How do Beginners Learn Data Science?

When you consider the matter from the broadest of viewpoints, data science is just a kind of statistics, while statistics is just a branch of mathematics. Without math and without statistics, there would be no data science. There are other skills you need to know in order to be a data scientist, but the foundation was laid with these two highly numerical fields. You can, arguably, get by as a data science professional without too much math (the assumption being that the computer can manipulate matrices, arrays, and other aspects of linear algebra better than you ever could), but a sound knowledge of statistics and statistical computation is an inevitable requirement if you want to get anywhere with data science. You’ll have to learn Python or an equivalent computer language that’s capable of executing the kinds of monstrous calculations data science involves, but, since everyone needs to begin somewhere, you should first learn to distinguish a mean from a median and standard deviation from standard diffusion. 

Getting Started with Data Science

While calculus and linear algebra can help you understand the kinds of calculations that are necessary to construct and analyze a data model, those advanced branches of math are, in the data science world, put at the disposal of statistical number-crunching. You’re not just multiplying matrices for the sport of it; you’re doing that kind of computational heavy lifting because you want to solve a statistical problem. On the other hand, computers can multiply matrices far more quickly than a human can, so people have argued that you don’t need all that much linear algebra for data science after all.

On the other hand, you can’t wriggle your way out of the need to understand statistics as easily as you can avoid the math requirement. You will need to know how to calculate fundamental statistical quantities such as means and standard deviations, but you’ll also need to have a solid grasp on how probability works and is calculated, as, in the last analysis, all data science models of future performance are based on probability, which makes it possible to predict whether the market can bear another Star Wars series. That means knowing your way around in descriptive statistics, and maybe getting your feet wet with Bayesian statistics, a branch of probability theory that can be used to pinpoint a target audience, or which products Amazon should recommend to customers who buy memory foam chair cushions.

In addition to the math and the statistics, you should also have a code editor at your disposal and know how to use it, as you will soon be programming in Python (or, less likely, R), and you need an environment in which to practice. A knowledge of Excel and the ways it can be used for data science won’t hurt you, either. 

The First Steps when Learning Data Science

Once you’re past the foundation-laying in statistics, your data science studies will most likely proceed to teach you introductory Python, a computer language invented by Guido van Rossum that first hit the market in 1991 and has mushroomed in popularity ever since. (For the record, the language isn’t named after those big snakes that allegedly have detachable jaws, but, instead, after the highly zany British sketch comedy troupe, Monty Python, of Holy Grail and “Spanish Inquisition” fame.)

Python is an all-purpose computer language that has one of its happiest applications in the world of data science, as it’s capable of doing some pretty intense calculations, especially when coupled with its NumPy library. In addition to NumPy, you’ll learn how to use the pandas library (no relation to Ailuropoda melanoleuca), which is especially useful when dealing with vast and unruly datasets. The third Python library you’re going to get to know is Matplotlib, which makes possible any number of data visualizations, i.e., the tables, charts, and graphs that make your findings as a data scientist intelligible to the people who hired you to predict the future in the first place.

(Do be aware that there are some courses that don’t teach Python, but, rather, teach another computer language, R. Unlike Python, R isn’t a multi-purpose language, but, rather, was designed specifically with statistical computation in mind. R comes with its own share of libraries; unlike Python, which was designed to have a syntax that speakers of natural (human) languages would be able to grasp quickly, R’s syntax is a bit tricky, which is probably the reason why it is not taught as frequently as Python.)

After you’ve learned to deploy a computer language that can be used for data science, you’ll need another computer language (albeit an easier one) that can be used to query databases and bring forth the data you need. The most frequently employed of these is SQL, Structured Query Language, which is the tool par excellence for (as the name suggests) querying structured datasets. (Structured data are the kind that come pre-broken down into fields such as names, addresses, and order histories. Unstructured data are random bits of information, sometimes an overwhelming quantity of them, that can be anything from text to audio to video files, and which require “cleaning” in order to be of any use. Cleaning data involves an entire suite of tools of its own, with names like RingLead, Informatica, and Xplenty, although you’ll get to those later on in your data science education; you should concentrate on mastering SQL first.)

These skills, especially your ability to work with Python, will help set the stage for the next phase of your data science education, the application of machine learning techniques for data science. Machine learning will allow you to process in short measure as much data as a data scientist can perform manually in an entire lifetime.

Free Data Science Tools for Beginners

If you’re curious about data science and want to test the waters before deciding that it’s the career path you wish to follow for the rest of your life, there are ways of doing so without having to fork over any of your hard-earned money. Your first destination will probably be YouTube, which offers a motley collection of videos, long and short, good and not-so-good, and up-to-date and past their sell-by dates. There’s one video that claims it will explain the entire field to you in five minutes, and others that claim they will teach you everything you need to know in just a matter of hours. These probably can’t live up to their promises (there’s just too much to learn about data science to squeeze it even into a 20-hour video tutorial), but they will get you started and give you a taste of what’s in store for you when you sign up for a course from a more dependable school. When watching YouTube videos, never forget that you can proclaim yourself a data science expert and post content even if you don’t have the slightest idea what you’re saying or doing.

Another free resource of which you can avail yourself is a seven-day free trial of one of the two best-known platforms for self-paced learning, Udemy and Coursera. You do need to surrender your credit card details to start the free trials, and be warned that the annual subscription fees are substantial. Udemy does offer the broader range of classes (in addition to serious professional training, the platform offers classes in reading tarot cards and decluttering your space.) Seven days poking around Coursera or Udemy is a good way to learn something about data science, and maybe get some good advice on how to get your dog to stop barking as well.

Several IT schools that offer programs in data science offer complimentary appetizers to give you a taste of what their paid classes are like. These are generally introductory classes, and offer you a chance to decide whether data science (or the school) is for you. The Get Started in Data Science webinar on offer from Noble Desktop is an excellent example of the breed, and it may well whet your appetite to stick around and order a main course.

Live Data Science Training for Beginners

Once you’ve given data science its due due diligence, you can proceed to a live course. Beginner-friendly courses are plentiful, and presuppose no great knowledge on your part, beyond the ability to use a computer and enthusiasm for a new topic that’s actually pretty interesting.

Your best bet for learning data science is going to be a live class, one that has a live teacher who’ll be able to answer any questions you might have as the class unfolds. The current realities of the IT school market include a shift almost entirely toward online classes as opposed to live in-person ones, although, in the very largest markets, you may still be able to find classes to which you can commute and sit in the same room as your instructor.

You needn’t have any qualms about enrolling in a live online course, even if you have no experience with the format. It’s rapidly becoming the modality of choice for continuing adult education. True, it has proven to be less than effective as a means of schooling children, but children go to school for a lot of other reasons besides learning stuff. Adults don’t need to socialize over their Lunchables while at school, and can concentrate on studying the topic for which they’ve signed up. Having made the leap to continuing training online, you’ll be able to realize that the teaching paradigm, far from measuring up unfavorably from the in-person class, offers several advantages over the traditional one-room schoolhouse.

First and probably foremost, you’re spared having to commute to school. Time is a valuable commodity, and trying to plow your way through rush hour traffic to get to school can be a nuisance, to say the very least. In fact, a live online class means you get to study wherever you want, which preferably will be a room with a lock on the door in which you can do everything from controlling the air conditioning to selecting a comfortable chair to wearing something you might not put on to go to a live classroom situation. Just steer clear of pajamas: there is such a thing as getting too comfortable for your own good.)

Having settled on the teaching modality, you have next to find a class that suits you. Good topics for absolute beginners can include one in basic Python, one in the fundamentals of statistics, or even one that gets you started learning how to write SQL queries. Examples out of the Noble Desktop catalog that are open to beginners include a Python for Data Science Bootcamp and an SQL Bootcamp. Both are good places to start a data science education without too large an outlay of funds or time commitment.

The Next Step

Once you’ve conquered statistics, Python for data science with NumPy, pandas and Matplotlib, and SQL, the next big chunk of work you’re going to have to do in order to qualify for data science jobs is a deep dive into the world of machine learning. Without this aspect of artificial intelligence (AI), many of today’s data science projects would be impossible. Machine learning is used to clean messy, unstructured data by eliminating outliers, duplicate rows, and null values in one automatic swoop. Since there’s no way to proceed with big data until it’s been tidied up, machine learning can do it in a comparative minimum of time to what could be achieved by human beings searching through thousands upon thousands of records to identify inconsistencies.

Machine learning’s role in data science doesn’t end with data cleaning: it can also be used to create the predictive models that constitute the end use of most data. You can design the type of model you want by selecting a machine learning algorithm and putting it to work on your sparking clean data, and the algorithm will analyze the data and answer whatever the initial question was. . Machine learning does most of the interpretive heavy lifting, although the data scientist is still required to fine-tune the machine learning model.

If you’re interested in taking a class in machine learning, Noble Desktop’s Python Machine Learning Bootcamp will introduce you to this genuinely fascinating topic, assuming you’re already familiar with Python, NumPy, and pandas. Noble Desktop can provide you with a complete data science education, enough to qualify you for an entry-level position in the field, through one of its two data science certificate programs. The Data Science Certificate program is a detailed traversal of much of the terrain discussed in this article. Even more in-depth is the Data Analytics Certificate, which comprises all the modules that make up the Data Science Certificate, and adds instruction in how to use Excel for data science and training in how to use Tableau, business intelligence (BI) software that can help with data visualizations on a grander scale than Matplotlib can accommodate.