Data science is vast. It is easier to be confused when there’s so much information and no clear guideline to follow. For those, who are looking to embark on a data science career, the path could be intimidating. There are so much to figure out—which language to learn? Which statistical techniques to focus on? Is machine learning necessary and other similar questions.
This guide will help you answer some of these questions and help you build a learning framework for yourself. You can expect answer to some questions which clear the confusion related to data science career.
Overview of data science industry
Even though data science came recently, its adoption has grown phenomenally in the industry. Contrary to the popular belief, there are multiple roles in it—data scientist, machine learning engineer, data engineer, statistician, data visualizer etc. Each role requires a specific set of skills. All roles have some overlapping skills, but each one differs in the output delivered. Additionally, each role has some dependencies on others. What’s noteworthy? You need to decide a role and stick to it. You can decide so by looking at the set of skills required to perform well in the chosen role.
Additionally, to figure out which role is suitable for, you can talk to working data science professionals and ask them for help. LinkedIn is a good place to reach out to people. Anybody would help to answer questions.
Take data science certification or course
Next, you need to learn the skills required for your chosen role. Each role has a specific set of skills, but can be challenging to learn and hone. Don’t get deterred by the complexity of concepts, instead focus on being consistent in learning and practicing. Data science is an extremely application oriented role, unless you are practicing and solving problems, getting hang of it will be difficult.
Essentially, you need to focus on application part, and not just theory. Take up as many projects as you can. Open data sets are available on Kaggle, Stack Exchange and various other places online, which you can use to practice concepts. Maths is involved in every technique. It might be difficult to understand the math at first, but stick to it and eventually you will get the hang of it. So just keep practicing.
Further, you can seek help from working data science professionals. They will pin point problem areas and help you develop faster approaches to solve problems.
Choose a technology stack
Data science is about solving problems. At the end of the day, you will be required to solve problem using data. Tools and technologies are means to help you solve problems. So focus on learning effective and faster approaches to solve problems. Tools and technologies will keep coming, don’t limit yourself to one.
Initially, you can start with technologies which you feel comfortable with. For instance, if you are not comfortable with coding, you can start with GUI based tools and learn concepts. Later, you can learn coding.
Improve your communication skills
This might not sound like a big deal, but it is. Communication skills might not be a reason for rejection in a data science role, but it is an extremely important skill. It is essentially required to communicate ideas in meetings and groups effectively.
You don’t have to be an exceptional speaker, which would be good if you are, but learn to convey your ideas clearly.
Data science isn’t as challenging as it is made to be. Yes, it can be a little difficult to learn and master. However, given consistent practice and following a proper learning path will lead you to success.