Can I Learn Data Science in 3 Months?

Of course you can.

And of course you can’t.

The question omits an all-important quantity variable. Given that you can spend eight years of higher study getting a PhD in data science, there’s clearly a whole lot more than you can learn in three months. On the other hand, three months of dedicated study should suffice to teach you enough to take on an entry-level role in data analytics, assuming that you’re willing to continue your education on the job. You can acquire a lot of knowledge that way, and, while you still won’t have earned that PhD, you’ll be able to move onward and upward with your career.

Data science is a highly, highly complex field that only gets more complex as you learn more and more about it: you can go from using machine learning algorithms out of the box to being the person who writes the algorithms. The general understanding is that a master’s degree is required to secure one of the better-paying jobs in the field, and you don’t get an MS in three months. On the other hand, if you’re willing to start out on the data science ladder’s bottommost rungs, three months ought to make you employable.

How Much Data Science Can I Learn in 3 Months 

While some maintain that it’s rude to answer a question with a question, in this case, an answer to what can be done in the time it takes a newborn baby to begin shaking things depends on how much of those three months you’re going to devote to learning something. Full-time study is obviously going to teach you a lot more about data science than you can learn if you only can study part-time.

If you work at it full-time—and full-time means 40 hours in a week in the classroom and at least half that on homework assignments and independent study—you will be able to learn a great deal. As to just how much territory that means you can cover, you need to factor in your personal starting line. Someone who already knows statistics and the basics of Python will be able to get further into machine learning and other advanced topics in 12 weeks than someone who needs to begin by learning the definition of a standard deviation. 

In either case, three months is more than enough time to learn the theory on which data science is based, statistics, Python with NumPy and pandas, and SQL. You’ll then be able to attack the intricacies of machine learning, and have time left over to learn some fancy visualization and dashboard techniques that will allow you to exhibit your crystal ball’s findings to people who have limited data science backgrounds, but who are in a position to act upon what you’ve been able to predict. You won’t become an expert in any of the above disciplines in three months, but you can learn enough to be able to conduct an intelligent conversation on the subject of data science, and be qualified to take on an entry-level role in the field.

As for what you can achieve if you work at data science part-time, you can apply some basic math: if you put in 20 hours a week instead of 40, you’ll be able to cover about half the ground you’d cover in a full-time bootcamp. Three months of part-time work should probably get you through basic Python with NumPy and pandas. That seems to be a fairly standard mid-point to a six-month part-time data science bootcamp, and it leaves you another 12 weeks to cover SQL, machine learning, and, if you’re delving into it, Tableau or other visualization tools.

If you’re working on your own, be it with a self-paced class or by buying the books and learning from those, you’re obviously going to be setting the pace, so, if you apply yourself to your course with something of a vengeance, you may be able to complete the whole thing in three months. You can also set a slower pace and take closer to the six months that seem to be the standard length for a self-paced bootcamp. 

If you’re teaching yourself, the sky’s the limit, and you can zoom through your books using your brain’s hyperspace drive, or take your time and let the new material soak into your brain more slowly, on the theory that you’ll retain more that way. And, of course, as you’ll be setting your own curriculum, you can give more time to some topics (such as linear algebra) that aren’t covered in much depth in bootcamps.

How Can I Learn Data Science More Quickly?

If you wish to accelerate the process of learning data science, your best bet is to select a class that moves at a faster pace than the average bootcamp. Be advised that bootcamps, especially the full-time ones, already go at a substantial clip, so you’ll be choosing to undertake quite a challenge if you sign up for a six-week bootcamp.

Noble Desktop’s two data science certificate programs are beginner-friendly and take under two months to complete. They cover the same ground other bootcamps cover. The Data Analytics Certificate even includes instruction in using Excel for simple data science problems, and employing Tableau for data visualization. The Data Science Certificate program offers a slightly pared-down version of the Data Analytics Certificate program, but includes all the same modules in Python, its libraries, SQL, and machine learning. As it covers a bit less ground, it can be completed in closer to one month if taken full-time.

Another way to speed up the process is not to waste time learning something you already know. You can probably teach yourself statistics (either using free tutorials or buying a textbook on the subject) relatively quickly, meaning that you can start off with a Python for beginners class without having to sit through all the preliminary material covered in some beginner-friendly bootcamps.

If you really want to learn as much as you can in absolutely as little time as possible, you probably can’t beat a self-paced class in which you set a breakneck pace. There’s nothing to stop you from zipping through a self-paced course in a couple of weeks, or to binge-watch the whole thing in a weekend as though a whole new series of Bridgerton just dropped all at once. If you’ve been ordered to understand pandas by Monday morning and it’s already Wednesday, this may be the best option for escaping your unenviable predicament.

What Data Science Skills Will I Need to Learn After 3 Months?

Since a data science curriculum can easily be extended to fill a master’s degree program, you’ll clearly have a lot more to learn after you complete your three-month program. And, while the three-month approach will make you employable, the really interesting salaries in the field go to those with more knowledge and experience. You can, however, learn a great deal on the job, with the bonus that you’ll be adding to your store of experience in the process.

The more advanced topics in data science tend to cluster around machine learning, a highly engaging topic that constitutes the backbone of advanced work in the field. Machine learning is used as a means of “cleaning” unstructured data, i.e., data that don’t come neatly arranged into fields. The definition of data seems to broaden by the day, meaning that more and more things are being seen as fodder for data science, but it needs to be kicked into shape before it can be used, and, here, using a seemingly intelligent machine is far more efficient than having to arrange thousands upon thousands of bits of data manually. Machine learning also lies at the heart of predictive modeling, as, not only can it wrangle data, it can also process them faster than a human can. (Fortunately, a human is still required to write the algorithm that the machine executes.)

With advanced data science training, you’ll be eligible for more than just entry-level data analytics jobs. Actual data science positions in which you’re processing and interpreting big data should be within reach, along with more advanced data analytics roles. Yes, all of that equates to larger salaries. But you do have to be willing to learn and to continue to learn after you’re employed. And be prepared that you may need to go back to school while you’re working: there are limits to what can be learned by pure osmosis. (The good news is that your employer will as likely as not foot the bill for your continuing education.)

While certificate programs and bootcamps can play a significant role in getting you started in data science, you shouldn’t for a minute expect that your education can be completed in three months. You’ll still have a lot to learn. Luckily for you, the field is an absorbing one, and, if you found the first three months of your training to be involving, you should be able to continue enjoying the learning process as you delve ever deeper into the mysteries of data science.