Data science is a rapidly expanding field with a growing number of specializations and roles to fill. There are more courses, books, and articles aimed at helping data scientists learn the skills they need to be good at their jobs as a result of the boom (or to get a job). There is so much to discover. So, how do you figure out where you should go next?
Starting with the fundamentals may appear to be a pointless exercise. However, I was only at the start of my data science journey. I understand that deep learning appears to be very cool and enticing. But that isn’t the place to begin. Before you attempt to learn anything crazy, make sure you have a firm grasp on basic math, statistics, and computer programming.
Once you’ve mastered the fundamental concepts that make up data science, you have a plethora of options for what to learn next. There is no one-size-fits-all solution, and each learning path has advantages and disadvantages.
Option 1: Concentrate Your Education on What’s New
Focusing on staying current with the state of the art in your chosen field is one way to organize your learning. This can be accomplished by reading and learning how to apply new research papers as they become available. This is one of the more difficult ways to learn for many people because it requires you to read and understand research at a deep enough level to be able to translate it to code.
Focusing on cutting-edge research has the advantage of keeping you up to date on the best known solutions to a variety of difficult problems. It allows you to work on exciting projects and create things that few, if any, others are creating. It also prepares you to conduct your own research if that is something you are interested in.
To begin with, reading research papers is difficult. It takes time to read through a research paper and comprehend everything the authors did. It’s also difficult to determine how applicable the research is to the problems you’re working on. Many people, I have discovered, do not enjoy learning in this manner, and if you do not enjoy learning, you will not take the time to do so.
Option 2: Concentrate on a specific area of interest
You can simply dive into a particular area of data science that interests you, such as natural language processing, computer vision, explainable machine learning, or compute-efficient deep learning. Being a specialist in something that interests you is a great way to approach learning, and it makes it relatively easy to figure out what you should learn.
The benefit of learning this way is that you are only studying what you are truly passionate about. It is also easier to become an expert on a subject matter if you concentrate your studies in one area, making you a valuable asset.
The benefit of becoming a specialist is counterbalanced by the disadvantage of becoming a specialist. Concentrating all of your education in one area can pigeonhole you into a job with limited flexibility. You might miss out on some other important skills that a more broad approach could provide.
Option 3: Acquire knowledge in order to complete a task or project
Another great way to decide what to learn is to concentrate on learning things that will be most useful to whatever project you are currently working on. You can begin studying time series modeling if you are working on a project that would benefit from it. Once you’ve completed that project, you’ll need to figure out what topic will be most useful for your next one.
This method of learning allows you to have a purpose in what you’re learning. It provides you with a clear sense of direction and motivation for your study time. Furthermore, the majority of what you learn has a clear and immediate payoff.
I’ve discovered that jumping from topic to topic leaves some knowledge gaps. Learning how to do image classification on the fly, for example, may cause you to overlook the concept of convolutions. Sometimes these holes are minor, but other times they are significant holes that you will need to fill in later.
Option 4: Adhere to a Program of Study
This could be anything from a college diploma to an online course or a book. Any structured program that lays out what needs to be learned and provides the materials needed to do so.
The convenience is one of the biggest advantages. You’ll be told what’s important to know and given everything you’ll need to master those skills. The majority of the time, you will be given examples and exercises to help you gain practical experience.
Regrettably, not every course is created equal. If you go this route, make sure the place you’re getting your courses from has a good reputation and will provide quality content. Another disadvantage of this path is that it is usually the most expensive. You may also discover that the skills and techniques you learn in the classroom are not always easily transferable to the real world.
Option 5: Increase the value of what you offer
This method of deciding what to learn could be applied to any of the others. It is the notion that you are learning the skills that will make you the most valuable in your career. If your company (or a company you want to work for) values full-stack data scientists, a statistician might concentrate on dev ops learning.
Learning with the goal of increasing your value will almost always result in the most significant advancements in your career. It can also open up a slew of new opportunities for fun.
You may find yourself studying topics that you are not interested in. This makes learning and mastering new material difficult, and it discourages students from continuing to learn.
Here a list of free Courses Data Science Courses offered from Coursera.
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