Is Data Science a Hard Class?

There’s no sense in pretending that learning data science is as easy as, say, learning to scramble an egg or play Crazy Eights. Data science is an interdisciplinary field that incorporates elements of mathematics, statistics, computer programming, and machine learning. That’s a lot, especially if you’re a beginner with no background in any of those fields. If you can program in Python, you’re already ahead of the game, and you’ll have less of a mountain to climb. However, even if you have the whole mountain to scale, you shouldn’t be afraid or intimidated, since many people just like you have made it to the summit unscathed. You’re just going to need determination, willingness to explore new things, and some genuine interest in the field. With those (and some control over any arithmophobia you might have) in hand, you should be able to complete a course and emerge with an intelligent grasp of what makes data science tick.

What is Hard about Learning Data Science

One of the major challenges people getting data science degrees in college face is the sheer amount of math they’re required to study. Advanced calculus and linear algebra are almost always required courses. Sometimes, the dreaded Differential Equations course is required as well. And that’s not reckoning with statistics, from the basic (the means and standard deviations stuff) to advanced probability theory. Without the latter, there would be no regressive modeling nor any of the fancy operations data science can perform to give an idea of what’s going to happen to an organization if it persists in its determination to open an air conditioner emporium in Utqiaġvik, Alaska.

Alongside all that math, data science students also need to learn to program a computer. The good news here is that, more often than not, the first language they learn is Python. Created by Guido van Rossum and first released in 1991, Python is a general-purpose programming language that was designed to be user-friendly: its commands correspond as closely as possible to English words, and its syntax resembles that of human languages.

There is a computer language that was expressly designed for statistical computations. Named R, it is sometimes taught in place of Python, although it is far less ingratiating than Van Rossum’s language. Data scientists usually end up bilingual, but Python is far and away the more accessible place to start.

After Python, the next thing to learn is the language’s libraries of code that turn a multi-purpose language into something ideally suited to data science. These libraries, NumPys, pandas, and Matplotlib, are among the reasons why Python has become so popular with data scientists and statisticians. The level of difficulty here isn’t too terribly high: you are still working within the Python universe, in which such dicta as “simple is better than complex” and “sparse is better than dense” prevail. NumPy is the library of computational operations that can do things like multiply matrices faster than you can say “In-N-Out Burger Spread” (or “thousand island dressing.”) Pandas, on the other hand, is a library of pieces of code you can plug into your Python project to clean, process and otherwise manipulate datasets. It’s the Python data processing tool par excellence. Great news: pandas is as easy to learn and use as giant pandas are cute.

Matplotlib, the library that enables the user to create data visualizations (charts, graphs, and tables), is far less cooperative than NumPy and pandas. One Reddit user described it as a pain in the, um, hindquarters. Matplotlib is definitely unpythonic, meaning that it’s cumbersome and complicated, mainly because it was designed back in 2003, when Python was nothing like the big thing it’s since become and before the concept of Pythonic code had really caught on. Ironically, Matplotlib might make more sense to a time traveler from twenty years ago than it does to programmers today. Still, it’s a versatile tool, and it’s definitely a hoop through which any budding data scientist will have to jump.

Another such hoop is SQL, Standard Query Language, which is used to extract information from structured databases, which is to say, a database that is arranged neatly, with each entry fitting into a field such as name, address, or favorite flavor of ice cream. SQL is, fortunately, one of the easier things data scientists need to learn.

Where data science becomes truly complex and potentially difficult for the newcomer is when, with all the above tools in hand, the student has to tackle the most exciting aspect of data science: machine learning. A branch of hot-topic AI (artificial intelligence), machine learning enables the computer to sort through enormous sets of unstructured data, organize them, and then process them to come up with the answers to the data scientist’s questions. There’s no way around the fact that machine learning is potentially challenging, especially once you’re past the earliest stages, which can be simplified a bit for newcomers

One of the keys to machine learning is (to come full circle) advanced math of the calculus/linear algebra order. That doesn’t mean that you’re going to have to multiply 9 x 9 matrices by hand (perish the thought), but there is a lot of math underlying machine learning, and at least an understanding of advanced mathematical concepts is mandatory for someone who wishes to become a data scientist.

How Can I Make Learning Data Science Easier?

There are ways to make data science easier to learn, especially if your plan for learning the subject is buying the O’Reilly Essential Math for Data Science and working your way through its 349 pages on your own. That’s unquestionably doing things the hard way, especially as there are resources out there to help you learn what you need to learn.

The first thing that will lighten your burden is that you can learn a lot of data science without all the math that you get in college. You don’t absolutely need Calculus III and linear algebra (although they do come in handy down the road) as long as you have an understanding of the concepts that underlie those two fields. Thus, you might not quite need all those 349 pages of essential math before you even get started.

The other thing you can do is get someone to help you, even if it’s just a canned talking head in one of the many free video tutorials that are to be found on YouTube. There’s no guarantee that these are up-to-date or even reliable: anyone can make a how-to video and post it on YouTube; some may teach you a lot, but some may be like the video that tells you to combine frozen limeade concentrate with angel food cake mix and put the resulting slop into the oven. In addition to getting a feel for on-demand online classes, you’ll learn at least something, but don’t expect to learn something as complex as data science from five-year-old videos that promise to do the job in five minutes.

You’ll do better to consider a paid online on-demand course from Udemy, Coursera, or one of their competitors. These courses are curated and are, therefore, more reliable than what you can find on YouTube. They also cost money, although Udemy and Coursera offer seven-day free trials, which enable you to sample their wares. That should give you a chance to see whether on-demand training is for you, and whether you like the instructors. True, you don’t have any direct contact with the teacher in an on-demand class, but you still have to like the person you’re going to have talking at you for something like 30 hours. If you want to draw mustaches and devil’s horns on your instructor on your screen (using the old Winky Dink Magic Drawing Screen you found in your grandparents’ attic), you probably shouldn’t sign up for that particular class. But, especially for people who have substantial time commitments during the day, the on-demand class may be your only way to learn data science.

Best Ways to Learn Data Science Without Difficulty

You can also learn data science with a live teacher in a live class, and do it without leaving the comfort of your home. This is the world of the live online class, and it represents probably the most effective means for preparing you for a career in data science. A live online class is very much like the kind of classes you used to attend in school: in a room with the teacher and other students. The novelty is that, in a live online class, you all don’t share the same spatial coordinates. You connect to the class via a teleconferencing platform such as Zoom from a comfortable chair situated in a quiet room of your choosing. With this type of class, you’ll be able to ask your instructor questions by raising your hand, either at the press of a button or just by raising it. You’ll also be able to share your screen with the instructor, who can thus come to your rescue when you find yourself sinking in digital quicksand.

Noble Desktop offers an extended selection of data science classes, including a Python for Data Science Bootcamp and a more advanced Python Machine Learning Bootcamp. The former should suit beginners, while the latter will be of use to people who already know Python, but who want to move on to its AI and data science applications.

If you’re interested in receiving a complete education in data science that is suited to beginners, Noble Desktop also offers certificate programs that take over a month to complete if taken in their intense full-time mode, or closer to six months if you go the part-time evening classes route. There are three from which you’ll be able to choose: the Data Analytics Certificate, the Data Science Certificate, and, adding a twist to spice up the more usual cocktail, a FinTech Bootcamp that includes both a Python for Finance Bootcamp and a Financial Modeling Bootcamp. Noble Desktop includes 1-to-1 mentoring sessions with its certificate programs that may be used for whatever you require at the time, be it assistance with classroom matters or help putting together your job search portfolio.