The author discusses their day-to-day life as a mechanical engineer in oil and gas, focusing on making coffee, updating project boards, and attending meetings. They discuss the CS grad lifestyle, salary progression, and the importance of a healthy work-life balance. They mention that most CS majors end up in 5-10 years with a decent pay of around 110K.
The author also shares their experience with CS people being empathetic and nice, but emphasizes the need for a balanced approach to life. They mention a 9-month educational nursing program in patient groups with CS, which showed improvements in sleep patterns, pain, and healthy lifestyle.
The author also discusses the importance of starting with the core weapons in CS:GO, such as introducing new skills to the game. They also discuss the potential risks of a caesarean section (CS) and the importance of making lifestyle changes before trying medicines. They also mention a guideline covering identifying and assessing the risk of cardiovascular disease (CVD) in adults without established CVD.
The author also discusses the importance of having a strong work-life balance in high-skill software engineering positions with high demand and low labor supply. They also discuss the importance of programming as a skill that can be practiced more. The author concludes by emphasizing the importance of maintaining a healthy balance in studying Computer Science, as it can provide numerous benefits, including lowered stress levels, increased well-being, and higher job satisfaction.
📹 Data Science Career: (Is Becoming A Data Scientist ACTUALLY Worth It?)
———- These videos are for entertainment purposes only and they are just Shane’s opinion based off of his own life experience …
Is CS good for introverts?
The field of computer science is particularly well-suited to individuals who are introverted, as it emphasizes independent work, structured learning, and specialization. Its distinctive characteristics and opportunities offer a multitude of advantages.
Is CS a stressful job?
Computer Science jobs can be overwhelming, with constant technological advancements, complex problem-solving, and deadline-compliance leading to stress, fatigue, and burnout. Burnout is a state of emotional, physical, and mental exhaustion resulting from chronic work-related stress, affecting health, happiness, and performance. To prevent or cope with burnout, it is essential to remember that everyone is capable in their unique way. Connecting the dots and discussing with others can provide the best solutions. It is crucial to put the burden of the job aside and open up to find solutions.
Is CS becoming oversaturated?
The software engineering field may seem oversaturated for entry-level applicants, but demand for specialized engineering and soft skills is still high, offering ample opportunities for qualified candidates. To maximize success, new developers should specialize in languages like Python and JavaScript, explore in-demand skills like security, AI/ML, and data engineering, gain work experience through internships or open-source projects, build a portfolio with real projects, network through local tech events and conferences, practice soft skills, consider overlooked markets, and continuously expand their skills. By following these tips, aspiring software engineers can maximize their chances of success in the complex field.
What is the best job for a quiet person?
Introverts can find high-paying careers in various fields, such as database architect, software developer, actor, information security analyst, data scientist, computer systems analyst, mechanical engineer, and digital (UX) designer. These careers allow introverts to leverage their natural tendencies and unique strengths, as they don’t require constant meetings or large group work. Resume Genius has curated a list of 15 high-paying jobs with strong job growth projections that are well-suited for introverts.
Is cybersecurity good for introverts?
Cybersecurity requires a diverse range of skills and qualities, including analytical thinking, attention to detail, curiosity, independence, creativity, and problem-solving. Introverts possess these qualities and can thrive in this field. Hiring introverts in a cybersecurity team can enhance focus, depth, and quality of work, increase innovation, diversity, and balance, and improve research, learning, and adaptation.
Hiring introverts is an innovative and strategic decision that can boost performance, efficiency, and effectiveness. They are valuable assets and should be recognized, appreciated, and supported for their contributions.
What career is right for a shy person?
Individuals who are introverted tend to demonstrate superior performance in environments that are relatively quiet, such as workspaces, one-on-one interactions, and independent work roles. They exhibit a preference for quiet, open environments over noisy, open ones and a proclivity for independent work over large group collaborations.
Is CS a stressful career?
Computer Science jobs can be overwhelming, with constant technological advancements, complex problem-solving, and deadline-compliance leading to stress, fatigue, and burnout. Burnout is a state of emotional, physical, and mental exhaustion resulting from chronic work-related stress, affecting health, happiness, and performance. To prevent or cope with burnout, it is essential to remember that everyone is capable in their unique way. Connecting the dots and discussing with others can provide the best solutions. It is crucial to put the burden of the job aside and open up to find solutions.
Does CS have a future?
Computer science professionals are in high demand due to the integration of technology in daily life. Despite recent layoffs, the demand for skilled professionals is expected to continue. A computer science degree opens doors to various career paths, including software development, data analysis, and cybersecurity. Skills developed include problem-solving, critical thinking, programming languages, and analytical abilities, which are highly valued across various industries.
Is CS a good career path?
A computer science degree offers a valuable investment with in-demand skills, diverse career opportunities, and a problem-solving mindset. With 23 possible career outcomes, including driving digital innovation and developing software applications, computer science is a versatile field with many potential career paths. Software developers are the creative brains behind apps and websites, using their technical skills to build software applications. The median salary for a computer science degree is $58, 250 per year, making it a valuable investment in the digital world.
Are people in CS happy?
A survey conducted by CareerExplorer indicates that computer and information research scientists assess their career satisfaction at 3. 3 out of 5 stars, which positions them within the top 42% of careers. This level of satisfaction is then benchmarked against that of other careers within the same industry. The concept of career happiness can be defined in a number of ways, including in terms of career satisfaction, career growth, and personal fulfillment.
Are CS engineers happy?
Computer science engineering offers numerous career opportunities in various industries and business sectors, with students completing a degree in this field having access to numerous job opportunities in the technology sector. Job satisfaction is high among computer engineers, who face numerous challenges and continuously learn new skills. The design of new software products and problem-solving are the top reasons for their happiness. Additionally, they receive good pay for their work.
Meanwhile, computer engineers believe that their work is valuable and will have a positive impact in the future. Technology makes life easier, and engineers spend considerable time working, ensuring they will make a lasting difference in the world. They also enjoy different work on many days, as the field is constantly developing, providing opportunities to add knowledge, challenge themselves, and offer new ideas for problems.
Computer science engineering professionals are well-paid due to their expertise in keeping up with ever-changing technologies. With the development of data, technology, and machine learning, computer engineers are in high demand, and obtaining a Computer Science engineering degree can lead to a lucrative salary.
In conclusion, computer science engineering offers numerous career paths and lucrative salaries, making it an attractive option for those interested in this field. With the continuous development of the world, more people are relying on computers for both business and personal use, making it an attractive career path for computer science engineering students.
📹 Why So Many CEOs Are Engineers
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Great article Shane! Completely Nailed it. As a data scientist myself, I was a little surprised at how high the job satisfaction scores were. On the job, a lot of my peers find that they aren’t challenged enough by their positions. This is probably because data science is still relatively new, and companies are still figuring out how to create value with data teams. The other side of that is that once you have a data science role it is pretty easy to land another data science job so qualified data scientists can just leave if they aren’t liking the position at hand. I love seeing the data and trying to map it to what I’m hearing anecdotally!
I’m a data analyst I will say, you need to be good at attention to detail and actually know your skill set One wrong variable can skew your results majorly which can cost time and money I’ve had to bite the bullet before and it hurts wasting hours of your life on something that needs to be redone or straight abandoned
I’m currently a financial analyst and getting my 2nd degree in data analytics. My advice, get job experience before you graduate so that you have a job lined up. Even though my job consists of finance I still have to do data analytics with the financial data. I love it and will have a year in around December. I’m planning to get my masters as well. Get a entry level job way before you graduate. Any experience with money or data is good. Pay might be crap at first but once you get the degree it will be good.
Excellent, data-driven assessment of this field. I am 57 with a BS in math just retired (not really, want to work in data science) and pursuing a masters in statistics. I have one class in machine learning where neither the professor nor students are over thirty. Most of the students are early twenties and some might still be in their teens. I was able to start on Zoom but now that we are back in the class my status is very clear although I keep fit and positive (I never lied about my age – that would be creepy but didn’t need to advertise it either so long as I pulled my weight in study groups). I feel so out of place but just shake it off and try to keep on moving forward as best I can. You mentioned ageism and while I have not attempted to apply for any jobs yet I have experienced ageism and how one’s “value” can diminish at a job. The brutality of it can be very harsh on the receiving end and wonder why people do not realize they will be there sooner than they might care and wonder if they consider the world they are creating. Me, I’ll keep on going until I cannot. I enjoy turning data into knowledge. I am sure your analytics show your main demographic are on the younger end but wonder if you will address ageism in later articles. Either way, keep the data and its interpretation flowing and “Rage, rage against the dying of the light!”
Thanks for the great content Shane. As a data scientist myself, who came into this field via the shortcut route essentially, given that my own engineering field had few jobs available, one thing I have noticed is that it is often more useful to a data scientist and their company if the data scientist comes from the shortcut route. If you come in with the domain knowledge of one engineering discipline or some other discipline, plus the skills you need as a data scientist, you can carve for yourself much more important roles within an organization and offer insights that traditional data science program graduates cannot. While traditional education pathways into data science offers a clear cut set of classes to take, it rarely allows us to draw the connection to real world data science in depth. You end up being able to do what the shortcut data scientists can do, but without the specialized domain knowledge. Coming from an engineering discipline myself, I was able to understand data science problems with product perspective and help deliver projects in novel ways. I have noticed the same with my other peers who also don’t come from the traditional route. I say this because I have met with a lot of non-traditional route students who want to do data science but feel a little scared or worried that they can’t break through. They definitely can break through and thrive in this field. They offer specialized insights you can’t get through the traditional route. So if you are someone who is studying even something in the humanities, like linguistics or english, you can become a powerful data scientist (with perhaps a specialization related to what you studied, such as NLP).
My close friend got his bachelors in Electrical Engineering then went for his Masters in Data Science. He is now making $245k including bonuses at Intuit as a Machine Learning Engineer and he’s only 26 with no experience. Got the interview thanks to his Masters then barely passed the interview by doing over 200 leetcode questions 3x each.
Interesting note on Ageism at the end of the article. I haven’t experienced that at all, but I have definitely seen some “degreeism”. When data science started, it was extremely difficult to evaluate the skillset. Companies used advanced degrees as a simple heuristic to vet talent. Now we have far better methods for evaluating the skillset, but many companies still haven’t updated their hiring methods. This is slowly changing, but it frustrates me that there is this still this belief when it has been proven that someone can be a qualified data scientists without needing advanced formal education
I’m not a data scientist myself, but from what I gathered online data scientist constantly communicate with business leaderes. If so then, I would say that communication skills is also a big requirement of data science (in addition to domain expertise, programming and math/stat) because constantly communicating with business leaders is very different from constantly communicating with other IT colleagues. Many IT people are great at what they do, but a lot of business leaders don’t want to waste their time deciphering an overly technical mind (unless they are very technical themselves). When talking with other departments, communication style can be met in the middle, but there’s no way business leaders want to meet you in the middle in terms of communication style. Instead of hiring another translator, they would want a bilingual fluent in tech and business talk.
Shane, I would highly recommend looking into sub reddit information. I looked into data science and when you go to the data science subreddit you will see how it is nearly impossible to get a job in data science. Usually people will go into something like Business analyst to data analyst to data scientist as a means of going up the scale. Also these subreddits will help you get a better idea of the requirements of different jobs. For instance in IT career questions even though IT may be a fantastic career eventually the vast majority of entry level starts at helpdesk with a salary of $15-$20 an hour and is there for a few years before being able to make something like sys admin.
I feel like the term “Data Science” is generally broad, it could range anywhere from a role that requires you to manually collect, clean and analyse data on your microsoft excel, all the way to a role that creates / automates pipelines, use heavy statistical techniques on multi-dimensional data that may or may not involve multivariable functions and require you to optimise complex problems with a bunch of different constraints, apply machine learning methods, or even conduct some research too. So I wouldn’t agree that a 3 month bootcamp with some work experience can get you anywhere near to the latter position
Great article, Shane! I’m new to the website but love the content you’re putting out! Completely agree with you that it’s normally easier to start with becoming a Data analyst, get some experience + domain knowledge in a certain field, then look for a data scientist job. That’s exactly how I got into data science after my Master’s a few years back. It’s a fantastic job, but I feel like the job satisfaction is overrated sometimes and that could differ per individual, company, and position. Some data scientist jobs are actually more like data analysts or data engineers (lots of data cleaning and pre-processing), which might not be challenging and exciting enough for someone with a lot of expectations.
Hi Shane, the actuary here who has commented on your website frequently. I would just like to point out that actuary is a special case of data scientist. The skill sets are extremely similar, and also, there are many data scientists who are hired by insurance companies. I haven’t finished perusal your article yet but I already know, this is a REALLY good one whether in or out of insurance.
The most important part is first having domain knowledge in the industry where you will aim to work as a data scientist. After that a masters in analytics / DS should be enough to breach in the field. Personally If I would go back in time I would much rather do a masters in either Applied Statistics, or Computer Science – and the better route would be Software Engineer -> AI Engineer / MLOps Engineer Etc.
Also note, switching to this career is not as easy as people paint it. I have a PhD in theoretical physics and have over 200 rejections from data science jobs, many of which were entry-level and mid-senior level. What companies really want is people with corporate experience, people that have done at least some data analytics in a business setting, this is way more important than what kind of education you have (although if you manage to get a Masters or PhD in data science, then you will have a humongous advantage in the job market)
i have a question plz, all i know in math is +-×÷, how much time do you think its gonna get me to understand the math that a data scientist need.(i know its a matter of my learning capabilities but i’d really appreciate it if you give me an approximate amount of time that im gonna need, because i have about 5 free months ) and thanks alot
Hey Shane I am so confused with masters in data analytics or business analytics from Boston University My bachelor’s degree is in mechanical 😭😭😭😭😭😭I don’t want to be a data scientist coz of coding and hard skills r hard But I feel business analytics will b too easy n saturdated n data analytics is flexible 😭😭😭😭😭
Do i need to go to a college or university to get the education or degree needed? Or can i go through a technical/vocational college/school and get certifications? Like comptiaa+, security+ or network+ im 23 and switching careers from labor intensive aviation field to IT trying to get into the field with the right education and on the right path in the most efficient way(considering my age) any advice would be much appreciated… i hear gov tech is the way to go with certifications but not sure was debating between cybersecurity, data analysts or programming/coding… again i appreciate any advice!!
In my university, which is among the best 100 in the world, they offer to study Data Science as soon as we enter university, that is, without doing Computer Science, do you recommend me to study Data Science without studying Computer Science as an undergraduate? I appreciate your opinion, thank you very much X
Hello Shane, I’m currently an intern for the Biotech company, Berkeley Lights for something similar to the Data Scientist position (Business Intelligence Analyst) and what you said in the article is pretty spot on. The challenge of breaking into this field is definitely the career establishment since this field has only been properly defined in this century (21st century) and a decent amount of positions require at least a Masters for the Machine Learning knowledge for the job, but definitely a worthwhile career choice for those interested in Statistics and programming. Thanks for sharing your thoughts on the topic and keep on making very informational content :’))
I’m currently studying a diploma in Media Communications & Digital Marketing, but I don’t think I’d want to continue a pathway of content design, and I kind of have been interested in how engineering or mainly IT was like. I was previously studying a course retail & online business related, but I like retail jobs because they’re convenient in distance & overall also work well with my schedule but it doesn’t as pay well for most jobs compared to office jobs, I hope I know what career I’m interested in soon.
This was a great, honest, data driven and unbiased overview. I’m recently unemployed with about 14 years experience in healthcare, both in clinical and administrative settings and a BS in Health Administration and MBA. I’ve been wanting to make a career change for a while and started looking into data science, but all the bootcamps just feel like a sales pitch. I know there is some overlap between healthcare and data science, but I’m still trying to figure out what that job market is like. Most data related jobs I’ve seen in healthcare pay way below $100k/yr but maybe I’m searching for the wrong things.
What is the difference between a data scientist, data analyst, data engineer, statistician and statistical engineer? In my country thry offer a major in statistical engineering and data science, others in statistical engineering and other in statistics. I struggle to see the difference. Which one should I take? Should I go for this?
I have a math undergrad and my job is paying for me to go back and get my MS in applied statistics. I wouldn’t recommend this due to the hype, only if you genuinely enjoy spending your free time writing code and doing math. You can make more money with less effort doing something bc else so make sure you love whatever you do. Also would not recommend any business related degrees for quant finance bc you’ll be laughed at.
career wise, very good Major wise, I would avoid it. If you want to become a data scientist, you’re way better off getting a mathematics degree. Complex data science is all about math, and even the data science program at my school has less math than any engineering/math/cs major. Way better off getting a mathematics degree, or even a computer science degree for a bachelors. Masters is a different story in order to specialize in something, but you’re way better getting getting a general degree like CS/Math where you have lots of other options outside of data science if you don’t like it
You think you can stop learning at some point You’ve subscribed to a couple of online courses on Data Science and are reading a few textbooks. Now you think that once you’ve mastered those, you have learned enough to break through in Data Science. Wrong. This is yet the beginning. If you think you’re learning a lot now, think about how much you’ll be learning in three years. If you end up as a Data Scientist, you’ll be learning ten times more than you are now. It’s an ever-changing field where new technologies are constantly needed. If you stop learning once you’ve landed your job, your trajectory is going to go from a beginner in Data Science to a Data Scientist that sucks. If you want to excel in Data Science (and if you’re reading this, you do), you need to face the fact that your learning curve will get steeper over time. If you don’t enjoy learning Bigly, stop dreaming about being a Data Scientist.
I have been debating data science. I have graduate degrees in nuclear engineering with a BS in math and physics. I have done quite a bit of regression and inverse problems in my work. It’s been very difficult to get a job in engineering. I just got an entry-level engineering job in aerospace manufacturing and some here really hold my lack of an engineering BS against. Some of the entry-level engineers hired after me with engineering BSs are more involved in side projects, though one individual has previous manufacturing experience.
Just to note that a database administrator and a data scientist is like comparing apples to oranges. It’s not like software engineer vs software developer where in some companies it’s the same others it’s different. Database admin is someone who purely works with databases not data. Data scientists work with the data in those dbs. Database admins can be a developer dba where they find efficiencies by considering what kind of data exists in the dbs, but that relation is tangential if anything. So it doesn’t make sense to compare their career statistics with each other
can’t agree more… the knowledge for a certain domain is really important, data science is about problem-solving, given the hardest question and trying to solve it. if u don’t have a good knowledge about ur company’s business then u definitely cannot give the right method to solve the problem .. actually this is a highly competitive job in my country, the reason is because nobody cannot define what is data science? there are so many different definitions about it, each person has different understanding about this job. So the salary is also not so good in my country, i mean for an ordinary data scientist, some really good data scientists can get a very high salary who have a good business sense, be able to explain complex issues and present them in a clear and neat manner and also let the company save more money or earn more.
Data science is a good option for engineers and scientists in areas where there are less jobs. They have most of the skills needed to be a data scientist. Statistics and python are all they need to start off. Machine learning is linear algebra plus optimization which any engineer will understand immediately.
It’s a very nice article, explaining different concepts of this domain. Have 1 questions. Having a master’s degree in digital communication (electronic) and 5 yrs of s/w development exp in the past, when looking out for a career after a long break is Data scientist a good or perfected choice. Would love to hear back. Thank you!
I still struggle when describing what data scientists do. I still do a lot of data analysis, but that is a prerequisite for algorithm/model development, which is the end product. In the process, there’s also a lot of statistics. But I think what’s really relevant is the ability to understand the mechanism behind available AI solutions to determine which one works the best for a given problem and apply it. It does inevitably involve some degree of mathematical proficiency. Without it, it can be difficult to transition from an analyst to a scientist.
Hello Shane. I have a Bachelors Degree in Mathematics and I have been working as a Data Engineer for the past 1 year. I am always going back and forth as to whether I should co tinue down this road or switch over to Data Science. Would you consider it easy to transition organically from Data Engineering to Data Science if I choose to change my mind?
Great article. I’m a data analyst and just accepted an offer for data scientist position, so I have some ideas on the markets for both jobs. In my opinion, the window for people with no previous data experience and no degree in an analytical subject getting a job as a data scientist has almost completely closed. Those folks oftentimes won’t even get past the HR screener. There’s so many new data science and analytics Master’s programs that have opened up, along with the wide variety of analytical degrees that are acceptable (statistics, computer science, economics, physics, etc) that someone without an analytical degree and without any job experience as a data analyst or other data professional is just never going to get an interview. Even if you have a great portfolio, the HR screen is not even going to look at it. However, if you have experience as a data analyst and pick up one of these new data science or analytics master’s degrees the job market becomes super easy.
Great article Shane! I switched majors and did a lot of work comparing and contrasting between a Data Science undergraduate degree and a Computer Science Degree. After looking at my options, I decided on Computer Science. Maybe you could do a article on AI/ML Engineering and or Big Data Engineering? Maybe even the top specialties of Software Engineering article/tier list?