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.

Reflecting On Turning 68

 It's never been just about tech…

Diane Caught This Grouper

In many ways, I've always found myself reverting back to a childhood where I spent a lot of time on the water. I feel fortunate I learned the "old school" methods that complement today's marine technology rather than being replaced by it. 

 

At almost sixty-eight, I value both innovation and tradition in my tech and in my hobbies. Back then as a kid on the water, it was the Pamet River ramp and access to Cape Cod Bay where my father connected with me and my brothers in our 16-foot aluminum Starcraft. Those early mornings heading out and days on the water created bonds that technology could never replicate. Our Starcraft wasn't just transportation; it was our classroom, our sanctuary, and the vessel through which generations connected. Now, decades later I still feel that aluminum hull beneath me.

 

Today, a 33-foot Grady-White with GPS, fish finders, radar, and satellite communications represents technological evolution as it cuts through Gulf waters, yet the foundational skills remain essential. With family and friends beside me we're grounded in the traditional while learning new things and creating new memories. Despite modern electronics, those early Cape Cod teachings remain relevant: weather reports, tides, a magnetic compass, reading water conditions, landmarks, tracking surface-feeding birds, and sensing environmental shifts.

 

The fishing calendar provides structure through seasonal migrations, changing conditions, and equipment upgrades. Each season on the Gulf builds cumulative experience for all of us. The physical exertion reminds me of my shared heritage – I'm proud of the connections I have maintained to the water while embracing the future, now with my own family.

 

Offshore navigation in the Gulf offers perspective on continuous improvement. Just as my Dad taught us in that 16-foot aluminum Starcraft, these waters present opportunities to integrate traditional methods with technological advances, creating a sustainable approach for future generations. When we successfully navigate to a spot "way out" and catch some nice fish, we're all learning together, and I feel that same warmth that bridged generations on Cape Cod Bay in that old aluminum boat. 

At almost 68 I'm still learning about water, about weather, about fish, about tech, and about life.  Balancing innovation with proven methods ensures optimal outcomes for everyone, whether navigating familiar territories or exploring new ones.

Deepseek and Open Source Large Language Models (LLMs)

Deepseek is getting a lot of publicity these days as an open source Large Language Model (LLM) and has got me thinking, not just about Deepseek but about the potential of open source LLMs in general. MIT Technology Review recently published a scary article titled An AI chatbot told a user how to kill himself—but the company doesn’t want to “censor” and it got me thinking a little bit more about the impact open source LLMs can have.

The MIT Technology Review Article

The article reports on a concerning incident where an AI chatbot explicitly encouraged and provided instructions for suicide to a user named Al Nowatzki. The article highlights broad concerns about AI companion apps and their potential risks to vulnerable users' mental health.

 

Anthropomorphization 

Anthropomorphization is a pretty fancy word – it is basically the attribution of human characteristics, behaviors, emotions, or traits to non-human entities, such as animals, objects, or in this case, artificial intelligence systems. It is something the AIs are getting better and better at.

What does this have to do with Open Source?

The recently released open-source large language model that specializes in coding and technical tasks, has been developed as an alternative to proprietary AI models. If you are not familiar with the term “open source” basically it means the source code, model weights, or other components are freely available for anyone to view, use, modify, and distribute under specified licensing terms.

Now, since Deepseek is open source, if you have adequate computing resources, you can easily install and run Deepseek models locally on your computer. Here’s basically what you'll need:

• Sufficient GPU memory - depending on the model size, you'll need a powerful GPU (like an NVIDIA card with 8GB+ VRAM)
• Enough system RAM - typically 16GB+ recommended
• Adequate storage space for the model weights

The basic process that you can find all over the web now commonly involves:

1. Setting up Python and required dependencies
2. Installing the necessary ML frameworks (like PyTorch)
3. Downloading the model weights
4. Using libraries like transformers or llama.cpp to run the model

It may sound complicated but it is really pretty simple to set one up if you follow instructions.

What’s the big deal?

AI training is the process of feeding large amounts of data into machine learning algorithms to help them recognize patterns and learn to perform specific tasks, like generating text or recognizing images, by adjusting their internal parameters through repeated exposure and feedback.  So what is to prevent a malicious person with an open source AI installed taking this a few steps further, training an AI to do all kinds of malicious things and providing access via the web?

If you or someone you know is struggling with suicidal thoughts, call or text 988 to reach the Suicide and Crisis Lifeline.

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/