Why do Google, Facebook, Amazon, etc Call Computer Science Degree Holders Engineers?

It can get a little confusing, especially for students selecting a college major, applying for internships, and jobs. Here's my take on this question:

Software engineering is the disciplined approach to designing, building, and maintaining software systems through systematic methodologies, architectural planning, and rigorous testing practices. The engineering designation reflects the application of computer science principles to practical problem-solving under real-world constraints. Unlike pure computer science, which centers on theoretical foundations and algorithm analysis, software engineering encompasses:

 

·      Systems architecture - Designing complex, distributed systems with considerations for scalability, reliability, and performance metrics (latency, throughput, etc.)

·      Technical debt management - Making strategic implementation decisions that balance short-term delivery with long-term maintainability

·      Production engineering - Implementing fault-tolerant systems with proper error handling, monitoring, logging, and recovery mechanisms

·      Resource optimization - Operating within hardware constraints (CPU, memory, network, storage) while meeting Service Level Agreements (SLAs)

·      Engineering tradeoffs - Making calculated decisions between competing factors like development time, operational complexity, and performance characteristics

·      Design patterns implementation - Applying established patterns to solve recurring challenges

·      Cross-functional integration - Working at abstraction boundaries between different system components, often requiring knowledge of multiple domains

 

The title reflects the application of engineering methodologies in code development rather than simply implementing algorithms. This includes requirements analysis, system design documentation, testing strategies, deployment planning, and operational considerations—all core engineering disciplines applied to the software development world. 

 

And yeah – in some ways the title "engineer" is used by tech companies like Google, Facebook, and Amazon for strategic reasons when hiring computer science graduates. Think of it like how some coffee shops call their workers "baristas" instead of "coffee makers" - it's partly about perception and partly about reflecting that the job involves skill and craft.

Resources for Computer Engineering & Computer Science Career Paths

It's that time of year when students are making important education decisions. Last night, I received an email from a reader whose daughter is graduating high school and considering college majors in either Computer Science or Computer Engineering. They asked about career prospects and future trends in both fields.

I'll be addressing this topic in more detail in the coming days, but for now, I'd like to share some valuable resources that provide insights into various career paths in these fields:

Government & Official Statistics

• U.S. Bureau of Labor Statistics (BLS) - Occupational Outlook Handbook
• Projects 15% growth for software developers (2022-2032)
• 23% growth for information security analysts
• 11% growth for computer hardware engineers
• National Science Foundation (NSF) - Science & Engineering Indicators
• Tracks STEM workforce trends and emerging fields

Industry Reports

• World Economic Forum - "Future of Jobs Report 2023"
• Identifies AI specialists, robotics engineers, and cybersecurity experts among fastest-growing roles
• Gartner Research - "Top Strategic Technology Trends" (annual reports)
• Highlights growth in AI engineering, hyperautomation, and cybersecurity mesh
• IEEE Computer Society - "Technology Predictions" (annual report)
• Projects growth in quantum computing, AI hardware acceleration, and sustainable computing

Professional Organizations

• Association for Computing Machinery (ACM) - Job market analyses https://www.acm.org/ 
• Publishes regular assessments of computing job market trends
• IEEE Computer Society - Workforce studies https://www.computer.org/ 
• Tracks emerging specializations in computer engineering

Academic & Research

• Computing Research Association (CRA) - "Taulbee Survey"
• Annual study of computing degree production and employment outcomes
• Stanford University AI Index - Annual report on AI industry growth https://hai.stanford.edu/ai-index 
• Documents increasing demand for AI specialists across industries

Other Sources

In addition, major tech companies like Google, Microsoft, and Amazon often publish career guides and internship opportunities and college-specific career centers typically have placement data for their graduates in these fields.

Stay Updated

For the most current and detailed projections, consult these sources directly as they regularly update their forecasts and analyses.

The Differences Between Computer Science and Computer Engineering

As a Computer and Electrical Engineering professor, I get asked a lot about the differences between a Computer Science and a Computer Engineering degree. So.... how different are they? It turns out the two are closely related but have distinct focuses and career paths. Here’s a simple breakdown: 

Computer Engineering:

·       Focuses on hardware design and integration with software

·       Includes electrical engineering fundamentals

·       Covers computer architecture, digital logic, circuit design

·       Involves embedded systems and hardware-software interfaces

·       Often includes courses on microprocessors, VLSI (Very Large Scale Integration) design, and signal processing

·       Graduates typically work on designing hardware components, embedded systems, IoT (Internet of Things) devices, or robotics

 

Computer Science:

·       Focuses on theoretical foundations and software development

·       Emphasizes algorithms, data structures, and computational theory

·       Covers programming languages, software engineering, and systems design

·       Includes database systems, artificial intelligence, and operating systems

·       Often features more abstract mathematical concepts

·       Graduates typically work as software developers, data scientists, or system architects

 

The two curriculums do overlap with both degrees including programming fundamentals, discrete mathematics, and computer organization courses, but with different emphases and depths.

 

When it comes to careers, there's significant overlap in job opportunities, with many roles accessible to graduates of either program. Computer engineers typically have an edge in hardware-focused roles, while computer science graduates typically have advantages in pure software positions.

A Chance at Excellence: John Dunn's Legacy

John was someone who took a chance on me 24 years ago. He was a mentor and a friend. So much of the good stuff in Western Mass and at community colleges across the country are the direct result of John's commitment, talent and hard work. 

I stopped by an old haunt a couple weeks ago and had to grab a pic. Twenty-four years ago, John Dunn, the Academic Vice President at Springfield Community College (STCC) at the time, made a decision that would transform my career and the future direction of the college. When Jim Masi retired as director of the NSF funded National Center for Telecommunications Technologies National Center of Excellence, most administrators might have conducted an extensive search for a seasoned replacement. John saw something different.

With quiet confidence that belied his position as Academic VP, John, along with President Andrew Scibelli, took a chance on me to continue the important work Jim had begun. While I had big shoes to fill following Dr Masi's accomplished tenure, John trusted my vision for taking the center forward. "Great work evolves through fresh perspectives," he told me the day STCC officially appointed me as the new director.

John's leadership style combined academic rigor with uncommon accessibility. He navigated the complex waters of NSF funding requirements with the same ease he showed chatting with nervous first-year faculty during lunch at The Blue Eagle restaurant around the corner from STCC. His door was always open, but his standards never wavered.

What made John exceptional wasn't just his willingness to take calculated risks on promising talent, but his genuine belief in the potential of community colleges to contribute meaningfully to society. In a system often overshadowed by larger universities, John advocated tirelessly for our place at the table.

Under John's supportive guidance, we built upon Jim Masi's foundation to take the center in new directions. That center became a launching pad for countless students and faculty across the country, many from backgrounds traditionally underrepresented in the sciences. John never sought credit, but his fingerprints remain on every breakthrough we achieved.

Twenty-four years later, John Dunn's legacy continues through the work of everyone who participated and benefited — a testament to what becomes possible when someone believes in your potential to carry important work forward.

John passed away December 4, 2021. I miss him and I think of him often.

Characterizing Student Perspectives on Departmental Culture

The AAAS-IUSE initiative, funded by the NSF Improving Undergraduate STEM Education: Directorate for STEM Education (IUSE: EDU) posted an interesting read last month. If you are not familiar – the AAAS-IUSE Initiative supports faculty, students, and the greater undergraduate STEM education community by disseminating research and knowledge about STEM teaching, learning, equity and institutional transformation.

 

The post that caught my eye discusses how traditional metrics for evaluating student success (like graduation rates) are insufficient because they only measure outcomes at the end of a student's academic journey. 

 

It introduces what the authors are calling the student DELTA survey (s-DELTA) as a new tool to measure departmental culture from students' perspectives, with the goal of improving diversity, equity, inclusion, and accessibility.

Key points:

1. Departmental culture significantly impacts student experience, and departments often have more distinct cultures than institutions as a whole. This makes departments an effective focus for implementing change.
2. The s-DELTA survey is based on six core principles, including student partnership, collective outcomes, data-informed decisions, collaboration, continuous improvement, and commitment to equity.
3. The survey structure asks students to evaluate both their current department and their ideal department using a six-point Likert scale, allowing for comparison between reality and aspirations.
4. Initial findings showed significant differences between current and ideal department ratings, particularly in areas related to equity and inclusion. Gender-based analysis revealed that women and men often had different experiences of departmental culture.

A great start. While the s-DELTA survey needs further psychometric testing, it can provide a valuable tool for:

• Tracking student perspectives over time
• Informing departmental policy development
• Supporting diversity and inclusion efforts
• Helping departments understand and improve their cultural impact on student experience

Worth a look for academics. Here’s a link if you are interesting in reading the original post: https://aaas-iuse.org/characterizing-student-perspectives-on-departmental-culture-the-student-delta-survey/

I'm Retiring From My Full Time Engineering Faculty Position at Holyoke Community College

“Often when you think you’re at the end of something, you’re at the beginning of something
else.” – Fred Rogers

After an enriching and fulfilling career spanning five decades, I am announcing my retirement from my full-time engineering faculty position at Holyoke Community College (HCC) effective August 30, 2024. This decision comes with mixed emotions, as I reflect on the journey that has shaped not only my professional life but also my personal growth.

Teaching has been more than just a job; it has been a vocation that allowed me to inspire and nurture countless students, guiding them through the complexities of engineering principles and practices. My time in academia has been marked by moments of profound satisfaction, seeing students evolve into competent and confident engineers and technologists, ready to make their mark in the world. Each graduation ceremony, each successful project, and each breakthrough in understanding (and of course each belly-flop failure) has reinforced my belief in the power of education.

The field of engineering itself has undergone tremendous transformation during my tenure. From the early days of teaching with chalk and blackboards to leveraging cutting-edge technology and online platforms, I have witnessed and embraced these changes, ensuring that my students received the most current and relevant knowledge. The shift towards digital learning, in particular, has been a remarkable journey, one that demanded continuous adaptation and learning on my part.

Retirement from HCC marks the beginning of a new chapter, where I am eager to channel my energy and expertise into some exciting consultancy projects, writing, mentoring, and of course some part-time teaching. The flexibility will allow me to spend more time with my family and yeah (!) - get in a little more fishing. 

Will this be a “sabbatical”  or a permanent change? Having experienced "retirement" in the past and not having it “stick”, I've learned to appreciate the unpredictability of life. This journey has taught me to embrace uncertainty and always stay open to whatever opportunities and possibilities the future may bring.

My academic career has been a tapestry woven with dedication, passion, and countless rewarding experiences. I carry with me a wealth of memories and the profound satisfaction of having contributed to the field of engineering and to the lives of so many young minds. 

An Exponential Leap: The Emergence of AGI - Machines That Can Think

Tech companies are in a rush. They're trying to lock in as much electricity as they can for the next few years. They're also buying up all the computer components they can find. What's all this for? They're building machines that can think and referring to the tech as Artificial General Intelligence, or AGI.

On June 3 Ex-OpenAI researcher (yeah he was fired) Leopold Aschenbrenner published a 162 page interesting document titled SITUATIONAL AWARENESS The Decade Ahead. In his paper Aschenbrenner describes AGI as not just another incremental tech advance – he views it as a paradigm shift that's rapidly approaching an inflection point.

I’ve read the whole thing - here's my short list of highlights by topic.

Compute Infrastructure Scaling: We've moved beyond petaflop systems. The dialogue has shifted from $10 billion compute clusters to $100 billion, and now to trillion-dollar infrastructures. This exponential growth in computational power is not just impressive—it's necessary for the next phase of AI development.

AGI Timeline Acceleration: Current projections suggest AGI capabilities surpassing human-level cognition in specific domains by 2025-2026. By the decade's end, we're looking at potential superintelligence—systems that outperform humans across all cognitive tasks.

Resource Allocation and Energy Demands: There's an unprecedented scramble for resources. Companies are securing long-term power contracts and procuring voltage transformers at an alarming rate. We're anticipating a surge in American electricity production by tens of percentage points to meet the demand of hundreds of millions of GPUs.

Geopolitical Implications: The race for AGI supremacy has clear national security implications. We're potentially looking at a technological cold war, primarily between the US and China, with AGI as the new nuclear equivalent.

Algorithmic Advancements: While the mainstream still grapples with language models "predicting the next token," the reality is far more complex. We're seeing advancements in multi-modal models, reinforcement learning, and neural architecture search that are pushing us closer to AGI.

Situational Awareness Gap: There's a critical disparity between public perception and the reality known to those at the forefront of AGI development. This information asymmetry could lead to significant societal and economic disruptions if not addressed.

Some Technical Challenges Ahead:

- Scaling laws for compute, data, and model size

- Achieving robust multi-task learning and zero-shot generalization

- Solving the alignment problem to ensure AGI systems remain beneficial

- Developing safe exploration methods for AGI systems

- Creating scalable oversight mechanisms for increasingly capable AI

An over reaction by Aschenbrenner?  Some think so. Regardless - this stuff is not going away and as an educator and technologist, I feel a responsibility to not only teach the tech but also have students consider the ethical and societal implications of this kind of work. The future isn't just coming—it's accelerating towards us at an unprecedented rate. Are we prepared for the AI  technical, ethical, and societal challenges that lie ahead?