Is 30 Too Old to Learn Data Science?

If, as quite a few people have affirmed, 50 is the new 30, then 30 must be the new ten. And ten is a good age for kids to start learning how to program in Python.

The speciousness of the above math notwithstanding, 30 is definitely not too old to start learning data science. It may even be a better time than earlier in life. The lack of experience from which all people in their 20s suffer can make them less than ideal students: they’re too busy making bad life decisions to study effectively. Your head is considerably better planted on your shoulders once you reach 30. You’ll learn better, you’ll understand better why you’re addressing yourself to a topic like data science, and, when it comes time to approach the workplace, you’ll have a decade’s worth of work experience on your resume to go with your certificate for data science studies. Experience is in many ways more valuable than training, even when it isn’t exactly in the field in which you are seeking employment.

And, no, at 30, you’re definitely not a dog that’s too old to learn new tricks. Rather, you’re a seasoned dog that comes to data science knowing plenty of tricks already. 

Why Learn Data Science at 30?

Data science, once termed the sexiest job in corporate America by Harvard Business Review (no one was too sure what it meant, but it sure sounded good), remains an area with enormous growth forecast over the next decade: the Bureau of Labor Statistics puts it at a whopping 35% for the decade 2022 to 2032. Data science, once thought of as just a branch of statistics, has become one of the prime movers of business today. Rarely is an important corporate decision made without consulting the data scientist’s crystal ball.

The data scientist’s job, performed in tandem with advanced machine learning computing techniques, is to come up with predictive models that can do as good a job as possible of predicting an organization’s future. A principle in statistics is that, the larger your sample size, the more precise your result. That’s become a fundamental concept in modern data science, where datasets have grown to immense proportions and take into account everything from social media posts to audio and video files to text scribbled onto a comment sheet when you’re paying a restaurant check. All this big data input gets transformed from clutter to workable data through the legerdemain known as data cleaning. Through the further magic of machine learning algorithms, you can come close to predicting the future, and give very good and informed advice as to what sort of moves an organization should make. Whether or not your corporate superiors follow your advice is another story. But at least they won’t be able to say you didn’t warn them when they try to blame you for not anticipating an increased demand for air conditioners in summer.

Data scientists are well-rewarded for their efforts. The Bureau of Labor Statistics figures for 2023 list an annual mean salary of $119,000 and a median of $108,000 (if you’re interested in data science, you’ll need to know the difference between these two figures), with 90th percentile outliers as high as $184,000. Of course, you can’t expect to command salary numbers that high right after completing a bootcamp or certificate program, but those numbers are within reach with on-the-job training, experience, and a little patience. Moreover, the expectation that the job pool will expand at a precipitous rate makes the field an excellent one to enter into today. Moreover, scientific appraisal of data isn’t going to disappear: it may evolve, but data scientists are sure to evolve with it.

How Long Will it Take to Learn Data Science?

Anywhere from five minutes to eight years. YouTube has a video that says it will teach you data science in five minutes, and you can get a PhD in the field, which will take four years of graduate work on top of a four-year bachelor’s program.

Although the five-minute figure is obviously ridiculous, so is the eight-year figure excessive unless you’re planning to remain in academe and teach future generations of data scientists. Most people approach the data science job market with a master’s degree in the field, which can be earned after a bachelor’s degree in either data science or a field such as mathematics, statistics, or computer science. That’ll take at least a year on top of your bachelor’s degree, which is still an awfully long time, although you will earn a college diploma in the process.

There are other ways that take months rather than years, and that will have you prepared to assume an entry-level position without needing to take the long way through an undergraduate program that comes with required courses that have nothing to do with data science. These other ways are targeted classes, bootcamps, and certificate (as opposed to degree or diploma) programs that are designed to teach students as much as possible about data science in as little time as possible. These courses prepare candidates for the job market, and eliminate superfluous classes, preferring to teach you the skills you need to get hired. Thus, you’ll have no papers to write on A Farewell to Arms, but you’ll have a lot of Python to learn in a matter of just a few weeks.

These programs generally top out at three months if you take them full-time. (And full-time entails classes that meet nine-to-five as well as homework. The bootcamp term wasn’t coined for nothing, although this type of bootcamp won’t teach you how to make your bed with hospital corners.) Quite a few of these courses run for half that time or even less, including Noble Desktop’s Data Science Certificate and the slightly longer Data Analytics Certificate, which runs for about six weeks. These, you should note, are certificate programs designed for absolute beginners. Shorter courses, such as Noble Desktop’s Python for Data Science Bootcamp, take people who already know how to program in Python into the world of the NumPy and pandas libraries and other ways in which that highly versatile computer language can be used to abet the data scientist’s professional efforts.

To return to the original answer, you can learn data science in anywhere from five minutes to eight years, but closer to five minutes.

Ways to Make Learning Data Science Easier and Quicker

If you’re over 30, you have no doubt acquired transferable workplace skills that can be applied to a career shift to data science. If you know Excel, it can be used to solve relatively simple data analysis problems. Similarly, you may have had experience writing SQL (Structured Query Language) queries to extract information from a database. Knowing these things means that you won’t need to begin at square one with your data science studies, which can substantially shorten the time you’ll need to study before entering your new field of choice.

Perhaps the most useful skill you can have before undertaking data science training is the ability to program in Python. Python is a multi-use language that can be particularly felicitously applied to data science, as one of its strengths is its ability to handle tortuous calculations. If you’ve learned how to take advantage of the NumPy library as well, you’ll save yourself quite a bit of time compared to a bootcamp curriculum that’s designed for absolute beginners. You’ll thus be better off shopping for briefer courses that will teach what you need to learn. Noble Desktop divides its certificate programs into modules, many of which can be taken independently. Thus, if you already know Python and NumPy, you may well be able to start your data science journey off by joining the train at the Python Data Science & Machine Learning Bootcamp, which will show you how to use the pandas library and other data science-specific applications of the programming language before moving along to the machine learning that serves as the backbone of all data science today.

In addition to taking advantage of what you already know, how you study can be an essential factor in the speed with which you’re able to navigate a data science class. Online classes (and most classes are online these days) come in two very different flavors, grandly termed asynchronous and synchronous. In the former, you’re working with a series of video tutorials on any schedule that is convenient to you. There is no live teacher with whom to rendezvous in real time, so you’re on your own, for better and for worse. In a synchronous course, you are connected to a teacher in real time, and attend school at fixed hours so that you may interact with your instructor, ask questions, and even share your screen in the event that you’ve worked yourself into a Gordian knot that needs cutting.

In terms of timing, you set the pace for an asynchronous class, which means that you could conceivably jump to lightspeed and get through something like 40 hours of video tutorials in, well, in under two days. Data science tutorials weren’t made for binge-watching, however, and you’ll probably get hopelessly muddled if you try to get through a course in that kind of time. At the other extreme of working your way through an asynchronous course is the all-too-human problem of losing momentum and not finishing the class, either in the time allotted by the provider or ever. Keeping after yourself to finish a course like this takes a great deal of sticktoitiveness. Most people signing up for asynchronous classes have other responsibilities, so falling behind, losing interest, or just plain old running out of gas are, if not inevitable, definitely common pitfalls to the teaching modality.

Thus, while you could conceivably complete an asynchronous course in less time than it takes to complete a synchronous one, the realities of human nature are such that your best bet for getting through a course as quickly as possible is if you select one that involves a live teacher. A sentient being to accompany you as you wend your way through the Forest of Pythons through to the Mountains of Machine Learning is invaluable for any number of reasons, the first being the Gordian Knot mentioned above. As a field, data science is rich and complex, and, unless you’re a data science prodigy, you’re going to have questions about what you’ve studied, and need someone to answer them if you’re to progress successfully onto the next lesson. The live teacher also guarantees the pace of the class: if it’s supposed to take six weeks, the instructor has to finish in six weeks, and will see to it that the students’ time is used to their best advantage. Thus, to mix musical and racing metaphors, while you could conceivably make your way through the course prestissimo, you run the risk of the presto turning adagio by the time you get to the backstretch. The best pace is probably an allegro non troppo that can be sustained all the way to the finish line.

The live course is your best bet for any number of reasons, not only because you get a teacher (and, sometimes, a dedicated mentor with whom you meet 1-to-1 outside of class), but also because of a drawback inherent to any asynchronous class: the expiration date problem. Data science is a dynamic field that is constantly changing. New technologies are always being invented, and, if you are to be able to secure a role in the field, you’ll need to be on top of whatever is latest in the world of data science. A synchronous class is able to adapt itself to these evolving and emerging trends: if something happens to cause a major disturbance in the data science Force, a live instructor can immediately alter a lesson plan to take it into account.

When you’re working asynchronously, your entire curriculum has been pre-recorded, Heaven knows when, and, while it isn’t the case every single time, the reality is that time waits for no man, least of all where technology is involved, and you’re as likely as not to draw some out-of-date modules if you take a self-paced class. Although there may be reasons why you have to resort to an asynchronous course, the practical reality is that the best and most efficient way to learn data science is going to be with a live teacher who can be with you every step of the way, respond to your questions, and encourage you to complete an arduous course of study as quickly and efficiently as is humanly possible, regardless of that insignificant factor, your age.