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Database Administrator Courses

Database professionals are in high demand. If you already work as one, you probably know this. And if you are looking to become a database administrator, that high demand and the commensurate salary may be what is motivating you to make this career move. 

How can you advance your career as a database administrator? By taking the courses on this list.

If you want to learn more about database administration to expand your knowledge and move up the ladder in this field, these courses can help you achieve that goal.

Oracle DBA 11g/12c – Database Administration for Junior DBA from Udemy

Udemy’s Oracle DBA 11g/12c – Database Administration for Junior DBA course can help you get a high-paying position as an Oracle Database Administrator. 

Best of all, it can do it in just six weeks.

This database administrator course is a Udemy bestseller that is offered in eight languages. Over 29,000 students have taken it, giving it a 4.3-star rating. Once you complete it and become an Oracle DBA, you will be able to:

  • Install the Oracle database.
  • Manage Tablespace.
  • Understand database architecture.
  • Administer user accounts.
  • Perform backup and recovery.
  • Diagnose problems.

To take the intermediate-level course that includes 11 hours of on-demand video spanning 129 lectures, you should have basic knowledge of UNIX/LINUX commands and SQL.

70-462: SQL Server Database Administration (DBA)

The 70-462: SQL Server Database Administration (DBA) course from Udemy was initially designed to help beginner students ace the Microsoft 70-462 exam. Although that exam has been officially withdrawn, you can still use this course to gain some practical experience with database administration in SQL Server.

Many employers seek SQL Server experience since it is one of the top database tools. Take the 70-462: SQL Server Database Administration (DBA) course, and you can gain valuable knowledge on the syllabu and deliver your resume a nice boost.

Some of the skills you will learn in the 70-462 course include:

  • Managing login and server roles.
  • Managing and configuring databases.
  • Importing and exporting data.
  • Planning and installing SQL Server and related services.
  • Implementing migration strategies.
  • Managing SQL Server Agent.
  • Collecting and analyzing troubleshooting data.
  • Implementing and maintaining indexes.
  • Creating backups.
  • Restoring databases.

DBA knowledge is not needed to take the 10-hour course that spans 100 lectures, and you will not need to have SQL Server already installed on your computer. In terms of popularity, this is a Udemy bestseller with a 4.6-star rating and over 20,000 students.

MySQL Database Administration: Beginner SQL Database Design from Udemy

Nearly 10,000 students have taken the MySQL Database Administration: Beginner SQL Database Design course on Udemy, making it a bestseller on the platform with a 4.6-star rating.

The course features 71 lectures that total seven hours in length and was created for those looking to gain practical, real-world business intelligence and analytics skills to eventually create and maintain databases.

What can you learn from taking the Beginner SQL Database Design course? Skills such as:

  • Connecting data between tables.
  • Assigning user roles and permissions.
  • Altering tables by removing and adding columns.
  • Writing SQL queries.
  • Creating databases and tables with the MySQL Workbench UI.
  • Understanding common Relational Database Management Systems.

The requirements for taking this course are minimal. It can help to have a basic understanding of database fundamentals, and you will need to install MySQL Workbench and Community Server on your Mac or PC.

Database Administration Super Bundle from TechRepublic Academy

If you want to immerse yourself into the world of database administration and get a ton of bang for your buck, TechRepublic Academy’s Database Administration Super Bundle may be right up your alley.

It gives you nine courses and over 400 lessons equaling over 86 hours that can put you on the fast track to building databases and analyzing data like a pro. A sampling of the courses offered in this bundle include:

  • NoSQL MongoDB Developer
  • Introduction to MySQL
  • Visual Analytics Using Tableau
  • SSIS SQL Server Integration Services
  • Microsoft SQL Novice To Ninja
  • Regression Modeling With Minitab

Ultimate SQL Bootcamp from TechRepublic Academy

Here is another bundle for database administrators from TechRepublic Academy. With the Ultimate SQL Bootcamp, you get nine courses and 548 lessons to help you learn how to:

  • Write SQL queries.
  • Conduct data analysis.
  • Master SQL database creation.
  • Use MySQL and SQLite
  • Install WAMP and MySQL and use both tools to create a database.

Complete Oracle Master Class Bundle from TechRepublic Academy

The Complete Oracle Master Class Bundle from TechRepublic Academy features 181 hours of content and 17 courses to help you build a six-figure career. This intermediate course includes certification and will deliver you hands-on and practical training with Oracle database systems.

Some of the skills you will learn include:

  • Understanding common technologies like the Oracle database, software testing, and Java.
  • DS and algorithms.
  • RDBMS concepts.
  • Troubleshooting.
  • Performance optimization.

Learn SQL Basics for Data Science Specialization from Coursera

Coursera’s Learn SQL Basics for Data Science Specialization course has nearly 7,000 reviews, giving it a 4.5-star rating. Offered by UC Davis, this specialization is geared towards beginners who lack coding experience that want to become fluent in SQL queries.

The specialization takes four months to complete at a five-hour weekly pace, and it is broken down into four courses:

  1. SQL for Data Science
  2. Data Wrangling, Analysis, and AB Testing with SQL
  3. Distributed Computing with Spark SQL
  4. SQL for Data Science Capstone Project

Skills you can gain include:

  • Data analysis
  • Distributed computing using Apache Spark
  • Delta Lake
  • SQL
  • Data science
  • SQLite
  • A/B testing
  • Query string
  • Predictive analytics
  • Presentation skills
  • Creating metrics
  • Exploratory data analysis

Once finished, you will be able to analyze and explore data with SQL, write queries, conduct feature engineering, use SQL with unstructured data sets, and more.

Relational Database Administration (DBA) from Coursera

IBM offers the Relational Database Administration (DBA) course on Coursera with a 4.5-star rating. Complete the beginner course that takes approximately 19 hours to finish, and it can count towards your learning in the IBM Data Warehouse Engineer Professional Certificate and IBM Data Engineering Professional Certificate programs.

Some of the skills you will learn in this DBA course include:

  • Troubleshooting database login, configuration, and connectivity issues.
  • Configuring databases.
  • Building system objects like tables.
  • Basic database management.
  • Managing user roles and permissions.
  • Optimizing database performance.

Oracle Autonomous Database Administration from Coursera

Offered by Oracle, the Autonomous Database Administration course from Coursera has a 4.5-star rating and takes 13 hours to complete. It is meant to help DBAs deploy and administer Autonomous databases. Finish it, and you will prepare yourself for the Oracle Autonomous Database Cloud Certification.

Some of the skills and knowledge you can learn from this course include:

  • Oracle Autonomous Database architecture.
  • Oracle Machine Learning.
  • SQL Developer Web.
  • APEX.
  • Oracle Text
  • Autonomous JSON.
  • Creating, deploying, planning, maintaining, monitoring, and implementing an Autonomous database.
  • Migration options and considerations.

Looking for more database administration and database programming courses? Check out our tutorial: Best Online Courses to Learn MySQL.

Disclaimer: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit our Terms of Use page.

Thu, 21 Jul 2022 16:35:00 -0500 en-US text/html https://www.databasejournal.com/ms-sql/database-administrator-courses/
Killexams : How to become a machine learning engineer

As more companies bring artificial intelligence (AI) technologies into the fold, machine learning engineers will continue to be among the most highly sought IT professionals. Currently, the job opportunities are plentiful, and as time continues to pass by, organisations will need more trained machine learning engineers.

Statistics, data science, computer science, mathematics, problem-solving, and deep learning skills are all critical for machine learning engineers. And to be a successful machine learning engineer, one must learn an array of programming languages and execute precision when dealing with complicated data sets and algorithms.

There are plenty of online resources to learn from when launching a machine learning engineer career, but the sheer amount of information available can make absorption and retention difficult. It's also tough to choose the right career path for you, as the number of machine learning opportunities continues to grow and spread to many different industries.

To help you on your path to becoming a machine learning engineer, we’ll answer some key questions about getting into the role, including:

  • What does a machine learning engineer do?
  • What career opportunities are available to machine learning engineers?
  • Do I need a degree to become a machine learning engineer?
  • What skills do I need to become a machine learning engineer?
  • How do I become a machine learning engineer?

Machine learning engineer skills and job requirements

It may not be software development but there’s still no getting away from the need to code in a machine learning engineer role. The programming language or languages that you will be required to work with will vary depending on the organization and its technology stack, but the ones typically used include Python, Java, R, C++, C, JavaScript, Scala, and Julia. And, like with any job, a machine learning engineer will be expected to develop their skill set, melding their theoretical knowledge with hands-on experience.

What skills do you need to become a machine learning engineer?

In addition to coding capabilities and hands on experience, machine learning engineers will be expected to possess a number of key skills and proficiencies. There are a number of areas which commonly tend to play a role in machine learning careers, and it's worth making sure you're familiar with as many of them as possible.

  • Computer science fundamentals and programming: Build data structures (e.g., stacks, queues, multi-dimensional arrays), apply algorithms (e.g., searching, sorting, optimization), understand computability and complexity (e.g., P vs. NP, NP-complete problems, approximate algorithms), and develop computer architecture (e.g., memory, cache, bandwidth)
  • Probability and statistics: Employ techniques used in probability (e.g., Bayes Nets, Markov Decision Processes, Hidden Markov Models), calculate statistical measures and distributions (e.g., uniform, normal, binomial), and apply analytical methods (e.g., ANOVA, hypothesis testing) for building and validating models from observed data 
  • Data modelling and evaluation: Estimate the underlying structure of a given dataset, find useful patterns (e.g., correlations, clusters), predict properties of unseen instances (e.g., classification, regression), choose appropriate accuracy/error measures (e.g., log-loss for classification, sum-of-squared-errors for regression), and evaluate strategies (e.g., training-testing split, sequential vs. randomized cross-validation)
  • Machine learning algorithms and libraries: Find suitable models to apply libraries, packages, and APIs (e.g., Spark MLlib, TensorFlow), create learning procedures to fit the data (e.g., linear regression, gradient descent, genetic algorithms), and develop an awareness of advantages and disadvantages of different approaches (e.g., bias and variance, missing data, data leakage)
  • Software engineering and system design: Understand how elements work together, communicate with systems (e.g., library calls, database queries), and build interfaces

Machine learning applications

There are many potential applications that machine learning can be used for across a wide range of industries. Some of the most common examples include:

  • Image and speech recognition (e.g., auto-tagging images, text-to-speech conversions) 
  • Providing customer insights (e.g., noting a customer purchased product 1 and recommending product 2)
  • Risk management and fraud prevention (e.g., financial predictions, risk of loan defaults)

Quick facts about working as a machine learning engineer

  • The average pay for machine learning engineers in 2020 was $147,134 per year
  • Job postings for machine learning engineers have grown by 344% between 2015 to 2018
  • Machine learning engineers typically require a master’s degree or PhD in computer science, software engineering, or a related field for the best career prospects
  • Most job advertisements in fields involving AI or machine learning are for machine learning engineers

Machine learning engineer jobs

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Given the fact that AI is a field within technology that’s in its relative infancy, both job security and job opportunities are in abundance and expected to grow over the coming years. It’s nigh on impossible to pinpoint an industry that isn’t exploring new innovations in AI, allowing companies to develop new products quicker and in a more streamlined fashion, while embracing the latest and greatest technology.

That said, with the varying skill sets that are demanded by machine learning engineer roles, there are, naturally, plenty of opportunities to specialise in one specific field. There is an array of job roles a machine learning engineer can pursue and it may be worth tailoring your education pathway if one seems more attractive than the others.

Types of machine learning engineer jobs:

  • Machine learning engineer: Use of machine learning algorithms and tools to design and develop systems and applications
  • Data scientist: Use big data, AI, machine learning, and analytical tools to collect, process, analyse, and interpret large amounts of data
  • NLP scientist: Design and develop machines and applications to learn human speech patterns and translate spoken words into other languages
  • Software developer/engineer: Design, develop, and install machine language software solutions, create computer functions, prepare product documentation for visualisation, test code, create technical specifications, and maintain systems
  • Human-centred machine learning designer: Create intelligent systems to learn individuals’ preferences and behavior patterns through information processing and pattern recognition

Machine learning engineer duties

A data scientist and a machine learning engineer share similar duties: they must carry out complex modelling on dynamic data sets, perform data management, and work with vast amounts of data. Furthermore, they are also expected to design self-running software to automate predictive models, which utilise their previous findings to Excellerate their accuracy in performing operations in the future.

The core duties of a machine learning engineer, though, revolve around building algorithms to Excellerate predictive accuracy and analyse data without human intervention. Machine learning is linked to AI and deep learning, where artificial neural networks use deep data sets to solve complex problems and "think".

Aspiring machine learning engineers could be working on applications for image and speech recognition such as technology used for auto-tagging images and text-to-speech conversion software. Engineers could be working on customer insight products used by businesses looking to understand what a customer is likely to want to buy next. They also may be working on technology used by the likes of financial institutions to prevent fraud or assess the risk of an applicant defaulting on a loan, for example.

Machine learning engineer education and learning path

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Most employers of machine learning engineers will be looking for individuals with a strong educational background, with a master’s degree or PhD in a relevant field being the typical prerequisite qualifications for a decent job.

There aren’t too many dedicated courses for machine learning in the UK, as of now, so degrees in subjects such as mathematics, computer science, electrical engineering, physical sciences, and statistics are all considered. In the US, there is a swathe of top universities, such as Carnegie Mellon, that offer dedicated machine learning curriculums. 

Machine learning is unlike software development in that a degree is almost always a requirement to secure a job, although if you can show you have the necessary skills to enter the field, such as demonstrable skills in statistical analysis, exemptions may be made. In these cases, you would typically be expected to have undergone a machine learning-specific course to supplement your understanding of the field. Such courses could include those on an online learning platform or a skills bootcamp like the one offered by Teeside University in the UK.

Going through a master’s degree program will provide programming skills, an understanding of machine learning frameworks such as TensorFlow and Keras, and advanced mathematics skills like linear algebra and Bayesian statistics. 

Professional certifications from industry giants like Amazon, or another accredited association, will also help you to stand out in the field.

How do you become a machine learning engineer?

It’s best to develop a strategy before applying for a position as a machine learning engineer. Determine the industry you want to work in and what type of machine learning engineer you’d like to be.

Once you have a relevant undergraduate degree, you might want to get a position with a career path leading toward becoming a machine learning engineer. This could include working as a software engineer, programmer or developer, data scientist, or computer engineer. 

While you’re working in one of these careers, you can study for a master’s degree or PhD in computer science or software engineering. 

Make sure to stay on top of current algorithms, programming languages, and machine learning libraries. Take continuing education courses and update your professional certifications.

Build your network and learn more about the role by connecting with other machine learning engineers on LinkedIn, which will keep you in the know on job openings and industry expectations. Ask your contacts for advice on building your career as a machine learning engineer.

Tips for applying to machine learning engineering jobs

  • Update your knowledge, skills, and certifications on your resume before applying for a job. Highlight the skills advertised in job postings and list your previous accomplishments.
  • Write a cover letter to explain how your experience makes you ideal for the role. Describe why you want to work with the organisation and why they should hire you.
  • Include relevant references, but ask them for permission first and ensure their contact information is correct.
  • Search job boards that post machine learning engineer jobs. 

Start your journey toward becoming a machine learning engineer

Machine learning offers many opportunities for potential careers, and people in this field earn high wages and have a solid future. Now is a great time to start working toward a career as a machine learning engineer. 

Find out what jobs would most interest you and what roles are available in your desired field, as well as what skills and experience are required. Consider using your skills to analyse the data and formulate a plan to launch your career as a machine learning engineer.

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Fri, 15 Jul 2022 17:23:00 -0500 en text/html https://www.itpro.co.uk/business-strategy/careers-training/358000/how-to-become-a-machine-learning-engineer
Killexams : Full-stack developers continue to be hot property across firms We wrote several months ago that full-stack developers are in big demand. We went back to industry to check how the demand is now, and we found that demand has only become stronger, both in India and the world.

Pravin Yashwant Pawar, assistant professor of computer science at BITS Pilani’s Work Integrated Learning Programmes division, says full-stack developer roles have become one of the most sought after because of the tremendous growth potential and attractive pay-scales. “A survey conducted last year by popular online developer community platform Stack Overflow found that 50% of the respondents to the survey were terming themselves as full-stack developers. One can see over 10,000 job openings on popular portals for job hunters like LinkedIn, Glassdoor, Naukri and the like for fullstack skills for a wide range of experience levels,” he says.

Full-stack developers continue to be hot property across firms

What exactly is a full-stack developer? Ed-tech platform upGrad’s MD & co-founder Mayank Kumar says full-stack developers are proficient in both front-end and back-end development, and are also experts at a variety of coding niches – from databases to graphic design and to UI/UX management. Think of the backend as the plumbing of the website or app you are using that deals with data storage and processing, and the front end as the interface you interact with. It’s this versatility and ability to work on different aspects of web or app development that makes them so sought after.

When it comes to programming languages to learn, Hari Krishnan Nair, co-founder of Great Learning, says a full-stack developer needs to have in-depth knowledge of at least one programming language. “Java and Python are the most widely used programming languages. Object oriented programming, data structures, algorithms, database design, and server-side framework are crucial from a backend software development point of view. HTML, CSS, Javascript and AngularJS/ ReactJS from a front-end point of view. Apart from this, cloud computing fundamentals, Python basics, and SQL are highly sought after in the market today,” he says.

Apart from programming languages and software developer tools, there are other factors that are equally important when it comes to becoming a successful full-stack developer, says Skandh Gupta, senior software engineer at Optum Global Solutions. “Two personal characteristics that always top my mind are curiosity and zeal to learn. One should always focus on two questions: Why? and How? Curiosity helps you gain knowledge of technologies and how the application functions. The question “Why?” to every single step – from choosing tech to designing the application – gives deep knowledge of the flow of the application, and “How?” helps you gain insight on all parts of the application, like front-end, back-end, web architecture to designing applications, servers, databases and test and debug. Full-stack engineers know best practices of engineering excellence and where to apply what,” he says.

Aspiring full-stack developers do need to be aware of a few potential hurdles during their journey though, says Girish Dhanakshirur, IBM distinguished engineer & CTO of IBM India Software Labs. And all of them relate to the rapid changes that take place in the tech world. “First, rapid innovation in browser and server technologies leads to languages and frameworks evolving quickly, hence they must constantly keep their skills current across several technologies. Second, as open-source libraries make up almost all full-stack frameworks, there will be instances when they are not updated. Full-stack developers should be willing to debug and update such libraries when bugs are found. Finally, at times, as part of transitions, developers will end up inheriting projects and source code developed in a language and framework different from the one they are familiar with. In such circumstances, full-stack developers should be able to skill up and switch to deliver on the projects. ”

Wed, 20 Jul 2022 14:40:00 -0500 en text/html https://cio.economictimes.indiatimes.com/news/strategy-and-management/full-stack-developers-continue-to-be-hot-property-across-firms/93016732
Killexams : Communication, Media and Design

The Communication, Media and Design undergraduate bachelor's degree program encompasses 36 of the 120 credit hours required for a bachelor's degree.
This provides you with the opportunity to pursue multiple majors, minors or concentrations while working toward your Communication, Media and Design degree. All courses are 3 credits unless noted.

Clarkson Common Experience
The following courses are required for all students, irrespective of their program of study. These courses are offered during the fall semester, with FY100 First-Year Seminar being required of only first-year students. Both FY100 and UNIV190 are typically taken during the fall semester of the first year at Clarkson.

  • FY100  First-Year Seminar (1 credit)
  • UNIV190 The Clarkson Seminar (3 credits)

Communication, Media and Design Core Requirements
Students majoring in Communication, Media and Design are required to complete the following courses:

  • COMM210 Theory of Rhetoric for Business, Science & Engineering
  • COMM217 Introduction to Public Speaking
  • COMM490 Senior Communication Internship
  • COMM499 Communication Professional Experience

Communication, Media and Design Core Electives
The following are electives students are required to complete for the Communication, Media and Design major.

300-Level Communication, Media and Design Course:
Students must complete one Communication, Media and Design course at the 300-level from the following:

  • COMM312 Public Relations
  • COMM313 Professional Communications
  • COMM314 Placemaking, Marketing and Promotion
  • COMM330 Science Journalism

400-Level Communication, Media and Design Course:
Students must complete one Communication, Media and Design course at the 400-level from the following:

  • COMM410 Theory & Philosophy of Communication
  • COMM412 Organizational Communications & Public Relations Theory
  • COMM428 Environmental Communication

Courses with Technology Expertise:
Students must complete at least 6 credits with information technology expertise.

Mathematics/Statistics Electives:
Students must complete at least 6 credits from the mathematics (MA) and/or statistics (STAT) subject areas.

Science Electives:
Students must complete at least 6 credits, including a lab course, from the biology (BY), chemistry (CM), and/or physics (PH) subject areas.

Knowledge Area/University Course Electives:
Students majoring in communication will have approximately 42 credit hours available to use toward Knowledge Area and/or University Course electives.

Free Electives:
Students majoring in communication will have approximately 42 credit hours available to use toward courses of their choice.

Communication, Media and Design electives (21 credits) chosen from the following:

  • COMM100 2D Digital Design
  • COMM217 Introduction to Public Speaking
  • COMM219 Introduction Social Media
  • COMM226 Short Film Screenwriting
  • COMM229 Principles of User-Experience Design
  • COMM245 Writing for New Media
  • COMM310 Mass Media & Society
  • COMM322 Typography & Design
  • COMM326 Feature Film Screenwriting
  • COMM327 Digital Video Production I
  • COMM329 Front-End Development for the Web
  • COMM345 Information Design
  • COMM360 Sound Design
  • COMM391-395 Special Topics
  • COMM420-425 Communication: Independent Study (1-9 credits)
  • COMM427 Digital Video Production II
  • COMM429 Full-stack Development
  • COMM447 Design-Driven Innovation
  • COMM448 Portraying Innovation Through the Lens
  • COMM449 Narrating Innovation
  • COMM450 Leading Innovation
  • COMM470 Communication Internship
  • COMM480 Undergraduate Teaching Assistantship in Communication & Media (1-3 credits)
Thu, 26 May 2022 08:40:00 -0500 en text/html https://www.clarkson.edu/undergraduate/communication-media-and-design
Killexams : Startups News No result found, try new keyword!Showcase your company news with guaranteed exposure both in print and online Online registration is now closed. If you are looking to purchase single tickets please email… Ready to embrace the ... Thu, 04 Aug 2022 06:27:00 -0500 text/html https://www.bizjournals.com/news/technology/startups Killexams : New $10M NSF-funded Institute Will Get to the CORE of Data Science

Aug. 2, 2022 — A new National Science Foundation initiative has created a $10 million dollar institute led by computer and data scientists at University of California San Diego that aims to transform the core fundamentals of the rapidly emerging field of Data Science.

Called The Institute for Emerging CORE Methods in Data Science (EnCORE), the institute will be housed in the Department of Computer Science and Engineering (CSE), in collaboration with The Halıcıoğlu Data Science Institute (HDSI), and will tackle a set of important problems in theoretical foundations of Data Science.

The EnCORE institute will be directed by CSE and HDSI Associate Professor Barna Saha.

UC San Diego team members will work with researchers from three partnering institutions – University of Pennsylvania, University of Texas at Austin and University of California, Los Angeles — to transform four core aspects of data science: complexity of data, optimization, responsible computing, and education and engagement.

EnCORE will join three other NSF-funded institutes in the country dedicated to the exploration of data science through the NSF’s Transdisciplinary Research in Principles of Data Science Phase II (TRIPODS) program.

“The NSF TRIPODS Institutes will bring advances in data science theory that Excellerate health care, manufacturing, and many other applications and industries that use data for decision-making,” said NSF Division Director for Electrical, Communications and Cyber Systems Shekhar Bhansali.

UC San Diego Chancellor Pradeep K. Khosla said UC San Diego’s highly collaborative, multidisciplinary community is the perfect environment to launch and develop EnCORE. “We have a long history of successful cross-disciplinary collaboration on and off campus, with renowned research institutions across the nation. UC San Diego is also home to the San Diego Supercomputer Center, the HDSI, and leading researchers in artificial intelligence and machine learning,” Khosla said. ”We have the capacity to house and analyze a wide variety of massive and complex data sets by some of the most brilliant minds of our time, and then share that knowledge with the world.”

Barna Saha, the EnCORE project lead and an associate professor in UC San Diego’s Department of Computer Science and Engineering and HDSI, said: “We envision EnCORE will become a hub of theoretical research in computing and Data Science in Southern California. This kind of national institute was lacking in this region, which has a lot of talent. This will fill a much-needed gap.”

The core of EnCORE: co-principal investigators include (from l to r) Yusu Wang, Barna Saha (the principal investigator), Kamalika Chaudhuri, (top row) Arya Mazumdar and Sanjoy Dasgupta. (Not pictured, Gal Mishne).

The other UC San Diego faculty members in the institute include professors Kamalika Chaudhuri, and Sanjoy Dasgupta from CSE; Arya Mazumdar, Gal Mishne, and Yusu Wang from HDSI; and Fan Chung Graham from CSE and the Department of Mathematics. Saura Naderi of HDSI will spearhead the outreach activities of the institute.

“Professor Barna Saha has assembled a team of exceptional scholars across UC San Diego and across the nation to explore the underpinnings of data science. This kind of institute, focused on groundbreaking research, innovative education and effective outreach, will be a model of interdisciplinary initiatives for years to come,” said Department of Computer Science and Engineering Chair Sorin Lerner.

CORE Pillars of Data Science

The EnCORE Institute seeks to investigate and transform three research aspects of Data Science:

  • C, for Complexities of Data: data the researchers are dealing with is complex, of massive size and noisy. They will investigate what new tools and approaches are needed to address data complexity, including an overhaul of the concepts of algorithms, statistics and machine learning.
  • O, for Optimization: a very old and traditional field, it now needs to be data driven, which brings new challenges. Modern data and technology have created a large gulf between theory and practice of optimization. Adaptive methods and human intervention can lead to major advancement in machine learning.
  • R, for Responsible Learning: the ethical responsibility of when researchers are dealing with massive data, data with sensitive information and using that data to make decisions needs to be reoriented to adapt to an uncertain world.

“EnCORE represents exactly the kind of talent convergence that is necessary to address the emerging societal need for responsible use of data. As a campus hub for data science, HDSI is proud of a compelling talent pool to work together in advancing the field,” said HDSI founding director Rajesh K. Gupta.

Team members expressed excitement about the opportunity of interdisciplinary research that the institute will provide. They will work together to Excellerate privacy-preserving machine learning and robust learning, and to integrate geometric and topological ideas with algorithms and machine learning methodologies to tame the complexity in modern data. They envision a new era in optimization with the presence of strong statistical and computational components adding new challenges.

“One of the exciting research thrusts at EnCORE is data science for accelerating scientific discoveries in domain sciences,” said Gal Mishne, an assistant professor at HDSI. As part of EnCORE, the team will be developing fast, robust low-distortion visualization tools for real-world data in collaboration with domain experts. In addition, the team will be developing geometric data analysis tools for neuroscience, a field which is undergoing an explosion of data at multiple scales.

From K-12 and Beyond

A distinctive aspect to EnCORE will be the “E,” education and engagement, component.

The institute will engage students at all levels, from K-12 to postdoctoral students, and junior faculty and conduct extensive outreach activities at all of its four sites.

The geographic span of the institute in three regions of the United States will be a benefit as the institute executes its outreach plan, which includes regular workshops, events, hiring of students and postdoctoral students. Online and joint courses between the partner institutions will also be offered.

Activities to reach out to high school, middle school and elementary students in Southern California are also part of the institute’s plan, with the first engagement planned for this summer with the Sweetwater Union High School District to teach students about the foundations of data science.

There will also be mentorship and training opportunities with researchers affiliated with EnCORE, helping to create a pipeline of data scientists and broadening the reach and impact of the field. Additionally, collaboration with industry is being planned.

Mazumdar, an associate professor in the HDSI and an affiliated faculty member in CSE, said the team has already put much thought and effort into developing data science curricula across all levels. “We aim to create a generation of experts while being mindful of the needs of society and recognizing the demands of industry,” he said.

“We have made connections with numerous industry partners, including prominent data science techs and also with local Southern California industries including start-ups, who will be actively engaged with the institute and keep us informed about their needs,” Mazumdar added.

An interdisciplinary, diverse field- and team

Data science has footprints in computer science, mathematics, statistics and engineering. In that spirit, the researchers from the four participating institutions who comprise the core team have diverse and varied backgrounds from four disciplines.

“Data science is a new, and a very interdisciplinary area. To make significant progress in Data Science you need expertise from these diverse disciplines. And it’s very hard to find experts in all these areas under one department,” said Saha. “To make progress in Data Science, you need collaborations from across the disciplines and a range of expertise. I think this institute will provide this opportunity.”

And the institute will further diversity in science, as EnCORE is being spearheaded by women who are leaders in their fields.


Mon, 01 Aug 2022 12:00:00 -0500 text/html https://www.datanami.com/this-just-in/new-10m-nsf-funded-institute-will-get-to-the-core-of-data-science/
Killexams : 11 Higher-Yielding And Far Better Blue-Chip Alternatives To AT&T
Happy businessman and flying dollar banknotes

Prostock-Studio

AT&T (T) is one of the most controversial stocks on Seeking Alpha and for good reason. This failed aristocrat succumbed to poor management and costly and debt-laden empire building that showcases that even the mightiest blue-chips can fall.

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Charlie Bilello

AT&T was once the largest company in America, and so was IBM (IBM). Sears was once the 6th largest and is now bankrupt as are former dividend kings Winn Dixie and Kmart.

General Electric (GE), another former aristocrat, is still down almost 90% off its tech bubble highs, after briefly becoming the most valuable company on earth.

There are no sacred cows in finance, and the prudent long-term investor follows the fundamentals wherever they lead.

"When the facts change I change my mind, what do you do sir?" - John Maynard Keynes

Several Dividend Kings members have asked me to take another look at AT&T, to see whether or not this fallen aristocrat has a chance of rising like a Phoenix from the ashes and soaring to new heights.

To answer that question there are three things we must look at:

  • the balance sheet
  • the dividend safety
  • the long-term return outlook

So let's take a look at the three things prospective AT&T investors need to know, and why 11 higher-yielding and far superior blue-chips are the best place for your hard-earned savings today.

Fact One: The Balance Sheet Is Improving Slowly But Surely

There is nothing more important for long-term investing success than a strong balance sheet. If a company defaults on its debt, it almost always files for bankruptcy and the stock goes to zero.

"In order to win the game first you must not lose it." - Chuck Noll

AT&T Credit Ratings

Rating Agency Credit Rating 30-Year Default/Bankruptcy Risk Chance of Losing 100% Of Your Investment 1 In
S&P BBB Stable Outlook 7.50% 13.3
Fitch BBB+ Stable Outlook 5.00% 20.0
Moody's Baa2 (BBB equivalent) Stable Outlook 7.50% 13.3
Consensus BBB Stable Outlook 6.67% 15.0

(Source: S&P, Fitch, Moody's)

Rating agencies estimate AT&T's fundamental risk at 6.7%, a 1 in 15 chance of losing all your money in the next 30 years.

Why? Because the spinoff of WarnerMedia along with some of its debt and that nasty dividend cut has helped set AT&T on the path to financial health.

AT&T Consensus Leverage Forecast

Year Debt/EBITDA Net Debt/EBITDA (3.5 Or Less Safe According To Credit Rating Agencies)

Interest Coverage (4+ Safe)

2020 2.88 2.70 0.81
2021 3.46 3.06 3.39
2022 3.69 3.03 3.64
2023 3.44 2.77 4.29
2024 2.88 2.54 4.93
2025 2.86 2.48 4.70
2026 2.89 2.64 3.61
2027 2.62 NA 4.81
Annualized Change -1.37% -0.38% 29.03%

(Source: FactSet Research Terminal)

AT&T's leverage peaked at 3.7 pre-spin-off and is expected to fall rapidly. Its interest coverage ratio is expected to remain stable around the 4 minimum safety guideline for stable BBB-rated companies.

AT&T Consensus Leverage Forecast

Year Total Debt (Millions) Cash Net Debt (Millions) Interest Cost (Millions) EBITDA (Millions) Operating Income (Millions)
2020 $157,245 $9,740 $147,505 $7,925 $54,546 $6,405
2021 $177,977 $21,169 $157,379 $6,884 $51,469 $23,347
2022 $155,499 $19,970 $127,519 $6,202 $42,154 $22,569
2023 $148,884 $20,063 $119,858 $5,601 $43,247 $24,015
2024 $127,251 $16,795 $112,489 $5,140 $44,245 $25,347
2025 $127,251 $19,874 $110,491 $5,482 $44,535 $25,744
2026 $127,251 $31,778 $116,500 $7,067 $44,080 $25,520
2027 $127,251 $82,067 NA $6,194 $48,613 $29,815
Annualized Growth -2.98% 35.59% -3.86% -3.46% -1.63% 24.57%

(Source: FactSet Research Terminal)

Rising interest rates in the future are expected to keep AT&T's ability to service its debt manageable, but not so easy as to likely result in credit rating upgrades.

x

(Source: FactSet Research Terminal)

The bond market is getting a bit more thinking about AT&T's ability to service its debt, possibly due to rising recession concerns.

1-year default risk has risen by 150% in the last six months according to the bond market and 10-year default risk is up 49%.

However, the bond market is estimating a 30-year default risk at just over 4.5%, which is consistent with its existing credit ratings.

Or to put it another way, analysts, management, rating agencies, and the bond market all think that AT&T's turnaround plan remains on track, though management's initial guidance for 5% long-term growth appears to be unlikely.

Fact Two: Dividend Safety Remains Shaky At Best

The safest dividends are often the ones that's just been raised and the most dangerous can be from companies that have already cut in the latest past.

With AT&T almost halving its dividend and breaking the hearts of many dividend aristocrat investors, one of the most important questions we need to be answered is how safe is the dividend and is it likely to grow over time?

AT&T Dividend Consensus Forecast

Year Dividend Consensus FCF/Share Consensus FCF Payout Ratio Retained (Post-Dividend) Free Cash Flow Buyback Potential Debt Repayment Potential
2022 $1.22 $2.11 57.8% $6,372 4.34% 4.1%
2023 $1.10 $2.48 44.4% $9,879 6.73% 6.6%
2024 $1.10 $2.36 46.6% $9,020 6.14% 6.1%
2025 $1.09 $2.50 43.6% $10,094 6.87% 7.9%
2026 $1.18 $2.32 50.9% $8,161 5.56% 6.4%
2027 $1.20 $2.90 41.4% $12,170 8.28% 9.6%
Total 2022 Through 2027 $6.89 $14.67 47.0% $55,697.02 37.91% 37.41%
Annualized Rate -0.33% 6.57% -6.47% 13.82% 13.82% 18.47%

(Source: FactSet Research Terminal)

The good news is that most analysts don't expect AT&T to cut more. The bad news is that some due and the consensus is that the payout will basically stay flat for the next five years.

That's despite a payout ratio of under 50% compared to 70% that rating agencies consider safe for telecoms.

AT&T is expected to retain $56 billion in post-dividend free cash flow over the next five years. That's enough to potentially pay off 37% of its debt or buy back up to 38% of its stock at current valuations.

But don't get too excited about the potential for mega-buybacks.

AT&T Buyback Consensus Forecast

Year Consensus Buybacks ($ Millions) % Of Shares (At Current Valuations) Market Cap
2022 $205.0 0.1% $146,903
2023 $74.0 0.1% $146,903
2024 $111.0 0.1% $146,903
2025 $611.0 0.4% $146,903
2026 $611.0 0.4% $146,903
Total 2022-2026 $1,612.00 1.1% $146,903
Annualized Rate 0.16% Average Annual Buybacks $322.40

(Source: FactSet Research Terminal)

Analysts only expect $1.6 billion in total buybacks through 2026, roughly enough for 1% of the shares at current valuations.

So where is that retained cash flow going? Well, management hopes into growing the core telecom business. On that front, there is some good and bad news.

Fact Three: AT&T MIGHT Make A Decent Long-Term Investment If You Have Realistic Expectations

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(Source: FactSet Research Terminal)

AT&T's spin-off of WarnerMedia means that it's not expected to recover that free cash flow, not even close. Even in 2027, analysts expect free cash flow will be 24% below 2020's record, eight years of negative free cash flow growth.

But that doesn't mean that analysts don't expect AT&T to grow its earnings.

x

(Source: FactSet Research Terminal)

It will take until 2027 according to analysts for AT&T to hit a new record EPS, surpassing 2019's $2.7 per share.

But over the long-term, the median consensus from all 29 analysts is that AT&T can grow at 3.4%.

  • Verizon (VZ)'s consensus is 4.0%

What does this potentially mean for long-term AT&T investors?

Investment Strategy Yield LT Consensus Growth LT Consensus Total Return Potential Long-Term Risk-Adjusted Expected Return Long-Term Inflation And Risk-Adjusted Expected Returns Years To Double Your Inflation & Risk-Adjusted Wealth

10-Year Inflation And Risk-Adjusted Expected Return

AT&T 5.4% 3.4% 8.8% 6.2% 3.7% 19.5 1.44
Verizon 5.0% 4% 9.0% 6.3% 3.8% 18.8 1.46
Dividend Aristocrats 2.6% 8.6% 11.2% 7.8% 5.4% 13.4 1.69
S&P 500 1.8% 8.5% 10.3% 7.1% 4.7% 15.4 1.58

(Source: Morningstar, FactSet, Ycharts)

That AT&T could potentially deliver decent long-term total returns of about 9%, slightly less than Verizon and a lot less than the dividend aristocrats or S&P 500.

How realistic is it to believe that AT&T can deliver 9% long-term returns?

AT&T And Verizon Total Returns Since May 1985

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(Source: Portfolio Visualizer Premium)

AT&T has underperformed VZ by 0.3% annually for 37 years, and analysts expect it to keep doing so in the future.

It's delivered 9% long-term returns just as analysts expect from it today.

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(Source: Portfolio Visualizer Premium)

AT&T's rolling returns are consistent with what analysts expect in the future, with modest returns almost all coming from dividends. However, remember that these are long-term returns, and in the short-term, any company can disappoint, even for a decade.

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(Source: Portfolio Visualizer Premium)

AT&T is finally having a moment in the sun, up 19% in the last three months and up almost 17% YTD. But it's delivered almost zero inflation-adjusted returns over the last decade, thanks to former management's penchant for expensive debt-funded M&A.

Now AT&T is focused on its circle of competence, telecom, and analysts and rating agencies are the most optimistic they've been in about five years at AT&T's prospects, though admittedly that's damning with faint praise.

But the good news is that AT&T investors likely don't have to wait for decades to earn solid returns, potentially even Buffett-like short-term gains.

AT&T 2024 Consensus Return Potential

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(Source: FAST Graphs, FactSet Research)

AT&T offers about 20% annual total return potential according to analysts over the next 2.5 years.

However, just because AT&T isn't a dumpster fire of a company doesn't mean it's actually worth buying.

Let me show you how to stop settling for low-quality yield and instead harness the power of the world's mightiest and highest quality high-yield blue-chips.

How To Find Some Of The World's Best High-Yield Blue-Chips In All Market Conditions

I use the Dividend Kings Zen Research Terminal to always find the best blue-chips for any given goal, time horizon or risk profile. This super easy and convenient tool runs of the Dividend Kings 500 Master List.

The DK 500 Master List is one of the world's best watchlists including

  • every dividend aristocrat (S&P companies with 25+ year dividend growth streaks)
  • every dividend champion (every company, including foreign, with 25+ year dividend growth streaks)
  • every dividend king (every company with 50+ year dividend growth streaks)
  • every foreign aristocrat (every company with 20+ year dividend growth streaks)
  • every Ultra SWAN (wide moat aristocrats, as close to perfect quality companies as exist)
  • 40 of the world's best growth stocks

Let me show you the screen I used to find higher-yielding and far superior alternatives to AT&T.

  1. yield of 5.5+% (vs 5.4% AT&T): 33 companies remain
  2. 8.9+% long-term consensus return potential (vs 8.8% AT&T): 30 companies remain
  3. investment-grade credit ratings: 23 companies remain
  4. good buys or better (margin of safety is sufficient to compensate for each company's risk profile): 15 companies remain
  5. safety score 81+ (very safe dividends): 0.5% historically average recession cut risk and 1% to 2% risk in a severe recession: 11 companies remain
  6. 80+ quality score (Super SWAN quality or better): 11 companies remain

Total time: 2 minutes

11 Higher-Yielding And Far Superior Alternatives To AT&T

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Dividend Kings Zen Research Terminal

I've linked to articles about each company's investment thesis, long-term growth prospects, risk profile, valuation, and total return potential.

Note that LGGNY is the ADR version and LGEN is the London Stock Exchange version. The ADR fees on LGGNY amount to about 5% of the dividend, so if your broker allows it, buy LGEN to avoid the ADR fee.

ENB, MFC, and PBA, have 15% dividend tax withholdings.

  • not in retirement accounts
  • in taxable accounts, you get a tax credit to recoup the withholding

ALIZY has a 26.375% dividend withholding.

  • a tax credit is only available in taxable accounts
  • optically own non-Canadian foreign dividend stocks (except the UK which have no withholding) in taxable accounts

FAST Graphs Up Front

Magellan Midstream Partners 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

Altria 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

Legal & General 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

Enterprise Products Partners 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

British American Tobacco 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

ONEOK 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

Allianz 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

Enbridge 2024 Consensus Total Return Potential

x

(Source: FAST Graphs, FactSet Research)

Manulife Financial 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

Pembina Pipeline 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

Bank of Nova Scotia 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

  • average 2024 consensus return potential: 21.% CAGR
  • literally, Buffett-like short-term return potential from 11 high-yield blue-chip bargains hiding in plain sight.

S&P 500 2024 Consensus Total Return Potential

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(Source: FAST Graphs, FactSet Research)

Analysts expect about 12.5% annual returns from the S&P over the next 2.5 years, nearly 50% less than what these high-yield blue-chips potentially offer.

They also offer about 4% higher return potential through 2024 than AT&T, though with a far better track record of actually delivering market-crushing returns and dependable income growth.

OK, so now let me show you why these are such better alternatives to AT&T.

Some Of The World's Highest Quality And Most Dependable High-Yield Blue-Chips

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Dividend Kings Zen Research Terminal

These aren't just 7% yielding blue-chips, but 7% yielding Ultra SWANs (sleep well at night), as close to perfect quality dividend growth stocks as can exist on Wall Street.

How do I know? Because they are higher quality than the dividend aristocrats.

Higher Quality Than Dividend Aristocrats And Much Higher Quality Than AT&T

Metric Dividend Aristocrats 11 High-Yield AT&T Alternatives AT&T Winner Aristocrats Winner 11 High-Yield AT&T Alternatives Winner AT&T
Quality 87% 88% 57% 1
Safety 89% 90% 56% 1
Dependability 84% 88% 55% 1
Long-Term Risk Management Industry Percentile 67% Above-Average 73% Good 75% Good 1
Average Credit Rating A- Stable BBB+ Stable BBB Stable 1
Average 30-Year Bankruptcy Risk 3.01% 4.09% 7.50% 1
Average Dividend Growth Streak (Years) 44.3 15.3 0 1
Average Return On Capital 100% 478% 20% 1
Average ROC Industry Percentile 83% 87% 60% 1
13-Year Median ROC 89% 296% 19% 1
Forward PE 18.8 8.4 8.1 1
Discount To Fair Value 8.0% 26.0% 18.0% 1
DK Rating Good Buy Very Strong Buy Reasonable Buy 1
Yield 2.6% 7.0% 5.4% 1
LT Growth Consensus 8.6% 6.6% 3.4% 1
Total Return Potential 11.2% 13.6% 8.8% 1
Risk-Adjusted Expected Return 7.6% 9.1% 5.7% 1
Inflation & Risk-Adjusted Expected Return 5.1% 6.7% 3.2% 1
Years To Double 14.0 10.8 22.3 1
Total 4 13 2

(Source: Dividend Kings Zen Research Terminal)

These aren't just safe 7% yielding blue-chips, they are some of the safest 7% yielding companies on earth. How safe?

Rating Dividend Kings Safety Score (162 Point Safety Model) Approximate Dividend Cut Risk (Average Recession) Approximate Dividend Cut Risk In Pandemic Level Recession
1 - unsafe 0% to 20% over 4% 16+%
2- below average 21% to 40% over 2% 8% to 16%
3 - average 41% to 60% 2% 4% to 8%
4 - safe 61% to 80% 1% 2% to 4%
5- very safe 81% to 100% 0.5% 1% to 2%
11 Higher-Yielding AT&T Alternatives 90% 0.5% 1.5%
Risk Rating Low-Risk (73rd industry percentile risk-management consensus) BBB+ stable outlook credit rating 4.1% 30-year bankruptcy risk 20% OR LESS Max Risk Cap Recommendation (Each)

(Source: Dividend Kings Zen Research Terminal)

In the average recession since WWII, the approximate risk of these high-yield blue-chips cutting their dividend is 1 in 200. In a severe Great Recession or Pandemic level downturn, it's approximately 1 in 67.

Their average dividend growth streak is 15 years. How significant is that?

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Justin Law

During the pandemic, companies with 12+ year dividend growth streaks were significantly less likely to cut their dividends.

  • all of these companies have a progressive dividend policy
  • dividends are never cut unless absolutely necessary
  • and grow in-line with earnings over time

Joel Greenblatt considered return on capital his gold standard proxy for quality and moatiness.

  • annual pre-tax income/the cost of running the business

One of the greatest investors in history, 40% annual returns for 21 years, used valuation and ROC as his core investing strategy.

For context, the S&P 500 has 14.6% return on capital and AT&T 20%.

The dividend aristocrats have 100% ROC and these high-yield Ultra SWANs a spectacular 478%.

Their 13-year median ROC is 296% vs the aristocrats' 89% and AT&T's 19%.

Their ROC is in the 87th industry percentile vs the aristocrats' 80% and AT&T's 60%.

What does this mean? Some of the world's highest quality, most profitable, and widest moat companies.

S&P estimates their average 30-year bankruptcy risk (Buffett's definition of fundamental risk) is 4.1%, a BBB+ stable credit rating vs the aristocrats' A- stable and AT&T's BBB stable.

And six rating agencies estimate their long-term risk management is in the 73rd industry percentile vs 75% for AT&T and 67% for the dividend aristocrats.

Classification Average Consensus LT Risk-Management Industry Percentile

Risk-Management Rating

S&P Global (SPGI) #1 Risk Management In The Master List 94 Exceptional
Strong ESG List 78

Good - Bordering On Very Good

Foreign Dividend Stocks 75 Good
AT&T 75 Good
11 Higher-Yielding AT&T Alternatives 73 Good
Ultra SWANs 71 Good
Low Volatility Stocks 68 Above-Average
Dividend Aristocrats 67 Above-Average
Dividend Kings 63 Above-Average
Master List average 62 Above-Average
Hyper-Growth stocks 61 Above-Average
Monthly Dividend Stocks 60 Above-Average
Dividend Champions 57 Average

(Source: DK Research Terminal)

OK, so now that you understand just why these 11 higher-yielding and much higher quality companies are so much better than AT&T, here's why you might want to buy them today.

Wonderful Companies At Wonderful Prices

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Dividend Kings Zen Research Terminal

AT&T trades at 8.1X forward earnings, an anti-bubble valuation, that Morningstar estimates is an 18% discount to its fair value of $25.

The S&P trades at 16.0X forward earnings, a 4% historical discount to its 10, 25, and 45-year average forward PE.

The dividend aristocrats trade at 18.8X forward earnings, an 8% historical discount.

These high-yield blue-chips trade at 8.4X earnings, a 26% historical discount.

That's why analysts expect 32% total returns in the next 12 months, but 44% total returns are fundamentally justified by their fundamentals.

If these companies all grow as expected and return to their historical fair value within 12 months, then investors will make 44% returns in a year.

What about the aristocrats?

  • average 12-month median analyst forecast: 22.4%
  • fundamentally justified 12-month total return: 14.9%

But my goal isn't to help you earn 32% or even 44% in 12 months, though these high-yield blue-chips are fundamentally capable of that.

My goal is to help you retire in safety and splendor by earning potentially 45X returns over decades.

Long-Term Return Potential That Puts AT&T To Shame And Can Help You Retire In Safety And Splendor

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Dividend Kings Zen Research Terminal

Not only do these 11 blue-chips yield 7%, 33% more than AT&T, but analysts expect them to grow 6.6%, almost 2X as fast as AT&T.

That means 13.6% consensus return potential and 6.7% risk and inflation-adjusted expected returns. What are real expected returns?

  • analyst consensus adjusted for the probability of companies not growing as expected
  • not returning to fair value
  • going bankrupt
  • the bond market's 30-year inflation forecast

In other words, it's a reasonable estimate of what you can expect to make.

Dividend 72% by the real expected return and you get how long it's likely to take for you to double your inflation-adjusted savings.

  • S&P 500's doubling time is 15.3 years
  • aristocrats 14.0 years
  • AT&T's 22.3 years
  • these 11 high-yield blue-chips 11.2 years

Think that doubling your money 3 or 4 years faster than the aristocrats or S&P 500 doesn't matter? Well just take a look at what kind of life-changing difference in wealth it could mean for you.

Inflation-Adjusted Consensus Total Return Potential: $510,000 Average Retired Couple's Savings Initial Investment

Time Frame (Years) 7.7% CAGR Inflation-Adjusted S&P Consensus 8.7% Inflation-Adjusted Aristocrats Consensus 11.1% CAGR Inflation-Adjusted 11 Higher-Yielding AT&T Alternatives Consensus Difference Between Inflation-Adjusted 11 Higher-Yielding AT&T Alternatives Consensus Vs S&P Consensus
5 $740,037.07 $775,027.51 $864,423.86 $124,386.79
10 $1,073,833.07 $1,177,779.68 $1,465,154.15 $391,321.08
15 $1,558,188.78 $1,789,826.76 $2,483,361.20 $925,172.42
20 $2,261,014.63 $2,719,931.31 $4,209,169.97 $1,948,155.33
25 $3,280,852.25 $4,133,375.65 $7,134,327.39 $3,853,475.14
30 $4,760,690.75 $6,281,332.99 $12,092,319.31 $7,331,628.55

(Source: DK Research Terminal, FactSet)

For the average retired couple, it means potentially $7.3 million in inflation-adjusted wealth over a 30-year retirement.

Time Frame (Years) Ratio Aristocrats/S&P Consensus Ratio Inflation-Adjusted 11 Higher-Yielding AT&T Alternatives Consensus vs S&P consensus
5 1.05 1.17
10 1.10 1.36
15 1.15 1.59
20 1.20 1.86
25 1.26 2.17
30 1.32 2.54

(Source: DK Research Terminal, FactSet)

That's potentially 2.5X more than the S&P 500 and 2X better than analysts expect from the dividend aristocrats.

Do you see how the right high-yield blue-chips can help you retire in safety and splendor?

OK, but that assumes these companies deliver almost 14% long-term returns for decades. What evidence is there that they can actually do that?

Historical Returns Since November 2003 (Equal Weighting, Annual Rebalancing)

"The future doesn't repeat, but it often rhymes." - Mark Twain

Past performance is no ensure of future results, but studies show that blue-chips with relatively stable fundamentals over time offer predictable returns based on yield, growth, and valuation mean reversion.

valuation is axlmost allx that matters for long-term stock returns

Bank of America

So let's see how these 11 higher-yielding AT&T alternatives performed over the last two decades when 91% of their returns were the result of fundamentals, not luck.

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(Source: Portfolio Visualizer Premium)

Analysts expect 13.6% long-term returns and they delivered...13.4% CAGR. That's more than 2X the annual return of AT&T and almost 4% higher than the S&P 500.

And they did it with slightly lower volatility than AT&T and 2X higher negative-volatility-adjusted total returns (Sortino ratio).

  • 32% higher negative-volatility adjusted annual returns than the S&P 500
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(Source: Portfolio Visualizer Premium)

Analysts expect about 4X inflation-adjusted returns from the S&P in the next 20 years. Over the last 11 years, the market delivered 3.3X returns.

Analysts expect AT&T to double your money roughly every 22 years, and in the last 19 years, it delivered exactly 2X inflation-adjusted returns.

Analysts expect these 11 high-yield blue-chips to potentially deliver about 8.3X inflation-adjusted returns. Over the last 20? 6.6X and that's factoring in their current 11% bear market.

  • without the current bear market, they would have delivered 7.5X inflation-adjusted returns.
  • within 10% of what the Gordon Dividend growth model predicted
  • over 19 years
  • the most accurate long-term forecasting model ever devised
  • which is used by almost every asset manager
  • BlackRock, Vanguard, Oaktree, Brookfield, Fidelity, Schwab, etc.
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(Source: Portfolio Visualizer Premium)

Their average rolling returns were 12% to 15%, 2X more than AT&T's.

Their worst 15-year returns?

  • 3.82X return for these 11 high-yield blue-chips
  • 1.4X return for AT&T
  • 2.9X return for S&P 500
x

(Source: Portfolio Visualizer Premium)

In 2022, when the market is down almost 20%? These 11 high-yield blue-chips are up 4%. Does that mean these blue-chips are bear market-proof?

No company is bear market proof, as you can see from how poorly these companies did in the Pandemic.

x

(Source: Portfolio Visualizer Premium)

  • which is largely why they are still such attractive bargains today
  • and yield one of the safest 7% yields in the world

But does that mean these aren't defensive blue-chips? Not at all.

x

(Source: Portfolio Visualizer Premium)

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(Source: Portfolio Visualizer Premium)

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(Source: Portfolio Visualizer Premium)

These high-yield blue-chips are currently in an 11% bear market vs 33% for AT&T and 20% for the S&P.

The longest they've ever taken to recover record highs after a bear market is 2.5 years, vs 5 years for the S&P 500 and 11.5 years for AT&T.

So higher and much safer yield, stronger returns, smaller declines (usually), and faster bear market recoveries.

And let's not forget the most important part about high-yield investing, long-term income growth!

High-Yield Dividend Growth Blue-Chips You Can Trust

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(Source: Portfolio Visualizer Premium) 2008 was MO's PM spin-off

If your goal is maximum safe income why would you choose AT&T over these 11 high-yield blue-chip alternatives?

Portfolio 2004 Income Per $1,000 Investment 2021 Income Per $1,000 Investment Annual Income Growth Starting Yield

2021 Yield On Cost

S&P 500 $21 $77 7.94% 2.1% 7.7%
AT&T $53 $222 8.79% 5.3% 22.2%
11 Higher-Yielding AT&T Alternatives $83 $654 12.91% 8.3% 65.4%

(Source: Portfolio Visualizer Premium)

They delivered almost 2X the annual income growth of the S&P and AT&T and turned an 8.3% starting yield into a yield on cost of 65% over the last 17 years.

What about future income growth?

Analyst Consensus Income Growth Forecast Risk-Adjusted Expected Income Growth Risk And Tax-Adjusted Expected Income Growth

Risk, Inflation, And Tax Adjusted Income Growth Consensus

13.1% 9.2% 7.8% 5.3%

(Source: DK Research Terminal, FactSet)

Analysts expect 13% income growth from these blue-chips in the future, just as they've delivered for almost two decades. When adjusted for the risk of it not growing as expected, inflation and taxes is about 5.3% real expected income growth.

Now compare that to what they expect from the S&P 500.

Time Frame S&P Inflation-Adjusted Dividend Growth S&P Inflation-Adjusted Earnings Growth
1871-2021 1.6% 2.1%
1945-2021 2.4% 3.5%
1981-2021 (Modern Falling Rate Era) 2.8% 3.8%
2008-2021 (Modern Low Rate Era) 3.5% 6.2%
FactSet Future Consensus 2.0% 5.2%

(Sources: S&P, FactSet, Multipl.com)

  • 1.7% tax and inflation-adjusted S&P consensus income growth

What about a 60/40 retirement portfolio?

  • 0.5% consensus inflation, risk, and tax-adjusted income growth.

In other words, these 11 higher-yielding superior AT&T alternatives offer

  • almost 4X the market's yield (and a much safer yield at that)
  • 1.33X AT&T's yield (and a much, much safer yield at that)
  • about 3X the S&P's long-term inflation-adjusted consensus income growth potential
  • 11X better long-term inflation-adjusted income growth than a 60/40 retirement portfolio

This is the power of high-yield blue-chip investing done right.

Bottom Line: Don't Gamble On AT&T's Turnaround Story When You Can Buy These Higher-Yielding, Far Superior Alternatives Instead

Let me be very clear, AT&T is not a dangerous company that's likely going to zero. Rating agencies estimate a 92.5% probability AT&T will survive the next three decades.

But what I am saying is that after a careful examination of its fundamentals, I can think of just one group of investors who should own AT&T right now. Index fund investors who own it as part of an ETF or mutual fund.

We all have limited funds, and for new money today there are almost no reasons to buy AT&T over these 11 higher-yielding, much higher quality, much faster growing, and much safer Ultra SWANs.

The Evidence Is Clear: These Are 11 Much Better Alternatives To AT&T

Metric Dividend Aristocrats 11 High-Yield AT&T Alternatives AT&T Winner Aristocrats Winner 11 High-Yield AT&T Alternatives Winner AT&T
Quality 87% 88% 57% 1
Safety 89% 90% 56% 1
Dependability 84% 88% 55% 1
Long-Term Risk Management Industry Percentile 67% Above-Average 73% Good 75% Good 1
Average Credit Rating A- Stable BBB+ Stable BBB Stable 1
Average 30-Year Bankruptcy Risk 3.01% 4.09% 7.50% 1
Average Dividend Growth Streak (Years) 44.3 15.3 0 1
Average Return On Capital 100% 478% 20% 1
Average ROC Industry Percentile 83% 87% 60% 1
13-Year Median ROC 89% 296% 19% 1
Forward PE 18.8 8.4 8.1 1
Discount To Fair Value 8.0% 26.0% 18.0% 1
DK Rating Good Buy Very Strong Buy Reasonable Buy 1
Yield 2.6% 7.0% 5.4% 1
LT Growth Consensus 8.6% 6.6% 3.4% 1
Total Return Potential 11.2% 13.6% 8.8% 1
Risk-Adjusted Expected Return 7.6% 9.1% 5.7% 1
Inflation & Risk-Adjusted Expected Return 5.1% 6.7% 3.2% 1
Years To Double 14.02 10.80 22.30 1
Total 4 13 2

(Source: DK Zen Research Terminal)

Don't get me wrong, I'm not saying that you have to buy all of these companies.

This article is about providing 11 higher-yielding and far superior quality alternatives to AT&T and that's exactly what MMP, MO, LGGNY, EPD, BTI, OKE, ALIZY, ENB, MFC, PBA, and BNS represent.

Some investors absolutely detest K1 tax forms, and if that describes you then ignore MMP and EPD.

Some investors just can't stand dividend tax withholdings and extra complexity at tax time, and if that's the case then ignore PBA, BNS, and ENB.

Some investors avoid tobacco for personal ethical reasons, and in that case MO and BTI are not for you.

The point is that any one of these high-yield blue-chips is a superior alternative to AT&T.

  • higher yield
  • faster growth
  • a safer dividend
  • faster growing dividends
  • credit ratings as good or better (in some cases much better)
  • much higher quality and dependability
  • higher long-term return potential
  • superior long-term returns

When you have limited capital you need to be reasonable and prudent with where you invest it.

Can AT&T make a good investment from here? That depends on whether the turnaround succeeds and the company delivers on its expected growth.

  • 8.8% long-term consensus return potential is in-line with its historical returns

Could AT&T make a potentially fantastic short-term investment? Sure, because a return to fair value could mean 20% annual returns for the next 2.5 years.

  • 24% from these higher-yielding alternatives

But whether you are shooting for huge short-term upside or life-changing long-term wealth and income compounding by earning thousands of percent over decades, one thing is clear.

These 11 higher-yielding blue-chips are far superior alternatives to AT&T today.

Wed, 13 Jul 2022 22:00:00 -0500 en text/html https://seekingalpha.com/article/4523270-11-better-blue-chip-alternatives-to-att
Killexams : Industrial Engineering Bachelor of Science Degree Course Sem. Cr. Hrs. First Year CHMG-131

General Education – Elective: General Chemistry for Engineers

This rigorous course is primarily for, but not limited to, engineering students. Topics include an introduction to some basic concepts in chemistry, stoichiometry, First Law of Thermodynamics, thermochemistry, electronic theory of composition and structure, and chemical bonding. The lecture is supported by workshop-style problem sessions. Offered in traditional and online format. Lecture 3 (Fall, Spring).

3 ISEE-120

Fundamentals of Industrial Engineering

This course introduces students to industrial engineering and provides students with foundational tools used in the profession. The course is intended to prepare students for their first co-op experience in industrial engineering by exposing them to tools and concepts that are often encountered during early co-op assignments. The course covers specific tools and their applications, including systems design and the integration. The course uses a combination of lecture and laboratory activities. Projects and group exercises will be used to cover hands-on applications and problem-solving related to Topics covered in lectures. (This class is restricted to ISEE-BS, ENGRX-UND, or ISEEDU Major students.) Lecture 3 (Fall, Spring).

3 ISEE-140

Materials Processing

A study of the application of machine tools and fabrication processes to engineering materials in the manufacture of products. Processes covered include cutting, molding, casting, forming, powder metallurgy, solid modeling, engineering drawing, and welding. Students make a project in the lab portion of the course. (This class is restricted to ISEE-BS, ENGRX-UND, or ISEEDU Major students.) Lab 1 (Fall).

3 MATH-181

General Education – Mathematical Perspective A: Project-Based Calculus I

This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisite: A- or better in MATH-111 or A- or better in ((NMTH-260 or NMTH-272 or NMTH-275) and NMTH-220) or a math placement exam score greater than or equal to 70 or department permission to enroll in this class.) Lecture 6 (Fall, Spring, Summer).

4 MATH-182

General Education – Mathematical Perspective B: Project-Based Calculus II

This is the second in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C- or better in (MATH-181 or MATH-173 or 1016-282) or (MATH-171 and MATH-180) or equivalent course(s).) Lecture 6 (Fall, Spring, Summer).

4 PHYS-211

General Education – Scientific Principles Perspective: University Physics I

This is a course in calculus-based physics for science and engineering majors. Topics include kinematics, planar motion, Newton's Laws, gravitation, work and energy, momentum and impulse, conservation laws, systems of particles, rotational motion, static equilibrium, mechanical oscillations and waves, and data presentation/analysis. The course is taught in a workshop format that integrates the material traditionally found in separate lecture and laboratory courses. (Prerequisites: C- or better in MATH-181 or equivalent course. Co-requisites: MATH-182 or equivalent course.) Lec/Lab 6 (Fall, Spring).

4 YOPS-010

RIT 365: RIT Connections

RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based professional competencies. Lecture 1 (Fall, Spring).

0  

General Education – First Year Writing (WI)

3  

General Education – Artistic Perspective

3  

General Education – Ethical Perspective

3  

General Education – Elective

3 Second Year EGEN-99

Engineering Co-op Preparation

This course will prepare students, who are entering their second year of study, for both the job search and employment in the field of engineering. Students will learn strategies for conducting a successful job search, including the preparation of resumes and cover letters; behavioral interviewing techniques and effective use of social media in the application process. Professional and ethical responsibilities during the job search and for co-op and subsequent professional experiences will be discussed. (This course is restricted to students in Kate Gleason College of Engineering with at least 2nd year standing.) Lecture 1 (Fall, Spring).

0 ISEE-200

General Education – Elective: Computing for Engineers

This course aims to help undergraduate students in understanding the latest software engineering techniques and their applications in the context of industrial and systems engineering. The Topics of this course include the fundamental concepts and applications of computer programming, software engineering, computational problem solving, and statistical techniques for data mining and analytics. (This class is restricted to ISEE-BS, ENGRX-UND, or ISEEDU Major students.) Lecture 3 (Spring).

3 ISEE-325

Engineering Statistics and Design of Experiments

This course covers statistics for use in engineering as well as the primary concepts of experimental design. The first portion of the course will cover: Point estimation; hypothesis testing and confidence intervals; one- and two-sample inference. The remainder of the class will be spent on concepts of design and analysis of experiments. Lectures and assignments will incorporate real-world science and engineering examples, including studies found in the literature. (Prerequisites: STAT-251 or MATH-251 or equivalent course.) Lecture 3 (Fall, Spring).

3 ISEE-345

Engineering Economy

Time value of money, methods of comparing alternatives, depreciation and depletion, income tax consideration and capital budgeting. Cannot be used as a professional elective for ISE majors. Course provides a foundation for engineers to effectively analyze engineering projects with respect to financial considerations. Lecture 3 (Fall, Spring).

3 ISEE-499

Co-op (summer)

One semester of paid work experience in industrial engineering. (Prerequisites: ISEE-120 and EGEN-99 and students in the ISEE-BS program.) CO OP (Fall, Spring, Summer).

0 MATH-221

General Education – Elective: Multivariable and Vector Calculus

This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer).

4 MATH-233

General Education – Elective: Linear Systems and Differential Equations

This is an introductory course in linear algebra and ordinary differential equations in which a scientific computing package is used to clarify mathematical concepts, visualize problems, and work with large systems. The course covers matrix algebra, the basic notions and techniques of ordinary differential equations with constant coefficients, and the physical situation in which they arise. (Prerequisites: MATH-172 or MATH-182 or MATH-182A and students in CHEM-BS or CHEM-BS/MS or ISEE-BS programs.) Lecture 4 (Spring).

4 MATH-251

General Education – Elective: Probability and Statistics

This course introduces demo spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3 (Fall, Spring, Summer).

3 MECE-200

Fundamentals of Mechanics

Statics: equilibrium, the principle of transmissibility of forces, couples, centroids, trusses and friction. Introduction to strength of materials: axial stresses and strains, statically indeterminate problems, torsion and bending. Dynamics: dynamics of particles and rigid bodies with an introduction to kinematics and kinetics of particles and rigid bodies, work, energy, impulse momentum and mechanical vibrations. Emphasis is on problem solving. For students majoring in industrial and systems engineering. (Prerequisites: PHYS-211 or PHYS-211A or 1017-312 or 1017-312T or 1017-389 or PHYS-206 and PHYS-207 or equivalent course.and restricted to students in ISEE-BS or ISEEDU-BS programs.) Lecture 4 (Spring).

4 PHYS-212

General Education – Natural Science Inquiry Perspective: University Physics II

This course is a continuation of PHYS-211, University Physics I. Topics include electrostatics, Gauss' law, electric field and potential, capacitance, resistance, DC circuits, magnetic field, Ampere's law, inductance, and geometrical and physical optics. The course is taught in a lecture/workshop format that integrates the material traditionally found in separate lecture and laboratory courses. (Prerequisites: (PHYS-211 or PHYS-211A or PHYS-206 or PHYS-216) or (MECE-102, MECE-103 and MECE-205) and (MATH-182 or MATH-172 or MATH-182A) or equivalent courses. Grades of C- or better are required in all prerequisite courses.) Lec/Lab 6 (Fall, Spring).

4  

General Education – Global Perspective

3  

General Education – Social Perspective

3 Third Year ISEE-301

Operations Research

An introduction to optimization through mathematical programming and stochastic modeling techniques. Course Topics include linear programming, transportation and assignment algorithms, Markov Chain queuing and their application on problems in manufacturing, health care, financial systems, supply chain, and other engineering disciplines. Special attention is placed on sensitivity analysis and the need of optimization in decision-making. The course is delivered through lectures and a weekly laboratory where students learn to use state-of-the-art software packages for modeling large discrete optimization problems. (Prerequisites: MATH-233 or (MATH-231 and MATH-241) or equivalent course.) Lab 2 (Spring).

4 ISEE-304

Fundamentals of Materials Science

This course provides the student with an overview of structure, properties, and processing of metals, polymers, ceramics and composites. There is a particular emphasis on understanding of materials and the relative impact on manufacturing optimization throughput and quality as it relates to Industrial Engineering. This course is delivered through lectures and a weekly laboratory. (This course is restricted to ISEE-BS Major students.) Lab 2 (Spring).

3 ISEE-323

Systems and Facilities Planning

A basic course in quantitative models on layout, material handling, and warehousing. Topics include product/process analysis, flow of materials, material handling systems, warehousing and layout design. A computer-aided layout design package is used. (Corequisites: ISEE-301 or equivalent course.) Lab 2 (Spring).

3 ISEE-330

Ergonomics and Human Factors (WI-PR)

This course covers the physical and cognitive aspects of human performance to enable students to design work places, procedures, products and processes that are consistent with human capabilities and limitations. Principles of physical work and human anthropometry are studied to enable the student to systematically design work places, processes, and systems that are consistent with human capabilities and limitations. In addition, the human information processing capabilities are studied, which includes the human sensory, memory, attention and cognitive processes; display and control design principles; as well as human computer interface design. (Co-requisites: ISEE-325 or STAT-257 or MATH-252 or equivalent course.) Lecture 4 (Spring).

4 ISEE-350

Engineering Management

Development of the fundamental engineering management principles of industrial enterprise, including an introduction to project management. Emphasis is on project management and the development of the project management plan. At least one term of previous co-op experience is required. (Prerequisite: BIME-499 or MECE-499 or ISEE-499 or CHME-499 or EEEE-499 or CMPE-499 or MCEE-499 or equivalent course.) Lecture 3 (Spring).

3 ISEE-499

Co-op (fall, summer)

One semester of paid work experience in industrial engineering. (Prerequisites: ISEE-120 and EGEN-99 and students in the ISEE-BS program.) CO OP (Fall, Spring, Summer).

0 Fourth Year ISEE-420

Production Planning/Scheduling

A first course in mathematical modeling of production-inventory systems. Topics included: Inventory: Deterministic Models, Inventory: Stochastic Models, Push v. Pull Production Control Systems, Factory Physics, and Operations Scheduling. Modern aspects such as lean manufacturing are included in the context of the course. (Prerequisites: ISEE-301 and (STAT-251 or MATH-251) or equivalent course.) Lecture 3 (Fall).

3 ISEE-499

Co-op (summer)

One semester of paid work experience in industrial engineering. (Prerequisites: ISEE-120 and EGEN-99 and students in the ISEE-BS program.) CO OP (Fall, Spring, Summer).

0 ISEE-510

Systems Simulation

Computer-based simulation of dynamic and stochastic systems. Simulation modeling and analysis methods are the focus of this course. A high-level simulation language such as Simio, ARENA, etc., will be used to model systems and examine system performance. Model validation, design of simulation experiments, and random number generation will be introduced. (Prerequisites: ISEE-200 and ISEE-301 or equivalent course. Co-requisites: ISEE-325 or STAT-257 or MATH-252 or equivalent course.) Lecture 3 (Fall, Spring).

3 ISEE-560

Applied Statistical Quality Control

An applied approach to statistical quality control utilizing theoretical tools acquired in other math and statistics courses. Heavy emphasis on understanding and applying statistical analysis methods in real-world quality control situations in engineering. Topics include process capability analysis, acceptance sampling, hypothesis testing and control charts. Contemporary Topics such as six-sigma are included within the context of the course. (Prerequisites: ISEE-325 or STAT-257 or MATH-252 or equivalent course and students in ISEE-BS or ISEE-MN or ENGMGT-MN programs.) Lecture 3 (Fall).

3 ISEE-760

Design of Experiments

This course presents an in-depth study of the primary concepts of experimental design. Its applied approach uses theoretical tools acquired in other mathematics and statistics courses. Emphasis is placed on the role of replication and randomization in experimentation. Numerous designs and design strategies are reviewed and implications on data analysis are discussed. Topics include: consideration of type 1 and type 2 errors in experimentation, demo size determination, completely randomized designs, randomized complete block designs, blocking and confounding in experiments, Latin square and Graeco Latin square designs, general factorial designs, the 2k factorial design system, the 3k factorial design system, fractional factorial designs, Taguchi experimentation. (Prerequisites: ISEE-325 or STAT-252 or MATH-252 or equivalent course or students in ISEE-MS, ISEE-ME, SUSTAIN-MS, SUSTAIN-ME or ENGMGT-ME programs.) Lecture 3 (Spring).

3  

Professional Electives

6  

Open Electives

9  

Professional Elective/Engineering Management Elective

3  

General Education – Immersion 1, 2

6 Fifth Year ACCT-794

Cost Management in Technical Organizations

A first course in accounting for students in technical disciplines. Topics include the distinction between external and internal accounting, cost behavior, product costing, profitability analysis, performance evaluation, capital budgeting, and transfer pricing. Emphasis is on issues encountered in technology intensive manufacturing organizations. *Note: This course is not intended for Saunders College of Business students. (Enrollment in this course requires permission from the department offering the course.) Lecture 3 (Spring).

3 ISEE-497

Multidisciplinary Senior Design I

This is the first in a two-course sequence oriented to the solution of real world engineering design problems. This is a capstone learning experience that integrates engineering theory, principles, and processes within a collaborative environment. Multidisciplinary student teams follow a systems engineering design process, which includes assessing customer needs, developing engineering specifications, generating and evaluating concepts, choosing an approach, developing the details of the design, and implementing the design to the extent feasible, for example by building and testing a prototype or implementing a chosen set of improvements to a process. This first course focuses primarily on defining the problem and developing the design, but may include elements of build/ implementation. The second course may include elements of design, but focuses on build/implementation and communicating information about the final design. (Prerequisites: ISEE-323 and ISEE-330 or equivalent course. Co-requisites: ISEE-350 and ISEE-420 and ISEE-510 and ISEE-560 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

3 ISEE-498

Multidisciplinary Senior Design II

This is the second in a two-course sequence oriented to the solution of real world engineering design problems. This is a capstone learning experience that integrates engineering theory, principles, and processes within a collaborative environment. Multidisciplinary student teams follow a systems engineering design process, which includes assessing customer needs, developing engineering specifications, generating and evaluating concepts, choosing an approach, developing the details of the design, and implementing the design to the extent feasible, for example by building and testing a prototype or implementing a chosen set of improvements to a process. The first course focuses primarily on defining the problem and developing the design, but may include elements of build/ implementation. This second course may include elements of design, but focuses on build/implementation and communicating information about the final design. (Prerequisites: ISEE-497 or equivalent course.) Lecture 3 (Fall, Spring).

3 ISEE-561

Linear Regression Analysis

In any system where parameters of interest change, it may be of interest to examine the effects that some variables exert (or appear to exert) on others. "Regression analysis" actually describes a variety of data analysis techniques that can be used to describe the interrelationships among such variables. In this course we will examine in detail the use of one popular analytic technique: least squares linear regression. Cases illustrating the use of regression techniques in engineering applications will be developed and analyzed throughout the course. (Prerequisites: (MATH-233 or (MATH-231 and MATH-241)) and (ISEE-325 or STAT-257 or MATH-252) or equivalent courses and students in ISEE-BS programs.) Lecture 3 (Fall).

3 ISEE-750

Systems and Project Management

This course ensures progress toward objectives, proper deployment and conservation of human and financial resources, and achievement of cost and schedule targets. The focus of the course is on the utilization of a diverse set of project management methods and tools. Topics include strategic project management, project and organization learning, chartering, adaptive project management methodologies, structuring of performance measures and metrics, technical teams and project management, risk management, and process control. Course delivery consists of lectures, speakers, case studies, and experience sharing, and reinforces collaborative project-based learning and continuous improvement. (Prerequisites: ISEE-350 or equivalent course or students in ISEE BS/MS, ISEE BS/ME, ISEE-MS, SUSTAIN-MS, ENGMGT-ME, PRODDEV-MS, MFLEAD-MS, or MIE-PHD programs.) Lecture 3 (Fall).

3 ISEE-771

Engineering of Systems I

The engineering of a system is focused on the identification of value and the value chain, requirements management and engineering, understanding the limitations of current systems, the development of the overall concept, and continually improving the robustness of the defined solution. EOS I & II is a 2-semester course sequence focused on the creation of systems that generate value for both the customer and the enterprise. Through systematic analysis and synthesis methods, novel solutions to problems are proposed and selected. This first course in the sequence focuses on the definition of the system requirements by systematic analysis of the existing problems, issues and solutions, to create an improved vision for a new system. Based on this new vision, new high-level solutions will be identified and selected for (hypothetical) further development. The focus is to learn systems engineering through a focus on an real artifact (This course is restricted to students in the ISEE BS/MS, ISEE BS/ME, ISEE-MS, SUSTAIN-MS, PRODDEV-MS, MFLEAD-MS, ENGMGT-ME, or MIE-PHD programs or those with 5th year standing in ISEE-BS or ISEEDU-BS.) Lecture 3 (Fall, Spring).

3 Choose one of the following:

3

   ISEE-792

   Engineering Capstone

Students must investigate a discipline-related syllabu in a field related to industrial and systems engineering, engineering management, sustainable engineering, product development, or manufacturing leadership. The general intent of the engineering capstone is to demonstrate the students' knowledge of the integrative aspects of a particular area. The capstone should draw upon skills and knowledge acquired in the program. (This course is restricted to students in ISEE-MS, ENGMGT-ME, SUSTAIN-MS, PRODDEV-MS, MFLEAD-MS or the ISEE BS/MS programs.) Lecture 3 (Fall, Spring).

     ISEE-794

   Leadership Capstone plus 1 additional Engineering Elective

For students enrolled in the BS/ME dual degree program. Student must either: 1) serve as a team leader for the multidisciplinary senior design project, where they must apply leadership, project management, and system engineering skills to the solution of unstructured, open-ended, multi-disciplinary real-world engineering problems, or 2) demonstrate leadership through the investigation of a discipline-related topic. (Enrollment in this course requires permission from the department offering the course.) Seminar (Fall, Spring).

   

Engineering Management Electives

6  

General Education – Immersion 3

3 Total Semester Credit Hours

150

Sat, 12 Feb 2022 20:55:00 -0600 en text/html https://www.rit.edu/study/industrial-engineering-bs
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