9 Beginner Mistakes to Avoid When Starting Your Data Science Career

9 Beginner Mistakes to Avoid When Starting Your Data Science Career

21 November 2022

Studying a data science career is an exciting field, and the majority of the student will adore their university journey. However, starting such a critical phase of life can be stressful for any data science professional. It’s because the specific time of studying is over, and now it is the time to get into the real world and face reality. The data scientist job is fantastic and exciting, but there are a few mistakes that almost every data scientist will make.

Thus, beginners must avoid these mistakes; otherwise, it will drain their energy, time, and valuable resources. In this data science career guide, we compiled nine significant mistakes you should avoid.

Mistakes to Avoid While Starting Data Science Career

Let’s consider some of the top nine mistakes to avoid and learn from them to grow in the career.

  1. Do Not Dig Into the Theory

Many beginners only spend their time learning the theory rather than practicing practical problems. However, learning basic theory is essential but only dig deep into it for a short time. Instead, try to resolve practical issues; however, this experimental aspect will help you appreciate the knowledge you have obtained through theory.

However, learning too much theory also leads to feeling demotivated, and you might end up giving up data science. Learn both aspects simultaneously instead of all theory at once and slowly move to practical. Maintaining a balance between theory and practice is crucial, and this way, you will master it.

  1. Not Building Strong Data Visualization Skills from Beginning

Most learners who start learning data science will directly move towards preparing models and making assumptions. But one thing is sure the creation of a model covers 10% of an entire data science life cycle. Irrespective of any designation in the company, every person related to the role has to deal with data management. Building robust data visualization skills is a key factor that each person in the professional data domain should hold.

  1. Relying Solely on Degree

Obtaining a degree is crucial to initiating a career in data science. But, various people will deny further education and think that degree is more than enough to start their career. But, it is recommended to bring data science certification after completing a degree program. The accreditation will further help you improve your knowledge and practice your applied skills. You have a much stronger CV, making you the perfect data scientist.

  1. Giving Too Much Importance on Tools and Libraries

In the initial stage of the data science career, giving too much importance to the tools and libraries is not a good idea. However, the shiny object may be aesthetic but will only suit you if used correctly. Thus, before buying any fancy machine learning algorithms, you must learn how they work. Also, it allows you to understand how libraries and tools will work before writing the first line of code.

  1. Never Neglect Domain Knowledge 

You must be familiar with the company’s structure as a data scientist. As a data scientist, you need to understand and be able to comprehend the organization’s essential functions. Bank finance is an example of this. This will add value to your knowledge in the interview.

  1. Focus on Accuracy Rather Than Performance

This is a mistake that we all made at one time or another in our professional lives. While accuracy is essential, there are other factors in a good model. Your solution accuracy will depend on your chosen algorithm, the data you use, and the parameters you set. The accuracy of your results will be affected if you change any of these factors. Focus more on understanding your data, and accuracy will follow.

  1. Ignoring Communication Skills

The data scientist’s role is to define data mining, algorithms, and data analysis, but communication is the hidden role in the data scientist field. The data scientist professional will convey their insights and pain points to stakeholders. In this part communication, is comes into play.

This skill will not enable you to grow your corporate connections but also allows you to crack the interviews.

  1. Moving Directly to Advanced Topics

In the initial phase of data science, data scientist feels guilty about moving directly toward complex problems. It can lead you to work extra hours on the job and try to figure out things in the new organization. Instead of this, start the journey with basics until you become familiar with the organization’s work culture. This way, you can slowly build up your career.

  1. Do not Narrow Your Search 

If you are looking for a job that offers a data scientist designation and are not ready to compromise on any role, then don’t. Many organizations are currently actively hiring data scientists so that you may find your preferred role promptly. But, do not limit yourself to one opening, and always be ready to take up another job role. However, the designation may be pretty different, but the job and skill will enable you to develop other skills.


Data science is much more than a degree and working in a company. Thus, it is always better to start slowly to achieve better results in the long run. Never burden yourself to prove your skills and knowledge. Instead of this, spend time learning skills and making a better version of you.

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