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Should I focus on Embedded Systems or Data science/analytics?
I'm studying computer engineering in my 2nd year, and in the summer I'll be applying for a co-op. I've done my research and it appears that embedded systems has a steeper learning curve and barrier to entry than let's say data analytics. Both are not easy, but getting an internship as a data analyst looks more approachable. Both are also interesting to me, although embedded looks more hands on and engaging. Thoughts?
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7 answers
Dan Wolf
Retired Electrical/Software Engineer and part-time College Professor (BSEET and MS Engineering Management)
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Dan’s Answer
You're absolutely right that embedded systems offer a more hands-on experience, as they involve controlling and monitoring tangible products. On the other hand, data analytics revolves around data, which isn't as tangible.
When it comes to which is more engaging - embedded systems or data analytics - it really boils down to your personal interests. Any subject can be captivating if it piques your interest. The more knowledge you gain in a subject, the more engaging it becomes.
The learning curve of a subject also hinges on your interest level. If you're well-prepared for a subject, you'll find it easier to master. For instance, if you struggle with math and suddenly start taking calculus and physics classes, you'll face a steeper learning curve than someone who excelled in advanced math and physics in high school. The key is to devote more time to studying or take preparatory classes to make the learning curve less steep.
As for internships, it largely depends on the industries in your vicinity, where you study, or how far you're willing to travel/relocate. Both embedded systems and data analytics are rapidly growing fields, so there's a high chance of securing an internship. Remember, your university can offer guidance and ideas, and you can also glean industry knowledge from fellow students and professors. Don't hesitate to strike up conversations with them!
During my career, I found working with embedded systems to be highly engaging, dynamic, and enjoyable. I've worked on diverse projects such as strip chart recorders, weather systems, steel and aluminum manufacturing, pay telephones, freight locomotives, and mass transit vehicles.
If you're looking to make your university learning curve less steep and boost your internship prospects, consider these platforms:
For Embedded Systems, the Arduino Uno microcontroller is an excellent choice. It's used to control devices like sensors, motors, and lights. It's also affordable, with prices ranging from $3 on Aliexpress to $27 at the official Arduino store in the USA. You can buy a small kit of supporting parts for about $10 to $25. Simply Google "Arduino Projects" to access a wealth of ideas, projects, and free online help. There are also different versions of the Arduino Uno, such as the Arduino Mega, Arduino Nano, and the latest Arduino Uno Rev 4.
For Computer Science or Data Analytics, the Raspberry PI family of computer boards is a great option. More akin to a computer than a microcontroller, the Raspberry PI Model 4 and Model 3 are priced between $35 and $80, depending on the memory. The Raspberry PI Zero 2 W, priced at $15, can perform almost everything the Raspberry PI Model 3 can, albeit less powerfully. In addition to the board, you'll also need a power supply, cables, monitor, keyboard, and mouse, depending on your project. Like with Arduino, Google "Raspberry Pi projects" to find a plethora of ideas and free help.
I recommend starting with one (or both) of these boards. The concepts and skills you'll learn will be beneficial in your university classes and future career. Plus, they're a lot of fun!
When it comes to which is more engaging - embedded systems or data analytics - it really boils down to your personal interests. Any subject can be captivating if it piques your interest. The more knowledge you gain in a subject, the more engaging it becomes.
The learning curve of a subject also hinges on your interest level. If you're well-prepared for a subject, you'll find it easier to master. For instance, if you struggle with math and suddenly start taking calculus and physics classes, you'll face a steeper learning curve than someone who excelled in advanced math and physics in high school. The key is to devote more time to studying or take preparatory classes to make the learning curve less steep.
As for internships, it largely depends on the industries in your vicinity, where you study, or how far you're willing to travel/relocate. Both embedded systems and data analytics are rapidly growing fields, so there's a high chance of securing an internship. Remember, your university can offer guidance and ideas, and you can also glean industry knowledge from fellow students and professors. Don't hesitate to strike up conversations with them!
During my career, I found working with embedded systems to be highly engaging, dynamic, and enjoyable. I've worked on diverse projects such as strip chart recorders, weather systems, steel and aluminum manufacturing, pay telephones, freight locomotives, and mass transit vehicles.
If you're looking to make your university learning curve less steep and boost your internship prospects, consider these platforms:
For Embedded Systems, the Arduino Uno microcontroller is an excellent choice. It's used to control devices like sensors, motors, and lights. It's also affordable, with prices ranging from $3 on Aliexpress to $27 at the official Arduino store in the USA. You can buy a small kit of supporting parts for about $10 to $25. Simply Google "Arduino Projects" to access a wealth of ideas, projects, and free online help. There are also different versions of the Arduino Uno, such as the Arduino Mega, Arduino Nano, and the latest Arduino Uno Rev 4.
For Computer Science or Data Analytics, the Raspberry PI family of computer boards is a great option. More akin to a computer than a microcontroller, the Raspberry PI Model 4 and Model 3 are priced between $35 and $80, depending on the memory. The Raspberry PI Zero 2 W, priced at $15, can perform almost everything the Raspberry PI Model 3 can, albeit less powerfully. In addition to the board, you'll also need a power supply, cables, monitor, keyboard, and mouse, depending on your project. Like with Arduino, Google "Raspberry Pi projects" to find a plethora of ideas and free help.
I recommend starting with one (or both) of these boards. The concepts and skills you'll learn will be beneficial in your university classes and future career. Plus, they're a lot of fun!
Updated
Jennifer’s Answer
I would say you should pursue what interests you most. Getting hands on experience is invaluable. I can't imagine that embedded systems would hamper any chances in pursuing data analytics in the future. So often, we continue to grow in different ways than we ever expected. I have an accounting degree with a minor in accounting information systems. I now am in system support from the business side and don't do anything regarding accounting.
I would keep my options open, but take the opportunity that sounds the most interesting and engaging. Work can be long days and repetitive. Caring about what you're doing daily is important.
Best of luck to you.
Jen
I would keep my options open, but take the opportunity that sounds the most interesting and engaging. Work can be long days and repetitive. Caring about what you're doing daily is important.
Best of luck to you.
Jen
Thanks Jennifer, I appreciate the advice. Since the barrier to entry for a data analyst is easier, should I try getting an internship in data analytics for experience first? I have an end goal in mind but at the moment if I prioritize embedded I have a feeling that the supply would not be in my favour (this summer at least).
Aun
I wouldn't necessarily limit myself to it, but would be open to internships in either area. It's more work, but you would still be able to get your main goal if you try for both.
Jennifer Schoenbein
Thank you so much, appreciate it! My mind is set, time to get to work :)
Aun
Updated
Jerome’s Answer
It depends on your interests; both are solid choices. If you're more low-level hardware oriented and like programming in languages like C, C++, Rust, and/or Assembly, then embedded systems is the way to go. You'll get to play with sensors, real-time applications, and that sort of thing; the learning curve is steep but you won't be bored. On the other hand, if you like manipulating and understanding data, doing mathematical analysis to find out what information really means and making it useful to others, then data analytics is a very rewarding career; you'll need to have a good knowledge of SQL and analytical programs.
Good luck with your choice!
Good luck with your choice!
Thank you for your answer, Jerome. So you suggest working on one skill? I was thinking on focusing on what's easier in the moment (for internship opportunities) and then showing to employers who I want to work for that I have some sort of experience with some sort of transferable skills. In this case focusing on data analytics and then switching to embedded in the later years
Aun
I think that's a reasonable approach; you may find you like working with analytics and decide to stick with it, or you may decide to switch horses later on. Be aware, though; data analytics isn't necessarily "easier" than working with embedded code -- it depends on the engineer and on the application. Some folks gravitate toward one or the other based on individual preference and ability. Both are fast-growing fields, so you should be in good shape either way.
Jerome Levy
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
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James Constantine’s Answer
Hi Aun,
I hope this message finds you well. I understand you're contemplating whether to focus on embedded systems or data science/analytics. It's a significant decision and I'm here to help you navigate it. Both domains have their unique advantages and challenges, and the choice ultimately rests on your personal interests, career aspirations, and the kind of work environment you see yourself thriving in.
Embedded Systems: This field is all about designing and building computer systems that are part of larger systems or products. It's a domain that demands a robust understanding of hardware and software design, real-time operating systems, and low-level programming languages. Tasks you might handle include programming microcontrollers, integrating sensors, and developing device drivers.
Benefits of choosing embedded systems:
Hands-on work: You'll often work with physical devices and hardware components, adding a tangible, hands-on element to your role.
Broad applications: Embedded systems are used across industries like automotive, aerospace, consumer electronics, and healthcare, offering a variety of career paths.
Growing demand: With the expansion of the Internet of Things (IoT), the need for embedded systems experts is set to rise.
Challenges of choosing embedded systems:
Steep learning curve: Mastering embedded systems demands a deep grasp of both hardware and software, which may be tough for some.
Limited visibility: The work you do in embedded systems may not always be easily understood by those outside the field.
Data Science/Analytics: This field revolves around extracting valuable insights from large data sets using statistical analysis, machine learning, and programming skills. As a data science professional, you'd interpret data to guide business decisions, develop predictive models, and devise data-driven strategies.
Benefits of choosing data science/analytics:
Growing demand: As organizations increasingly turn to their data to make strategic decisions, the demand for data scientists and analysts is on the rise.
Versatility: Data skills are applicable across industries, providing opportunities in sectors like finance, healthcare, e-commerce, and more.
Impactful work: Data analysis can yield actionable insights that foster business growth, streamline processes, and solve intricate problems.
Challenges of choosing data science/analytics:
Fast-paced evolution: The tools and techniques in data science are constantly evolving, requiring ongoing learning.
Intensive programming and mathematical skills: Proficiency in programming languages like Python or R, as well as a firm grasp of statistics and machine learning algorithms, is often necessary.
In the end, let your passion for the subject and your preferred work environment guide your decision. I would recommend exploring internships or co-op opportunities in both fields to get a firsthand experience before making your final choice.
Key reference publications used for this answer:
IEEE Spectrum
Harvard Business Review
Towards Data Science
Take care and best of luck,
James
I hope this message finds you well. I understand you're contemplating whether to focus on embedded systems or data science/analytics. It's a significant decision and I'm here to help you navigate it. Both domains have their unique advantages and challenges, and the choice ultimately rests on your personal interests, career aspirations, and the kind of work environment you see yourself thriving in.
Embedded Systems: This field is all about designing and building computer systems that are part of larger systems or products. It's a domain that demands a robust understanding of hardware and software design, real-time operating systems, and low-level programming languages. Tasks you might handle include programming microcontrollers, integrating sensors, and developing device drivers.
Benefits of choosing embedded systems:
Hands-on work: You'll often work with physical devices and hardware components, adding a tangible, hands-on element to your role.
Broad applications: Embedded systems are used across industries like automotive, aerospace, consumer electronics, and healthcare, offering a variety of career paths.
Growing demand: With the expansion of the Internet of Things (IoT), the need for embedded systems experts is set to rise.
Challenges of choosing embedded systems:
Steep learning curve: Mastering embedded systems demands a deep grasp of both hardware and software, which may be tough for some.
Limited visibility: The work you do in embedded systems may not always be easily understood by those outside the field.
Data Science/Analytics: This field revolves around extracting valuable insights from large data sets using statistical analysis, machine learning, and programming skills. As a data science professional, you'd interpret data to guide business decisions, develop predictive models, and devise data-driven strategies.
Benefits of choosing data science/analytics:
Growing demand: As organizations increasingly turn to their data to make strategic decisions, the demand for data scientists and analysts is on the rise.
Versatility: Data skills are applicable across industries, providing opportunities in sectors like finance, healthcare, e-commerce, and more.
Impactful work: Data analysis can yield actionable insights that foster business growth, streamline processes, and solve intricate problems.
Challenges of choosing data science/analytics:
Fast-paced evolution: The tools and techniques in data science are constantly evolving, requiring ongoing learning.
Intensive programming and mathematical skills: Proficiency in programming languages like Python or R, as well as a firm grasp of statistics and machine learning algorithms, is often necessary.
In the end, let your passion for the subject and your preferred work environment guide your decision. I would recommend exploring internships or co-op opportunities in both fields to get a firsthand experience before making your final choice.
Key reference publications used for this answer:
IEEE Spectrum
Harvard Business Review
Towards Data Science
Take care and best of luck,
James
Updated
Mir’s Answer
While my thoughts may echo those of others on this topic, I wholeheartedly encourage you to follow the path that truly sparks your interest. Embedded engineering is indeed fascinating, and as someone who has built numerous robots and wireless networks, I can attest to its allure. However, it's essential to consider your career opportunities. The reality is, embedded engineering jobs are primarily found in a handful of hardware-producing companies.
On the other hand, data analytics enjoys a wider demand, even within hardware companies. This means you're more likely to find a role that allows you to shine in both data analytics and embedded engineering. For instance, IoT companies like Fitbit and Honeywell could offer you the chance to hone your skills in both areas. So, keep exploring, keep learning, and remember that the right opportunity is out there, waiting for you to seize it!
On the other hand, data analytics enjoys a wider demand, even within hardware companies. This means you're more likely to find a role that allows you to shine in both data analytics and embedded engineering. For instance, IoT companies like Fitbit and Honeywell could offer you the chance to hone your skills in both areas. So, keep exploring, keep learning, and remember that the right opportunity is out there, waiting for you to seize it!
Updated
Jeff’s Answer
Speaking from an analytics/data science perspective, these fields are great if you enjoy dealing with data and discovering trends within the data. Often in business contexts, jobs in the analytics/data science field will be tasked with handling large amounts of data and allow you to work with tools such as Excel, Tableau and Python. These tools will allow you to visualize the data, which is another key aspect of these roles. If you enjoy data visualization and building a story to explain what you find in the data trends, then analytics/data science roles are a great career path. Data science also allows you to be in the machine learning and artificial intelligence focus areas. These studies and applications in the machine learning and artificial intelligence focus areas are really popular right now and I expect these areas to continue to be popular and important in business. Good luck!
Updated
Wayne’s Answer
Follow the path that sparks your interest, but don't shy away from challenges just because they seem tough. Often, people underestimate the value of tackling difficult subjects like the sciences. They may seem daunting, but remember, when others quit, your perseverance stands out. This can give you a competitive edge in the marketplace. So, embrace the hard stuff and let it set you apart!