Mistakes That Newbie Data Scientists Should Avoid

Check out this list of common mistakes that data science newbies make, so you know what to avoid in your job search journey.

Mistakes That Newbie Data Scientists Should Avoid Cover
Photo by Andrea Piacquadio


We all know how demanding the Data Science field is at the moment. With more and more people entering it, from all sorts of backgrounds. Some with Computer Science degrees, some with no tech degree background at all. 

This makes it more difficult for those candidates with a little technology background to enter the field and not make common mistakes. Below is a list of these common mistakes, so you know what to avoid in your job search journey. 


Don’t Underestimate Formal Education


If you have a search for Data Science degrees, most of them require education. Although there are many BootCamps and courses out there that complement your resume, many recruiters are looking for candidates with some form of technical degree and/or master's degree. 

The bright side is that more universities are offering data science programs and online courses to get you to the level of knowledge required to comfortably apply for Data Science roles. There is the possibility of going self-taught, however, that requires a lot more independent effort and determination. It’s a harder route, but it can happen. 

If you would like to check out some free university resources, have a look at this: Free University Data Science Resources


Focusing on the Theory and Not on the Projects


It is typically for newbies in a new industry to focus heavily on theory work; they want to have a great understanding just in case someone asks them a question. However, try not to dig too deep into it and start to focus on projects which present your skills and practical applications. 

These will test your level of theory and give you a better understanding of where and where not to apply it. Learning the theory whilst applying it will improve your likelihood of succeeding in the field and mastering the two. 

There are so many free datasets out there where you can play around and test your knowledge. You’re not limited at all, you just need to take the jump. 

If you would like to know more about some potential projects you can work on, have a look at this: Top Data Science Projects to Build Your Skills


Trying to Fly to the Top of the Ladder


Many people enter the Data Science world with hopes of working with self-driving cars or medicine. This requires a lot of deep learning knowledge which doesn’t come to you overnight; it takes time. Years even. You will need to have experience working with simple datasets, building machine learning algorithms, and more. 

It’s all a process that can’t be rushed; therefore you can’t just automatically enter your field of interest, you need to work towards it. 

Accepting that you will have to be a junior for maybe a year or two and then have to work on machine learning projects for the next 5 years is a good reality check for you to achieve your end goal.




Resumes are always difficult because you want to sell yourself but sometimes that can lead to your resume looking too messy. In Ladders 2018 Eye Tracking study, they revealed that recruiters spend on average 7.4 seconds scanning each resume. 

You can imagine how many people are applying for Data Science roles, and how overwhelming it can be for recruiters that come across resumes that are filled up with a lot of information. Rather than doing this, paint an easy picture to the recruiter with important points through bullet points and a good structure. 

This automatically increases your chances of moving on to the next step. 


Preparing Yourself for the Interview


Many Data Science graduates are constantly applying for job after job, and when someone gives them a call back; they’ve spent so much time and energy applying for jobs that they haven’t actually prepared for the interview stage. The easy part was applying, the hardest part is trying to win the recruiter over. 

Each technology company can do its recruitment phase differently, however, they are typically the same. It can start with an initial call which then moves on to coding assessments, which can either be requested to be done remotely or in the office. 

This is where your skills are really going to be tested and you want to ensure that you are prepared for it. You will be tested on your hard skills aswell as your soft skills; so try not to neglect one for the other. 

If you’re looking for more information to help you with this, have a read of this: 


Job Search Effectively


Don’t just apply through a job title; use your skills to help your search. There are going to be many openings for Data Scientists but you may not have the skills they require. In order to do this, you need to make sure that you read the description and requirements to see if you are a good match. 

Searching using the skills you do have will narrow your search and save you a lot of time and energy applying to thousands of jobs that may not reply. You can search by job responsibilities such as Predictive Modeling or skills such as SQL. 


Understand the Sector You are Entering


Data Scientists are in high demand in nearly every industry at the moment; from finance to fashion. When applying for jobs; it is imperative you understand the sector. You don’t want to start a career as a Data Scientist for a Bank with no knowledge of how banks work and the terminology. 

If you do that; you are literally throwing yourself into the deep end and it may be very hard for you to get out of it. You will end up hating your job and your choice of career; so ensure that you are entering the sector you wish with a sufficient amount of knowledge. 




These are the basics that will help you have an effective strategy for entering the world of Data Science. They are such common mistakes that can be easily resolved. If you want to know more about industries that are employing, have a read of this: Top Industries and Employers Hiring Data Scientists in 2022

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.