The demand for Big data professional is overwhelming along with their pay scale. If you are reading this article, you must be thinking of jumping into the Big Data field as it is a lucrative career.
Let’s take a look at the pay scale of a variety of Data Experts.
Image Source: Cognixia
We can see that the pay scale for Big Data professionals is already higher than average people and it is still rising over the period. There are not enough Big Data professionals in the world today that can give powerful insights to the businesses.
It is apparent that you will be playing around with various Big Data tools and to get an understanding about how to use them one must be familiar with the frameworks dealing with data management. One can look upon an Online Hadoop Training course that gives useful insights about data analytics and related tools made best to use.
In your journey, you are also likely to make many mistakes. I cannot list out all the errors in this article, but I will point out the seven common mistakes that hinder the growth of Big Data professionals.
Know in Advance the 7 Common Mistakes that Big Data Professionals Make
It is wiser to learn from others’ mistakes. Doing that will save us a lot of time and money. Without further delay, let’s jump right into seven common mistakes that you need to avoid.
- Thinking that you need to know everything under the sun to get started
There are tons of programming languages and technologies that are available on the market. Trying to understand them all will not get you anywhere. You should apply what you learn as you go. Go out and see what kind of real-world problems you can solve with the things that you’ve discovered.
If you are learning some algorithm, apply it right away. Search for the real world problems where you can use your knowledge. You can join the Kaggle community or some other communities where you can learn from experienced data science professionals.
Your employers are not looking for people who have done nothing at all. They want to hire people who can solve real-world problems. You should be able to solve their business problems and help them make money.
- Neglecting soft skills
There are plenty of technical things that you need to know before you can dive into the world of data science. However, the beginners get so much caught into hard skills that they ignore entirely soft skills.
Image Source: GoSkills
Even if you are the best coder or data science professional in the world, you will not be able to convince your employers or work in a team without soft skills. This study shows that one of the most severe concerns among IT employees is the lack of soft skills.
As a data science professional, your job is not only to analyze data. You need to work in a team and communicate your insights to your team members and seniors. It is a must to convince your colleagues about the importance of your findings. Your soft skills will greatly determine your success.
While learning about various aspects of data science, ensure that you are also working on your soft skills in the process. Some of the soft skills that you should work are public speaking, leadership, teamwork, communication, and so on.
- Being too nerdy
The beginners can get easily overwhelmed with the number of tools that are available on the market for solving business problems. They may want to use as many techniques as possible. However, they miss one huge thing, and, i.e. the actual business problem.
In many cases, we find data professionals focusing too much on knowledge of tools and libraries. Your job is to solve business problems, not invent new software or impress your employee with your understanding of tools.
Keep in mind that there is no such thing as the best tool or programming language. The tool/language depends on various factors like your preference, the type of project, the size of the project, the nature of the problem, and so on.
It would be much beneficial for you is you instead look for data sets in the industry that you want to work to use your knowledge for coming up with solutions.
- Putting little to no emphasis on structuring
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Would you go to a new country as a tourist without a map? Most of you will not do that. When it comes to data science, many analysts try to dive into the problem without structuring the problem. It creates a lot of confusion for the data science employee.
As a data science employee, you must be able to give a framework to unstructured data that you will deal while solving a problem. You will get to know the areas where you need to prioritize. After that, you will select the best tool and technology to solve that problem. However, amateurs usually lack this skill. They try to solve the problem without having a proper road map.
To have structured thinking, you need to learn how to plan and break the massive problem into many small problems. Look around for techniques that will help you structure the problem. Also, working on your logical brain will help a lot to avoid this mistake.
- Trying to learn many things quickly
Every single framework and tool has its unique features. Because of this fact, Big Data professionals tend to learn about every single tool at a quick pace. It will only lead them nowhere. Making this mistake can also negatively impact your ability to solve problems in hand.
To save yourself from making this mistake, stick to one tool at a time. Do not deviate into using many tools. If you are using Python, do not be tempted to learn R-based tools.
- Giving too much emphasis on academic degrees
Having a degree with good marks is a significant boost to your resume. However, it does not mean that your professional life will revolve around your degree. They have expectations that you can give them valuable insights from the collected data.
Instead of only focusing on getting good grades or boasting about your degree, go out and connect with Big data professionals on LinkedIn or other platforms. Try to learn how the real world works by interacting and participating in real-world projects.
- Giving too much priority to model accuracy
Having over 95% model accuracy is excellent. However, it does not mean that you will give too much priority to model accuracy. You need to analyze the projects and identify the features of tools that will solve the problem.
A lesser accurate tool can be more useful for a project if it has unique features that you can use to solve specific problems in that project.
Over to You
There is a problem of information overload today. That is why Big Data is getting a lot of love from corporations. A vast pool of information will not only complicate things for businesses but also professionals who are trying to enter into the real world professions.
Reading an article like this will help you along your journey to becoming a better Big Data professional. I’m not implying that you will be making all seven mistakes right away in your earlier career path.
However, it is likely that you will make some of these mistakes at some point in your career. Knowing about some common errors will drastically reduce your chances of making it and accelerate your growth.
Let me know about your journey in the data science field. If you have made some other mistakes than the mentioned ones, please share your experience in the comment section below. I would love to read your insights.