What is the Best Course to Learn Data Science?

There is more than one way to learn data science: classes come in a variety of formats and delivery methods, and you’ll have to peruse these to find the one that works best for you, your schedule, and, yes, your wallet. Among the things you’ll have to consider are what, exactly, you need to learn, how much time you have to learn it, and how much you can afford to spend on your training.

Are You Brand New to Data Science?

The first thing you need to evaluate is whence you are starting your education. Some people come to data science with a background in computing (perhaps they know Python), statistics (perhaps they’ve taken a class in that, or learned how to use Excel’s statistical functions), or maybe even some more advanced knowledge of machine learning or data visualization. Others have no background in the field or its component disciplines of which to speak, although they bring with them plenty of goodwill and interest in learning.

If you’re a neophyte, you’ll be limited to courses that are termed beginner-friendly, and which will start you off with the absolute basics, perhaps using Excel to demonstrate statistical operations, or perhaps with an introduction to statistics in general, including sorting out medians from means and standard deviations. You have to start somewhere, and there’s nothing wrong with starting out at the beginning. You can even start yourself off with a statistics class at a community college, both to get your feet wet and to lay a solid foundation for what’s to come. There is no shortage of classes suited to data science beginners; just make sure that the course you select is designed for people at your level.

If you do have some knowledge of the field, you’ll face the challenge of figuring out where to jump in, and how to find an intermediate-level class that assumes the knowledge you already possess. Taking too elementary a class can be helpful for some (those who want to cement their knowledge before moving along to something new), but it can also be tedious and uninspiring for people who don’t want to go back to learning the difference between a mean and a median. Read the syllabi for the different classes carefully to make sure that you’ve found a class at the right level. That could prove tricky, as most bootcamps are designed for beginners, but intermediate and advanced classes are to be found. You may just have to look a little harder.

How Much Are You Willing to Pay?

Regardless of how good an investment in your future a data science course may be, you’re still going to be governed by how much you can afford. As should be obvious, a longer and more in-depth certificate program is going to cost more than a one-week class that introduces you to Python. A bootcamp can easily cost upwards of $10,000 (although there are more economical options), whereas a brief class can cost you in the hundreds rather than the thousands.

Taking a brief course in Python or some other component of the data science constellation may be within your means, but you also need to consider whether that’s going to give you a knowledge base to improve your lot on the job market. You could very conceivably be wasting resources if you only half-learn the subject, and be aware that even extended bootcamps teach a comparative minimum of information when compared to a four-year college program.

This raises the very thorny question of going into debt to finance your education. Most schools offering bootcamps offer some kind of financial arrangement to ease the tuition burden. A lot of these options are 0% and divide your tuition into several more manageable payments, sometimes for the length of the course, sometimes for a year. Some schools, through third-party lenders, offer even more extended payment plans, although the interest involved in these can become a serious problem at the other end. A third payment option sometimes encountered postpones your first payment until you’ve obtained employment in the data science field, although that’s going to take a bite out of your first paycheck that can come as a most unpleasant surprise. The pros and cons of borrowing money to finance your education are complex and require a great deal of serious reflection and reading of all the fine print on your part. 

How Quickly Do You Need to Learn New Skills?

If you’re in a hurry to get started with a new career, or if you’re going to need to be making money again as soon as possible, a concentrated bootcamp or certificate program is going to be the most efficient way to learn as much about data science in as little time as possible. You can also learn less in even less time, as with an introductory Python class, although that is probably going to leave you with an incomplete skill set.

If you have more time on your hands, or if you are still working a full-time job, you can learn data science in a part-time bootcamp that will reduce your lesson time to a few hours, a few days a week, rather than eight hours, five days a week. This is a much easier schedule, and the only one that will allow you to juggle other responsibilities.

You can also consider an on-demand class if your time availability is at either end of the spectrum. You can concentrate on your video tutorials and cover your entire course in a matter of a couple of weeks. You can also coast your way and spread the program out over six months (or however long they give you to complete the course.) Convenience is the hallmark of the self-paced class, and part of that convenience is that you can take as long (or as little) time as you like for your studies.

How Much Technical Training Do You Need?

The answer here is as much as possible. Data science isn’t a field in which a small amount of knowledge is going to get you especially far, particularly as the field is liberally peppered with people who hold graduate degrees. An intensive bootcamp will teach you a lot, but even that a lot is closer to the bare minimum you’ll need to get a foot inside the professional door.

There is no way that a little Python and some experience using Excel to calculate standard deviations is going to get you hired in even an entry-level job in data science. You’re absolutely going to have to know more advanced Python data science techniques, how to use the NumPy, pandas, and Matpotlib libraries, how to query databases with SQL, and possibly have a handle on how to handle unstructured (NoSQL) databases. Yes, this should be able to get you a job, but remember that you’ll be going into a field that is full of people with advanced degrees, which means that you’re going to have to learn even more on the job, and pretty quickly, too. When it comes to breaking into a complex technical field like data science, knowledge most definitely is going to be power.

Do You Prefer In-Person or Online Training?

You no doubt began to learn in kindergarten (unless you were in kindergarten in 2020) by being in the same room as your teacher, and having the latter write on a white- or blackboard while talking to you, breathing the same air as you, and generally inhabiting the same space as you. It’s a good teaching model, and it probably saw you through high school (and beyond that, if you’ve been to college.) You may, as a result, wish to continue learning that way when it comes to your data science course. Alas, there’s disappointing news if you have your heart set on that: in all but the very biggest markets (New York, Chicago, Los Angeles), live data science classes can be as rare as hen’s teeth.

You’re very likely going to face the inevitability of taking an online class, although there is nothing at all wrong with that learning modality, even if it’s as yet unfamiliar to you. You’ve probably already learned something from the internet, be it how to make an orange mousse or how to fix a running toilet. As the Repository of All Human Knowledge Ever, the internet has its pedagogical uses, and the online class is one example. Don’t forget the all-important fact that, in a live online class, the teacher is still in real-time with you, give or take the time it takes for the information to travel at the speed of light from one computer to another.

You’ll always be able to ask your instructor questions and get immediate answers, while the magic of teleconferencing will allow your teacher to share your screen and rescue you from whatever programming shoals into which you steer yourself. You’ll thus benefit from practically all the advantages a live in-person class offers, with the added benefit that you can study from your own space, in a comfortable chair, with the air conditioning just the way you like it, and without needing to drag yourself to and from a brick-and-mortar schoolhouse, uphill both ways through driving snowstorms.

In either event, finding classes is as quick a matter as a wave of the Google magic wand. Be advised, however, that online schools will unuqestionaly turn up in a search for, say, “in-person data science classes, Lost Springs, WY.” Thus, when looking at your search results, don’t get your hopes up that a local class exists.

Learn Data Science Skills with Noble Desktop

Noble Desktop provides in-person classes in New York City and brings its offerings to the rest of the world thanks to the wonders of the internet. It can thus accommodate you either way if you reside in the Tri-State Area, and otherwise accommodate you very well online. Its catalog of data science classes offers courses ranging from the Data Analytics Certificate program that goes into the matter in considerable depth, from your first statistical steps in Excel all the way through to machine learning and using Tableau to create compelling data visualizations to show to people who wouldn’t know lasso regression from a lasso. You’ll need close to six weeks to complete the course full-time, or upwards of six months if you’re taking the certificate program part-time.

Noble Desktop makes many of the components of the Data Analytics Certificate available, either individually or in smaller packages. The Python portion of the curriculum, in particular, can be taken separately, be it as the Python for Data Science Bootcamp, or, if you already know your way through the fundamental spam and eggs of the language, the Python Machine Learning Bootcamp. You can even take the Tableau module independently of the rest of the extended certificate program. Therefore, Noble Desktop allows you to construct just the type of curriculum you require, given what you already know and what you need to know. You thus have a choice between the in-depth certificate programs and the flexibility of ordering your training à la carte.

All Noble Desktop’s data science classes include a complimentary retake option and recordings of all the classes in your program, so you’ll be able to refer back to them in the not unlikely event that you don’t catch something first time ’round. Noble’s cutting-edge teaching materials and workbooks will be yours to take home for future reference at the end of the course. You’ll also receive 1-to-1 mentoring sessions (the longer the course, the more sessions to which you’ll be entitled) that can be used however you desire, be it to go over something technical your thick skull won’t let in, portfolio curation, work on your job-search materials, or even mock interviews. Students regularly find these mentoring sessions to be an invaluable adjunct to their classroom studies.