Are Data Science Classes Worth It?

If you’re looking to make the jump to a career that Forbes says will have a 31% increase in its workforce by 2030, then, yes, taking data science classes is probably worth the investment. More and more businesses are realizing what data and their analysis can do for them; as a result, more and more jobs are being created in the field. Don’t, however, get the impression that all you have to do is take a quick Python class, and you’ll be making a six-figure salary. 

Data science is an extremely competitive field, and even people with master’s degrees are finding it hard to land a position. That’s not to say that there aren’t opportunities, but, whereas breaking into data science as recently as a few years ago wasn’t overly difficult, that no longer applies today. You’ll, therefore, have to evaluate the pros and cons in order to decide whether data science classes are worth it for you.

Consider the Costs

The minute “worth it” comes into the discussion, the sticky wicket of money is going to follow. Data science classes are readily available, but they generally don’t come cheap. You’re more likely going to have to make an investment of at least several thousand dollars to learn enough about the subject to make yourself a viable entry-level candidate for a data science position, and few and very far between are the schools that guarantee that you’ll find employment when you’re through. If you do land a job and start your way up the data science ladder, you may well end up making more than you did pre-career change. 

There is nonetheless an undeniable element of risk: you can very possibly spend quite a lot of money on a class and not end up where you wanted. The problem is worthy of a data science investigation all its own. And, while financing is available from most schools that offer data science classes, you’re going to want to be careful about spending more than you can afford or going into debt, even if it’s an investment in your future. 

Advantages of a Live Data Science Class

Although there are alternatives to taking a live data science class, most notably a self-paced on-demand course, by far the most recommendable option is a class that puts you and your instructor in the same moment in time. You very likely may not end up at the same point in space—most data sciences classes today are offered online, so you’ll be in your space but linked to the instructor by the wonders of an online teleconferencing platform—but there are incontrovertible advantages to being able to work in real-time with your teacher. None of the other teaching modalities available are able to offer this.

General Benefits

Learning in a live class (be it in-person or online) means that you’ll be working together with a teacher who, in addition to instructing you, will be able to monitor your progress, make sure you’re not lagging behind, and answer any questions you may have along the way. In an online class, the teacher can even have access to your screen with your permission, which will help you out should you get stuck on an exercise, as will probably happen at some point. As your class will meet at a specific time, you’ll additionally be responsible for showing up on time; that investment of discipline will be a big help in getting you across the course’s finish line. 

Learning Theory and Practice

There are two types of knowledge you’ll need to acquire if you’re to complete a data science class successfully. One of them is theoretical: you’ll need to learn the underlying statistical concepts upon which data science (which, arguably, is just a branch of statistics) is based. The other is technical and practical: you’re going to learn fundamental programming concepts that will allow you to analyze actual data. Although a number of programming languages can serve that purpose, the odds-on favorite is that you’ll be learning Python.

Another likelihood is that your live class will be hands-on and will have you working with code and programming problems from the first day. Although you can learn a subject entirely in the theoretical air, most people assimilate new concepts far more effectively by doing: you can read how to make a soufflé, or you can get out your mixer and beat egg whites the way Julia Child tells you and hope the thing rises in the oven. Only one method is going to keep you from getting hungry, and the same applies when it comes to hands-on computer learning.

Personalized Feedback

Working with a live teacher also guarantees you the kind of feedback you’re going to need if you’re going to learn your subject correctly. Passively watching video tutorials, as you would in a self-paced class, won’t give you anyone to check on your progress, tell you where you’re doing things right, and, more importantly, tell you where you’re doing them wrong. A live teacher is invaluable in that respect, and the one way you’ll be able to get one is by signing up for a live class. No one works well in a vacuum, and a good instructor can give you the kind of emotional and intellectual support you’ll need if you take on an arduous data science class.

Building a Portfolio

A good data science class is also going to have you work on a number of projects that will then form the basis of your portfolio when job search time rolls around. These projects can be things such as fake news detection, predicting everything from forest fires to equity performance to survival on the Titanic, or just creating an AI chatbot. Given the expansive definition of data today, the range of possible projects is enormous. More importantly, these projects will then constitute the portfolio of your work you can exhibit to prospective employers so they can see what you can do. You obviously won’t have a PhD-level portfolio after a single data science class, but you’re also not going to be competing with data science PhDs for the same positions.

A self-paced class won’t result in a curated portfolio such as can be created in a live class with a teacher or dedicated mentor who can go over your job-search materials with you. And there is a certainty that the HR people considering your candidacy will want to see a portfolio of your work.

Considerations When Looking at Data Science Classes

Once you’ve decided that you’re interested in a live data science class, you’ll have to decide on which class to take. You may find the choice bewildering, given the number of options, which is a good reason to proceed as methodically as possible through the selection process. By all means, do your homework, know thyself and thy needs, and take your time making a decision that, if all goes well, will completely change your future.

General Considerations

If you live in a city that offers in-person data science classes, you’ll be faced with the decision between learning in school or having school come to you through an online class. Online learning might seem like a strange idea if you’ve never experienced it, and you may find yourself harboring a bias towards an in-person class, as learning in the same room as your teacher is probably how you did most of your learning up to this point in your life. The success of online education, however, has shifted most data science classes to an online format

Looked at from the provider’s standpoint, the online modality makes it possible to fill seats for a class with people from different markets. There is usually mercenary logic like that behind major paradigm shifts, but, in this case, the study-from-where-you-are class offers a great deal of convenience to the student as well. Consider, first and foremost, that you won’t waste time and incur frustration by having to get to a brick-and-mortar classroom at rush hour. That’s in addition to the convenience of being able to study in your own comfortable space in which you get to control the air conditioning and sit in a chair that is pretty much guaranteed to be more comfortable than anything you can find in a brick-and-mortar classroom. You’re going to need a computer with a good internet connection, and a lock on the door, but there is a likelihood that you’ll already have those things before you start looking for a class.

As you search through the online data science offerings, you will unquestionably come upon a self-paced or asynchronous class in which you are a passive participant and watch a series of video tutorials, not unlike a lecture course in college. The immediate shortcoming where on-demand learning is concerned is that you have no chance to collaborate with your instructor, be it to ask questions or to receive assistance when needed. Convenient such classes certainly are (you can view the tutorials whenever you like, or whenever you can), but the convenience comes at a steep price. 

What Skills Do You Need to Know?

There are quite a few different skills that you’ll be expected to possess if you’re to break into the data science field. At the top of the shopping list sits the need to be able to write code in a programming language that is suitable for the kinds of large-scale mathematical computations that go with the enormous datasets that data science professionals routinely encounter today. The two preferred languages employed for this purpose are Python and R, and, of the two, Python is markedly easier to learn. It is, therefore, the language of choice in most data science courses. Python comes equipped with libraries that come in extremely handy when working with data, most notably NumPy for calculations and pandas for data processing. You’ll need to know how to use both of them.

Beyond that, you’ll need to know a thing or two about machine learning, a form of artificial intelligence that is employed to tame the gargantuan amounts of data that are employed nowadays. Machine learning can automate repetitive computer tasks that, because of the scale of big data, simply cannot be done by hand. Machine learning is also responsible for such processes as regressive modeling, which uses thousands and thousands of fragments of data. To get an entry-level position, you won’t need to be an absolute expert when it comes to machine learning, but you will need to know your way around the subject on a practical level and to be able to program a computer to perform the kinds of AI tasks that are essential to data science.

The other thing you’ll need to know about is data visualization. That is, how to arrange your data into charts, graphs, and tables that will illustrate your analyses and make them understandable to people without a technical background in data science. Quite a few tools exist that are capable of creating all kinds of static and moving visualizations. Those include a further library for Python, Matplotlib, as well as business intelligence (BI) software such as Tableau that can create exceedingly handsome dashboards without too much effort on your part.

People assessing your resume are going to expect to see this trifecta of skills. Fear not: these are precisely the skills that a good data science class will teach you.

Is it Worth Enrolling in a Data Science Class?

Data science classes aren’t necessarily for everyone. Some people may benefit immensely from one, while others really have no need for formal education in the field. The “is it worth it?” question here refers less to unavoidable money matters, and more to what you stand to gain when you sign up. 

Who will find it worthwhile?

Data science classes are tailored mainly for people seeking to start careers in the field, be it a career switch or a first professional direction you want to follow. Although a so-called bootcamp or certificate program will delve into the relevant topics in greater depth, most classes are still intended for people whose eyes are on a new career. Although there are risks involved in such a move—the competitiveness for jobs in data science today being the largest of these—jobs in data science can pay very well indeed, and there is every likelihood of job security given that positions are expected to increase considerably in the coming years.

Who might find it worthwhile?

Above and beyond the career-changer, a data science class can be of use to those who own their own companies and wish to learn as much as they can about using data without having to hire a full-time data scientist or analyst. That eliminates the potentially prohibitive cost of bringing a Data Scientist on staff.

The other category of people who might benefit from a data science class are those who are interested in the field, but still need to decide whether it is for them. As an alternative to making the sizable investment involved in a bootcamp, taking a class or two in the subject won’t qualify you for a whole career as a data scientist, but will let you know whether you should pursue more aggressive educational opportunities. A small taste of what data science is can spare you wasted money on a bootcamp, and, if nothing else, will leave you with a modest foundation in Python that you can put to use in a different IT field.

Who probably doesn’t need a Data Science class?

It may sound a bit tautological, but people who don’t want to work in a data science capacity probably needn’t bother with a data science class. You’ll have data scientists and analysts on your company’s staff who can do the big data heavy lifting, and whose job it is to turn out visualizations lay people like you can understand. Also most likely not in need of data science training are those who know they lack the discipline to attack a complicated field that requires a wealth of specialized knowledge. There are less difficult IT careers to pursue than data science. Finally, a data science class isn’t the best place to begin if you want to learn to code in Python. You’ll get a specialized rather than a general view of the language that, while it can be taken in a different direction, isn’t designed a priori for budding coders.

Learn Data Science Skills with Noble Desktop

If you’ve decided that attending a data science class is the correct path for you to pursue, Noble Desktop can offer you a choice of classes that will help you reach your goal. If you wish to make a substantial time commitment in order to get yourself ready for a new career, you ought to consider the Data Science Certificate program, an extended course of study that will equip you with the knowledge you’ll need to have to get your proverbial foot in the door. The program’s curriculum is based on Python, which you’ll discover how to use from a number of different angles, including the machine learning so essential to all data science activities today. In addition to Python, you’ll learn Standard Query Language (SQL), a means of getting information out of a structured database. (A structured database is one built with organized fields that can be filled in; unstructured data is just that, big piles of almost random information that need to be beaten into shape using different technologies before they can be used.) The course is a sizable commitment, but it can be taken part-time or full-time. The full-time class takes a bit over a month to complete.

On the other hand, if you’re more interested in a shorter class that will teach you one aspect of data science, most of the curriculum units of the Data Science Certificate are available à la carte. Thus you can select the Python for Data Science & Machine Learning Bootcamp. Here, the concentration is entirely on Python and how it is used in the data science profession. The curriculum resembles the Data Science Certificate program, with the primary difference being that the SQL unit is omitted. This will make the course particularly useful for people who know SQL but wish to learn Python to go with it.

Considerably briefer are two classes that teach parts of the Python curriculum just described. The Python for Data Science Bootcamp teaches fundamentals of Python programming and can be completed in a week. Of a similar duration is the Python Machine Learning Bootcamp, which takes students with a knowledge of Python, NumPy, and pandas through the mechanics of machine learning, including such techniques as regression analysis and classification algorithms (such as the ones that Netflix and YouTube use to recommend content.) All these classes come with a free retake option, recordings of classroom sessions, Noble Desktop’s proprietary state-of-the-art learning materials, and 1-on-1 sessions that will have you working with an experienced mentor who will be able to offer sage counsel as you make your way through your course.