle Corp., Paradiso Solutions, PowerSchool Holdings Inc., SAP SE, Byteparity Technologies LLP, and Kochar Infotech Ltd

Market Dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID 19 impact and future consumer dynamics, market condition analysis for forecast period,

Customization preview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table of Contents

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by Deployment

6 Customer Landscape

7 Geographic Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contact

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

Technavio (PRNewsfoto/Technavio)

Cision

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SOURCE Technavio

Fri, 29 Jul 2022 09:35:00 -0500 en-US text/html https://www.yahoo.com/now/learning-management-systems-market-value-213500679.html
Killexams : IBM beats quarterly revenue estimates, warns of $3.5 billion forex hit

By Chavi Mehta

(Reuters) -IT hardware and services company IBM Corp beat quarterly revenue expectations on Monday but warned the hit from forex for the year could be about $3.5 billion due to a strong dollar.

A hawkish Federal Reserve and heightened geopolitical tensions have driven gains in the dollar against a basket of currencies over the last year, prompting companies with sizeable international operations, including Microsoft and Salesforce, to temper expectations.

Shares of IBM pared losses and were down 1.3% in extended trading.

On the earnings call, Chief Financial Officer James Kavanaugh told analysts both currency headwinds and impact from exiting Russia operations has put pressure on IBM's near-term results but reiterated the company's full-year forecast of hitting the upper end of mid-single-digit revenue growth at constant currency.

IBM expects a foreign exchange hit to revenue of about 6% this year, Kavanaugh said. It had previously forecast a 3%-4% hit.

Second-quarter revenue was hurt by $900 million due to a stronger U.S. dollar, Kavanaugh said, adding the pace and magnitude at which the currency has strengthened was "unprecedented".

IBM posted adjusted gross profit margin of 54.5% for the quarter ended June 30, while analysts on average expected 56.6%, according to Refinitiv data.

However, strong demand at its consulting and infrastructure businesses helped IBM post second-quarter revenue of $15.54 billion, beating analysts' average estimate of $15.18 billion.

IBM sees revenue growth continuing, including in regions like Europe and Asia Pacific, despite geopolitical turmoil and inflationary pressures, Kavanaugh said, echoing words of peer Accenture, which had last month said it does not foresee a pull back in client spending.

The 110-years-old company has placed its hopes on high-growth software and consulting businesses with a focus on the so-called "hybrid cloud". Cloud revenue rose 18% to $5.9 billion.

Excluding items, the company earned $2.31 per share, beating estimates of $2.27.

(Reporting by Chavi Mehta in Bengaluru; Editing by Krishna Chandra Eluri)

Mon, 18 Jul 2022 08:21:00 -0500 en-GB text/html https://uk.finance.yahoo.com/news/ibm-beats-quarterly-revenue-estimates-200938752.html
Killexams : SIMPLILEARN COLLABORATES WITH THE UNIVERSITY OF MINNESOTA'S CARLSON SCHOOL OF MANAGEMENT TO LAUNCH BUSINESS ANALYTICS BOOTCAMP
  • The program is best suited for early-mid career professionals with 2+ years of work experience.

  • Upon program completion, learners will receive a certificate from Carlson School of Management and Simplilearn.

  • The program includes masterclasses delivered by distinguished faculty from the University of Minnesota and industry experts from IBM.

  • The bootcamp will also include career assistance from Simplilearn.

SAN FRANCISCO, Aug. 2, 2022 /PRNewswire/ -- Simplilearn, a global digital skills training provider, announced its partnership with the University of Minnesota's Carlson School of Management for a Bootcamp in Business Analytics. The program will provide a well-planned, high-level understanding of business analytics and the real-world application of analytics across multiple domains. Anyone who has completed their bachelor's degree (in any background) is eligible for this program. The bootcamp is best suited for early-mid career professionals having 2+ years of formal work experience, such as IT professionals, Banking and Finance professionals, Marketing Managers, Supply Chain Network Managers, Analysts, and Consultants.

Simplilearn_Logo

The six-month program will be based on a blended format of online self-learning and live virtual classes. The key features of the program include:

  • Program completion certificate from the Carlson School of Management and Simplilearn

  • Membership to University of Minnesota's Alumni Association

  • Masterclasses delivered by Carlson School of Management faculty and industry experts from IBM

  • Industry-recognized IBM certificates for IBM courses

  • Ask-Me-Anything sessions & hackathons conducted by IBM

  • More than twelve industry-relevant projects

  • Simplilearn career assistance

  • Integrated labs

The program curriculum consists of R Programming for Data Science, SQL, Business Analytics with Excel, Data Analytics with Python, Capstone Projects, and other modules.

Speaking about the program, Mr. Anand Narayanan, Chief Product Officer, Simplilearn, said, "In today's business-driven environment, every organization is devising ways to make their decision-making more insightful and impactful. As a result, the role of business analytics continues to grow in importance. Business Analytics provides companies the ability to derive deeper insights and create stronger business recommendations for their own success. Given the industry relevance of the program, we have partnered with the University of Minnesota's Carlson School of Management to curate this Business Analytics Bootcamp that will provide learners an extensive knowledge of the course and widen growth and professional opportunities."

Speaking on the partnership with Simplilearn, Dr. Ravi Bapna, Curtis L. Carlson Chair in Business Analytics and Information Systems at the Carlson School of Management, University of Minnesota said, "Business Analytics can help companies make better, more informed decisions and achieve various goals. Its ability to navigate crises has further increased its importance to the business. Business analytics has certainly changed the dynamics of working in the digital economy and how companies operate."

Simplilearn conducts more than 3000 live classes, with an average of 70,000 learners who together spend more than 500,000 hours each month on the platform. Simplilearn's programs allow learners to upskill and get certified in popular domains. In 2020, Simplilearn introduced a free skills development program called SkillUp. SkillUp lets learners explore in-demand syllabus in top professional and technology fields for free, helping them make the right learning and career decisions.

About the Carlson School of Management, University of Minnesota

The Curtis L. Carlson School of Management at the University of Minnesota is a recognized leader in business education and research. Established in 1919 and located in Minneapolis, the Carlson School is committed to developing leaders who believe business is a force for good through experiential learning, international education, and the school's strong ties to the dynamic Twin Cities business community.

With 13 degree programs that are ranked consistently among the world's best, the school offers bachelor's, master's, and doctoral degrees, as well as executive education programs hosted both domestically and abroad. Today, the Carlson School has nearly 60,000 alumni in more than 100 countries.

About Simplilearn

Founded in 2010 and based in San Francisco, California, and Bangalore, India, Simplilearn, a Blackstone company is the world's #1 online Bootcamp provider for digital economy skills training. Simplilearn offers access to world-class work-ready training to individuals and businesses around the world. The Bootcamps are designed and delivered with world-renowned universities, top corporations, and leading industry bodies via live online classes featuring top industry practitioners, sought-after trainers, and global leaders. From college students and early career professionals to managers, executives, small businesses, and big corporations, Simplilearn role-based, skill-focused, industry-recognized, and globally relevant training programs are ideal upskilling solutions for diverse career or/and business goals.

For more information, please visit www.simplilearn.com/

Logo: https://mma.prnewswire.com/media/1100016/Simplilearn_Logo.jpg

Cision

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SOURCE Simplilearn

Tue, 02 Aug 2022 01:52:00 -0500 en-CA text/html https://ca.finance.yahoo.com/news/simplilearn-collaborates-university-minnesotas-carlson-134900913.html
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