8 Common Mistakes Amateur Data Scientists Are Always Doing

Data Science is one of the most popular and fastest-growing industries in the digital universe. With so much information created on a daily basis, businesses get huge volumes of learning sources, but it’s not always easy to extrapolate valid conclusions from seemingly unrelated datasets.

This is where data science steps in to help companies grow. By definition, data science is the field of study that combines domain expertise, programming skills, and knowledge of math and statistics to extract meaningful insights from data.

If it sounds complicated, well that’s because it is. Not everyone can handle the task successfully, which is why the average salary for a data scientist in the US reaches $117 thousand per year. However, highly skilled professionals in this field can earn much more.

On the other side, beginner-level data scientists are prone to making errors. They don’t have enough knowledge and experience, which often leads to confusion and misconceptions. In this post, we will show you eight common mistakes amateur data scientists are always doing.

Too Much Focus on Theory

The first mistake on the list is also the most common. Content creators at Brill Assignment always tell their interns that the most important thing is to start writing, while theory is going to fit in later. The same goes for data science amateurs – they believe they must learn a lot of theory before engaging in practical work.

They learn algorithms, algebra, and derivations without testing and putting them into practice. This is not good because you won’t remember or use most of the things you see in the textbook. Therefore, you should mix theory and practice – that is the best learning model.

Learning Multiple Tools Simultaneously

The process of becoming a data science master forces you to use a wide range of tools, but it doesn’t mean you have to focus on all of them at the same time. As a matter of fact, it is much more productive to focus on one tool at a time to perfect your knowledge relatively quickly. After all, the goal is not to learn something about everything – the goal is to learn everything about something.

Focusing on Accuracy Instead of Understanding

When you build a predictive model, you definitely need to take care of accuracy. However, we don’t recommend you focusing too much on accuracy over figuring out how exactly the model functions. As a data scientist, you also need to provide clients with a meaningful explanation of your model, so it’s critical to understand each part of the process and present it briefly and clearly.

Not Imputing Missing Values

Most data science professionals deal with incomplete datasets and need to find a way to overcome the issue. Beginners are not an exception here, but they make a mistake by excluding observations that lack specific values. What makes it a mistake?

Well, if you eliminate too many observations just because they lack one value, you are jeopardizing the whole project. A dataset will be scarce and irrelevant, so you better impute missing values on your own. There are many ways to do it, including mean imputation, matching methods, and regression imputation, but you have to figure it out on your own in order to design a reliable predictive model.

Lack of Consistency

You don’t become a data science guru by working three days a week. We notice a lot of young guns feeling overoptimistic about their learning potential, but most of them end up losing pace and disappearing from the professional scene. You need to practice every day for at least a couple of hours to maintain consistency. Create a calendar of activities and do your best to meet the targeted objectives – it’s the only way to turn pro.

Dream of Academic Titles

Data science is a relatively new field of work, which means the first college courses appeared quite recently. In such circumstances, most employers are looking for individuals with practical knowledge and experience, while academic titles come second. The moral of this story is that you don’t have to strive for degrees and diplomas at all costs, but rather try to gain real-life experience and work on concrete projects.

Neglect Communication Skills

The worst thing a data scientist can do is concentrating on work exclusively and neglecting the so-called soft skills. We already mentioned that you need to present your ideas to the clients, but it’s impossible if you don’t understand how to approach people and explain complex concepts in a way that sounds credible and convincing. Don’t be afraid of meetings and public speeches – it is all part of the game that makes you a better data science professional.

Use Professional Jargon in the Resume

The last mistake on our list can easily turn out to be fatal for a young data scientist’s career. You don’t want to use too much of professional jargon in the resume because it will suffocate the recruiters. Instead, focus on specific achievements and show how your skills can contribute to the organization. If you don’t know how to do it single-handedly, we strongly suggest you consult with resume writing experts.

Conclusion

Being a data scientist is not easy, particularly if you are still learning the industry fundamentals. Over the course of the last few years, we noticed that beginner-level business intelligence professionals make some very nasty errors.

This is why we made a list of the eight most common mistakes amateur data scientists have been making regularly. If you are one of those young enthusiasts, keep our suggestions in mind and make sure to avoid them – it will make you a much better data scientist.

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