Although data science is a highly lucrative and rewarding field, there are many mistakes aspiring data scientists make that are potentially disastrous. The demand for data scientists is growing day by day. Over the past, the availability of 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. The remuneration for a data scientist is almost twice the median salary average in the US. Data scientists also get to solve complex problems. This is a source of fulfillment for many data scientists. 

However, a career in data science is not always linear. Many aspiring data scientists and even qualified professionals end up making some mistakes that wreck their careers. 

Data Science Mistakes To Avoid In Your Career Plan

We consulted industry leaders to find out some of the mistakes aspiring data scientists make. Here is our list of 4 mistakes aspiring data scientists make that you must avoid. 

Lacking a Strategy

Data science is very demanding. The number of skills that a data scientist must perfect is inexhaustible. Becoming a data science professional requires continuous learning and practice. However, many budding data scientists underestimate the demanding nature of data science.

Lacking a plan is one of the major mistakes aspiring data scientists make. A plan helps you to track your progress. Its absence thereof makes it impossible for you to track your growth and the steps you have made. 

There is too much to learn and practice. You need to have a strategy that details what you want to achieve, how you will achieve it and the time required. Lay out your goals and targets and make sure you track the progress.

Having a plan is not enough. You need to have the discipline to follow your plan. Many aspiring data scientists make the mistake of drafting a plan and failing to follow it to completion. Having the discipline to follow your plan down to every detail will make you fast-track your progress as you build your career.

The final mistake aspiring data scientists make is having an unrealistic plan. Your plan needs to be realistic. Do not be over-ambitious and set goals that are merely achievable.

Your plan also needs to be transitory. In the world of data science, concepts build on other concepts. There is a relationship between the concepts. Data scientists need to be cognizant of this association. Do not start studying models without learning basic exploratory data analysis.

Underestimating The Requisite Time and Commitment

The journey to becoming an expert in anything requires commitment. Data science and its expertise thereof is no different. A lot of commitment is required for you to become a data science expert. Many aspiring data scientists make the mistake of underestimating the commitment required in this field.

Data science is very extensive.  There is an inexhaustible list of concepts and topics to be covered. Data science also goes beyond the comprehension of theoretical concepts. Practice is paramount in data science. 

Data science requires topical knowledge in exploratory data analysis, descriptive statistics, inferential statistics, data visualization, and model deployment. Advanced statistics and mathematics skills are also required in data science. Above that, relational database skills such as SQL and MySQL are very necessary for the field of data science.

Data science requires patience and commitment. It is not a career for the fainthearted. Large data sets can be very troublesome to work on. Patience and an analytical mind are very necessary for every data scientist. 

To gain a sufficient amount of knowledge, data scientists need to read extensively on data science materials and practice the skill. Reading books and articles, watching videos, and listening to podcasts are just some of the initiatives that may help you gain sufficient requisite knowledge.

There is no doubt that data science takes time to master. It also requires patience and a lot of commitment. Underestimating the time and commitment required is one of the mistakes aspiring data scientists make.

Dismissing Basic Analytical Skills

Basic analytical skills are very important in data science. However, many aspiring data scientists focus on model development and overlook basic analytical skills. These aspiring data scientists restrain the scope of data science to model development alone. 

Basic analytical skills are very essential in data science. Spending more time on understanding the data comes in very handy in data science. Understanding the data helps the data scientist gain extensive insights into the data and the possible outcome of the model.

Data science is not only about model development. Aspiring data scientists must be cognizant of the basics of data science. Data analytics and visualization are very useful in model development. Exploring the data reveals a lot to the scientist, therefore, facilitating good decision-making while developing the model.

Never Going Beyond Online Course and Theoretical Knowledge

This is among the most common mistakes aspiring data scientists make. For you to be a professional who’s mastered every key aspect of data science, you need to go beyond online courses. Most aspiring data scientists limit themselves to online courses alone. 

While online courses offer valuable information and technical know-how, data scientists need to look beyond them. There is nothing wrong with online courses, but professionals need to prove that they learned the material and they did the work.

Data scientists need to prove knowledge of basic concepts of data science and demonstrate they can apply those concepts to solve real problems. As an aspiring data scientist, you must demonstrate practical knowledge and experience in solving real-world problems.

Taking a Bootcamp and competitions that are focused on applying theoretical knowledge is one way to get started. Bootcamps provide aspiring data scientists with the experience and technical know-how required to solve real problems. The point is to go beyond theoretical knowledge and gain experience. 

Conclusion

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

However, data science can be a little bit tricky for beginners and aspiring data scientists. We hope our list of mistakes aspiring data scientists make will help guide you as you build your career. 

We also have certified courses on data science that will help you build your career and establish yourself as a professional. 

Our courses are informative and will provide you with all you need to know in the field of data science. Check out our courses section or get in touch with us to get started.