Common Mistakes People Make In Starting Career in Data Science

Fatal Mistakes People Make In Starting Career In Data Science:

Blog Authors | Board Infinity

So, you’ve decided data science is the field for you & learning data science can be intimidating. Especially so, when you are just starting your journey. More and more businesses are becoming data-driven, the world is increasingly becoming more connected and it looks like every business will need data science practice. So, the demand for data jobs is huge and ever-increasing.

In this blog, we will be going through some fatal data science mistakes which people make while starting their rewarding careers in data science. If you are not careful enough these mistakes can even eat away at your most valuable resources, which are your time, energy, and motivation. We assure you to save your days, weeks, or even months of frustration by avoiding these costly mistakes. So, let’s dive into our content quickly!

Common Mistakes People Make In Starting Their Data Science Career

Let's cover 7 such common data science mistakes made by people when they start their careers in data science. We hope with this blog, you learn from these mistakes and stop making them yourself. Without further ado, let’s start!

1. Start with a Lot of Enthusiasm and Get Jaded Soon

Most people or beginners start their careers with a lot of enthusiasm. While Data Science is a buzzing field, it is also highly competitive and requires interdisciplinary skills. As a result, a lot of data science professionals are finding it hard to hold on to their data science learning journey.

Anxiety is normal in this field because there is a huge difference between what you learn as a student and what you learn as a professional. Therefore, instead of getting worried, focus on learning and working as much as you can, because, in the long run, your experience will act as a catalyst in driving your career. And how to tackle this? Be a part of Data Science Communities! This is one of the best things to do when you are low on motivation. When you enter this buzzing field, you realize how much people love to discuss ideas and how enthusiastic they are when it comes to sharing knowledge.

Remember that this time at the beginning of your educational journey is absolutely crucial to your long-term success. The actions that you take in these first few weeks will determine what habits you set for yourself, and as a result, will determine how you will deal with these situations throughout your journey.

2. Spending Too Much Time on Theory

Many beginners fall into the trap of spending too much time on theory, whether it be math related to linear algebra, statistics, etc., or any machine learning-related stuff like algorithms, derivations, etc. Data science is an applied field, and the best way to solidify skills is by practicing! There’s a greater risk that you’ll become demotivated and give up if you don’t see what you’re learning connects to the real world.

These theoretical approaches are traditionally taught in academia, but most practitioners can benefit from a more results-oriented mindset. To tackle this, balance your studies with projects that provide you with hands-on practice. Learn to be comfortable with partial knowledge, because there will be situations where you may not understand everything, but this may link to your knowledge in the future. Have patience, you’ll naturally fill in the gaps as you progress and finally Learn how each piece fits into the big picture. That way you will not only grasp the theoretical knowledge but also, would proficiently apply that theory in practical scenarios!

3. Heading Straight for Machine Learning or Deep learning Techniques without Learning the Prerequisites

The majority of folks who want to become data scientists/data analysts are inspired by videos of robots, awesome predictive models, and in some cases even the high salaries. But to be frank, there is a long road you need to travel before you reach there. Just as a house is built brick-by-brick, a data job responsibility is also the sum of all the individual parts.

You should get to know the basics, how techniques work before you apply them to a problem. Learning this will help you understand how an algorithm works, what you can do to fine-tune it, and will also help you build on existing techniques. Mathematics plays an important role here so it’s always helpful to know certain concepts. However, it’s important to master the fundamentals. Every Olympic diver needed to learn how to swim first, and so should you.

4. Not Building Strong Data Visualization Skills from the Beginning

Most of the people who start learning data science jump towards building models and making predictions. But one should understand that building a model just covers around 10% of the whole data science life cycle. After all, irrespective of any role, may it be a data analyst or a data scientist, each and every one needs to deal with data. And building strong data visualization skills is a key factor that every person in the data science domain should possess.

It helps to build an intuitive understanding and uncover patterns from the data. This intuitive understanding sets the foundation for better feature engineering, model development, and feature selection eventually. Well, how to build this skill?

  1. Learn the basics of data story-telling and building impactful dashboards.
  2. Practice the skills in some sample data to get better at it.
  3. Use these skills on a real-world project and share them for feedback.

There’s much more to learn beyond plotting a graph using matplotlib or seaborn, and most people stop their learning curve there.

5. Relying Solely on Certifications

Ever since data science became ultra-popular, certifications and degrees have cropped up just about everywhere. There are too many of these courses online being poured over and completed by thousands upon thousands of data aspirants! But to be frank, hiring managers do not care much for these pieces of paper – they place far more emphasis on your knowledge, and how you’ve applied it in real-life practical situations.

In simple words, certifications definitely matter but not because of the certificate itself but because of the skills you have gained as part of the certification. In the end, it all boils down to the interview process. The interviewer will test everything that you have mentioned in your skillset. Therefore, if you choose to go ahead with a data science certification, make sure that you keep up with your classes and gain the right skills. Your certificate won’t get you the job, skills will.

6. Getting a Proper Guidance

Data Science and machine learning, data engineering, and relatively a very new field and so are its alumni. There are only a few people who have decrypted their path in this field. There are many ways to enter data science, the simplest one is to cough up lakhs of rupees for a recognized certification only to later get frustrated with the recorded videos or even follow along with a YouTube playlist but you are still not an industry-ready professional!

Find a mentor who has navigated his career in the field of data science and ask them how they did it, how they decrypted the process of getting the job they aimed for, what are the resources they were offered, etc. Now, coming towards the final part, which gets neglected most of the time is.

7. Neglecting Communications Skills

Being a data scientist in an organization, presenting your findings to a business stakeholder is part of your job, and being able to make the shift from a technical talk to highlighting a business value expressed in human terms is extremely valuable. You’ll most likely always present your work to a business sponsor. These people are not technical and they never will be on your team. They only listen to what matters to them.

And this is where your communication skills come into the picture. These skills would not only help you grow your corporate connections, but also will help you crack any of your interviews. After all, communication is the most underrated skill one would have.


Well, coming to the conclusion, the demand for data science is huge and employers are investing significant time and money in Data Scientists. So, taking the right steps will lead to exponential growth. This is most definitely not a complete list, there are plenty of other data science mistakes that data aspirants tend to make. But these were the most common ones we have seen.

If you want to learn more about Data Science, enroll in Board Infinity's Data Science Learning Path. Get access to premium content, personalized 1:1 mentoring from industry experts in the field of Data Science, complete placement assistance, certification, and a lot more. Get started on your Data Science journey now!

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Blog Authors | Board Infinity

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