How Long Do Data Science Courses Take?

A data science class can last anywhere from a week to six months, depending on the curriculum and whether you choose to attend full- or part-time. As not everyone has an endless supply of time, which is a commodity at least as precious as data, you’ll want to be sure that you’re allotting just the right amount of it to your studies.

Lengths of Classes

As with any skill, the time required to obtain a solid data science education depends directly on how much knowledge you want to gain and how much time (and, yes, money, although that’s a subject for another article) you can and want to invest in your training. You’ll find classes that last a scant week, and there are PhD programs in data science that require at least four years of coursework and toiling on your dissertation above and beyond the four years you needed to earn your bachelor’s degree.

The length of a class is almost always proportional to the depth in which it covers its subject matter. Thus, shorter classes cover essential basic material, while longer courses focus more on the building of a basis for making a career in data science. That’s a decidedly involved body of knowledge, which explains why you can spend eight years studying the field and not run out of things to learn. Even if you don’t want to invest nearly a decade of your life in learning data science, you’ll discover that you can’t learn a data scientist’s skills overnight.

Introductory Classes

You’ll find that even brief introductory courses in data science are going to take up to about a week, and that’s assuming you’re studying full-time. The most elementary classes in the subject steer clear of some of the most important aspects of data science, such as programming in Python and machine learning. These classes use Excel as the technology for data analysis, which is a handy approach as far as it goes. They will undoubtedly introduce you to some basic concepts of data analysis and show you how to make the most of Excel, which may be useful in certain positions, but it won’t set you on the path to a career in Data Science. These classes can, however, help get you to the starting line for deeper studies.

Other introductory classes do go into the rudiments of Python for data science, as any serious big data project requires programming knowledge and the ability to create machine learning models to clean and process data. A brief course will, again, only give you the most basic nuts and bolts of these technologies, but you’ve got to start somewhere. You may also find an SQL class that teaches you that language and how to make your way through structured databases, which is a good data science skill, even in the face of the current trend that favors vast oceans of unstructured data that make up NoSQL databases. You’ll find that these classes run for about a week if taken full-time, and, usually, for around a month if taken part-time, depending on how your school defines that term.

Advanced Classes

To build upon the foundations laid in an introductory class, you’re probably going to go on to discover more about Python, and, specifically, about how it works together with the machine learning that is the backbone of today’s data analysis. These won’t make you a fully qualified data scientist, but, if the field interests you, moving along to machine learning and AI will grant you a look at the kinds of procedures that make the data science world go ‘round. These classes generally keep pace with the introductory ones described above, which is to say they will take up from a week (full-time) to a month (part-time.) Thus, working 40 hours a week (plus homework), you can sink your teeth into a big bite of data science in about two weeks. You may, however, discover that you’re still a little hungry when you’re done.

Data Science Bootcamps

A bootcamp is an immersive, intensive, and concentrated course of study that is designed to make a data scientist of you. These courses may begin by teaching you how to use Excel as a means of understanding basic concepts, or they may dive into Python right away, before moving along to SQL, machine learning, and then to data visualization. Covering all that material is going to take time, and you’ll find that data science bootcamps can last anywhere from one to three months full-time, and, part-time, from five or six months through to ten months, again depending on just how part-time the part-time class is. A bootcamp is designed to launch you on a data science career, and it should be specified that these full-time programs require attendance eight hours a day, and often involve homework that can increase the workload to as much as 80 hours per week. Bootcamps don’t involve early morning calisthenics in the snow, but you might find yourself longing for that kind of bootcamp by the time you’re finished with your data science studies.

Data Science Degrees

For the patient and financially solvent, going to college and getting a data science degree represents the most usual way of entering the data science profession. The most usual degree is the Bachelor of Science in data science, while other related degree fields include mathematics, statistics, and computer science. These are all four-year programs, although, if you’re dedicated (or if you wish to specialize in data science after you’ve finished a BA program in math), you can continue into graduate work and obtain a master’s degree in data science. That will take at least another year, and a PhD in the field (which really isn’t necessary to secure employment, although your dissertation will make a smashing addition to your portfolio) takes some four to five years after you’ve finished with college. Finally, although it’s of questionable usefulness, some community colleges do award associate’s degrees in data analytics or data science. Those take two years to complete full-time, although, in all honesty, you might do better with a bootcamp than an AS degree.

On-Demand Classes

In addition to the synchronous live classes just described, you can avail yourself of an on-demand or asynchronous class in which you set the pace by which you watch the video tutorials of which the course consists. You’re probably not going to want to binge-watch them the way you might Bridgerton or The Mandalorian, especially as each tutorial episode usually comes with assignments to be done offline. (One of the shortcomings of the entire on-demand learning paradigm is that most courses don’t offer anyone who can correct your homework.) Most on-demand classes come with a time limit, which is the provider’s way of making sure you stand a chance of finishing the course, as you can quickly lose momentum when following an on-demand class. These time limits vary from provider to provider, although they generally fall out between six months and a year. That should be ample time to complete your course, although, for the inevitable fee, extensions are often available.

Part-time or Full-time?

Whether you opt to take a part-time or a full-time course depends on a number of factors, the most important of which is whether you have the time to study eight hours a day, five days a week, in addition to time outside of class to complete homework assignments. Although the weekly commitment varies from school to school, a figure of 70 to 80 hours’ work per week isn’t an exaggeration for the kind of commitment you’ll be making. Clearly, such a program isn’t going to be a possibility for anyone who’s already working full-time. Whether you can combine it with a part-time job depends on how keen your time-management skills are.

This is why they came up with the part-time bootcamp, which requires a still substantial but more realistic 20-odd hour per week commitment. These are programs that are designed for people who are working full-time yet who are committed to improving their professional skill sets. You’ll probably be called upon to be present in class for two or three evenings a week, which is nonetheless considerable if you’re doing it after a hard day’s work. The question then arises as to whether there are any defects to part-time learning, or whether it’s the same thing as full-time, except for the fact that it takes longer to complete. The reality is that part-time has a potential shortcoming in that you’re not involved in a fully immersive program in which you eat, drink, and sleep data science for however long your full-time bootcamp takes. On the other hand, a full-time bootcamp can go by too quickly for some people; the part-time version gives the information longer to soak in. Your choice between full- and part-time will probably depend on your employment situation, but be aware that certain learning styles and paces are more or less well-suited to certain brain types.

Learn Data Science Skills with Noble Desktop

Having made your decision to pursue a data science class or bootcamp, you’ll find Noble Desktop can accommodate your needs, regardless of the length of the program in which you wish to enroll. Noble offers two bootcamps in data science, a Data Science Certificate program, and a more extended Data Analytics Certificate program. Both are Python-based curricula, so you’ll learn the basics of that programming language before branching off into specific data science applications, including machine learning. You’ll also learn Standard Query Language for summoning up data from structured datasets, as well as a number of techniques for data visualization. To this, the more extended program adds modules in Excel for data science and Tableau for even fancier data visualizations. You’ll need upwards of a month for both programs (obviously more upwards in the case of the Data Analytics Certificate) if you take them full-time, and upwards of six months for the part-time versions.

Noble Desktop also offers shorter classes in data science, perhaps most interestingly the Python for Data Science & Machine Learning Bootcamp. As the name suggests, the class specializes in Python and how it may be applied to data science. The class, therefore, covers the fundamentals of the programming language, and then its essential data science libraries, NumPy and pandas, along with such things as classification algorithms (like the ones Disneyland and Netflix use to guide you to attractions and movies you might enjoy) and regression analysis (to predict the number of guests a theme park will have in a given day.) All of these classes include a free retake option, classroom recordings (so you can catch up on something you might have missed during the day), state-of-the-art workbooks, and 1-on-1 sessions with a personally assigned mentor who can help with everything from explaining the bits about regression analysis you can’t seem to figure out to polishing your resume, portfolio and other job search materials.