The demand for data scientists has steadily grown over the past few decades. Data scientists are continually sought after by businesses, governments, and non-governmental organizations.

Businesses and organizations have shifted from making speculative and intuition-based decisions to using data to inform their decisions. Every decision must now be backed by sufficient data. 

The availability of massive data has led to an increased demand for data scientists. The application of data science and analytics is now cemented across all major industries.  

Having a career in data science can be very fulfilling. The rewards are enormous. However, a career in data science is not always linear. Many aspiring data scientists end up making some mistakes that wreck their budding careers. Here are the mistakes to avoid when starting your career in data science.  

6 Mistakes to Avoid When Starting Your Career in Data Science

There are 6 major mistakes that you must avoid when starting your data science career. We hope that this article will help you avoid these mistakes and help you grow your data science career!

Dismissing Basic Analytical Skills

Many aspiring data scientists focus on model development and overlook basic analytical skills. They restrain the scope of data science to model development alone. 

Data science is not only about model development and deployment. In fact, a huge part of data science is data exploration and analysis. This is the very first step before modeling. 

Basic analytical and data exploratory skills are just as essential as modeling in data science. Spending more time to understand the data helps you gain extensive insights into the data and the possible outcome of the model.

Data science is not only about model development. You must be cognizant of the basics of data science. Exploring the data reveals a lot to you, therefore, facilitating good decision-making while developing the model.

Too Much Focus on Theoretical Knowledge

This is among the most common mistakes aspiring data scientists make. Most people spend way too much time on theory yet data science is an applied field. 

Theory only helps you to understand the concept. You must go beyond theory to become a professional. You must prove that they learned the material and demonstrate that you can apply the basic concepts of data science to solve real problems. 

There is nothing wrong with theoretical knowledge. In fact, it forms the basis for your data science career. However, too much focus on theory only slows your progress. 

It also won’t help you really understand the concepts in depth. You must practice the concept to understand it well. Finally, too much focus on theoretical knowledge is bound to demotivate you as you start. 

Taking a Bootcamp and competitions that are focused on applying theoretical knowledge is one way to get started. 

Bootcamps provide you with the experience and technical know-how required to solve real problems. 

The point is to go beyond theoretical knowledge and gain experience.

Going Ahead of Yourself and Advancing Too Quickly

This is among the top mistakes to avoid when starting your career in data science. 

Most aspiring data scientists and junior data scientists enter the field after watching a YouTube video. They, therefore, enter the field to build the technology of the future: Robots for Amazon, Self-driving cars for Tesla, and so on. 

There is nothing wrong with this enthusiasm, but you need to curb your enthusiasm and take baby steps. Just as a house is built brick by brick, so is a career in data science – in a gradual manner.

Ensure you fully understand the basics of data science before you go to advanced techniques such as Natural Language Processing and deep learning.  

Some concepts are built on others while some are highly related. Ensure that the learning process is gradual and that you master the fundamentals before jumping to the deep end. 

Start slow and consistently add to your knowledge. This ensures that you learn everything there is to know in an organized manner. You will also not be demotivated midway through your learning process. 

Failing to Understand Data Visualization and Reporting

Most aspiring data scientists immediately jump to modeling and predictive analysis before understanding the basics of data science. Yet modeling is only 10% or thereabout in the data science life cycle. 

Data visualization and result reporting is a very crucial skill for you as a data scientist and analyst. Not everyone is a techie as you are, and not everyone will understand your model. 

You therefore must find a simple way to communicate and report the expected results of your model. Data visualization helps you to accomplish just that – to simply communicate your work in a non-techie, non-code manner. 

Data visualization also helps you as a data scientist. It helps you to understand your data and uncover patterns in the data. It also sets the foundation for model development and feature engineering. 

Lack of Specialization and Focus. 

Most budding data scientists dismiss the need to focus and settle on a niche. They go all in and forget to specialize. 

Data science is a wide field with a wide range of applications. Specialization is important in such a field.  

For one, not all data science jobs are the same, and over the lifespan of your career, you will find different jobs that require different skills. Specialization helps to set you apart and establish you as a specialist in a particular field. 

There are many fields within data science that you can specialize in. You could become a data engineer, a data analyst, or even a machine learning and artificial intelligence expert. Whatever you choose, just ensure you master the craft and become a revered expert. 

Neglecting Communication and Soft Skills

This is among the greatest mistakes to avoid when starting your career in data science. Soft skills are important in every career. The ability to communicate well and explain your thoughts and ideas is always a plus for you regardless of your career. 

However, most data scientists are code and algorithm oriented. They forget that they are humans existing in a community. 

Being a data scientist means that you will regularly present your findings to the rest of your non-techie colleagues and management team. You, therefore, need to find a way to explain the results of your work to these people.

You will most likely always present your work to a business investor or a major stakeholder. Such people are rarely technical. They only listen to what matters to them. 

It is at this point that communication skills come in handy. Good communication and relational skills will set you aside and prove you to be an asset in any team. Such skills also help ace interviews and grow corporate connections. 

Wrap Up

Data science is undoubtedly an industry filled with prospects. Building a career in data science is the right step going forward. 

There are many other things you can do to increase the chances of your data science career kicking off on the right foot. That would be anything from honing your data science skills to having good soft and relational skills.  

Data science can be a little bit tricky, especially when just starting your career. We hope our list of mistakes to avoid when starting your career in data science makes things easier for you and gives you a head start in your career.  

Are you looking to hone your data science skills? We have a wide range of certified courses including data science that will help you build your career and establish yourself as a professional. Check out our courses section or get in touch with us to get started.