Did you finally make up your mind to start your career in the field of Data Science? I would say quite a good decision you have taken. As the world of data science is growing, so the various job roles and opportunities in this field are becoming demanding. But the journey of becoming a successful data scientist doesn’t seem to be an easy task to achieve in one go.

It’s quite obvious for Data scientists to make numerous mistakes at the beginning of their professional life. We all consider the fact that errors are an important part of any job growth, but it is also important that we avoid such mistakes by continuous learning and rectification.

In this particular blog post, we will be discussing a few mistakes which an amateur data scientist faces and has to struggle with throughout the journey of his career growing phase. We will try to analyze why these mistakes occur and find some suitable solution to avoid it.

This is not a detailed explanation but some of the common mistakes which each one of us normally makes. I have myself made a few of these mistakes and so have included the resources which you can follow to avoid such pitfalls.

Before you see the mistakes, check out these related posts and learn more:


8 Mistakes in the field of Data Science:

1. Mugging up theoretical concepts without understanding their application: 

In a general approach, it’s quite a good practice to initially gather all theoretical concepts behind various machine learning techniques.  

If you are a beginner, planning to start your journey of learning various logistic and linear regression algorithms, then I have the best resource for you. Check out the article Simple Linear Regression. But ask yourself, is that enough?

When I started my journey in data science, I made the same mistake of studying the theories from various books or sometimes referred to online courses but didn’t at all apply those concepts to solve a real-time problem. And trust me, when a situation comes where in reality you are given the challenge to find a solution, till then it’s quite natural to forget half of the things and you may give up too losing all your zeal towards data science.

So, it’s better that you should maintain a healthy balance between both theoretical and practical learning processes. Once you are done learning some specific algorithm, just google out a dataset or problem related to that specific concept you learned and try solving it. In this way, you will raise your knowledge level and learn the approach for solving any problem.

2. Running Straight for Machine Learning projects without Learning the basics: 

The majority of the folks who aspire to become a data scientist get attracted by youtube videos showing the working model of robots, or some awesome predictive models, and start copying the code to complete a project to add to a resume. 

Sadly to say, but you are going on the wrong path as these will spoil your dream of becoming a successful data scientist. It is quite important to know and understand how such techniques work and how to perform fine-tuning before we start applying them to a problem.

Now, here comes the role of Mathematics which is considered to be the most vital subject to clear our concepts. Having advanced knowledge of calculus, statistics, probability, and linear algebra will help you a lot in building your career. You might not understand things initially, but don’t lose your motivation. Just continue your journey of trying and finally, you will get the desired result.

3. Not sharing data reference in code:

Imagine a case where a team of students is working on a particular machine learning project pipeline. Now, suppose the team leader initially starts collecting the dataset, cleaning it, and then he shares the code with his rest team members to reproduce the result. Is his work completed here? No, because he didn’t share his dataset which is quite essential for others to work on the same project. 

All members should have access to the data. Quite a simple problem but most people forget to share the data along with their programming code. You can either upload the dataset in google drive or GitHub and share the link, or you can directly mention the linked site from where you have downloaded it. And, always make it a point to dump the code and dataset in the same path location of your system.  

4. Giving less prior to data:

This mistake may cost you a lot. I assume that you have grasped a good understanding of the various machine learning algorithms and have also implemented it to solve some challenges. But do you think your work is over? Your excitement and eagerness to complete the task may lead you to risks. How? There may occur overfitting of data, data leakage, and other biases to add to the list. 

Whenever we are building a model, data engineering and data featuring are the most vital sections to put focus on. It is always mandatory to concentrate more on data than on the fancy algorithm because the proper data and its features shape the robust model with accuracy. Try to spend more time exploring and visualizing the data rather than just completing the task by building a model. The more you explore a dataset, the deeper insights you gain and it will have a huge impact on your result model. There is no scope of skipping these steps as data visualization is the most vital facet of building a project model.

5. Certificates and degrees are not your best identity:

Ever since data science has become the most trending technology, the wave of gaining certificates and degrees has flooded everywhere. We all are running in the rat race of collecting certificates. Understanding the basics of programming languages like Python and SQL is quite essential. 

Don’t take me wrong. I do also agree that a degree or a certificate in a related field will boost up your chance to grow your career but this is not an important factor, it will not qualify you. In most cases, what we are taught in our books turns out to be completely different from the applications of machine learning in various fields.

Undergoing internships in some small companies or start-ups (don’t bother about your experience level) will help you learn the complete working procedure of a data science team, and in turn, benefit you for your further interviews.

6. Focussing on accuracy score rather than understanding the model:

The fundamental aspect of data science is doing research to understand things to the depth.  Most of us try to increase our predictive model accuracy score to 95-96% but accuracy isn’t the ultimate goal of our project. We are making a mistake by overlooking how a predictive model makes a prediction and its workflow. If we fail to explain our model to a layman, then there is no value for a 95% accuracy predictive model. The client should get a clear explanation of how we got the model and what features are involved or else the model is simply rejected. 

One of the best ways to prevent such mistakes is gaining as much experience as possible which includes speaking with delegates working in the industry, practicing simple problems, and explaining them to non-technical people. Once you continue with this flow of learning, you will surely shine in your career.

7. Not good at coding doesn’t mean your dream is finished:

Gone are those days where you must be a technical guy to perform data analysis and exploration. Now, you don’t have to spend hours learning how to code as several tools are available in the market today to help you with any difficulty you face and save your time too. Mentioning a few tools which include Tableau, Rapid Miner, Trifacta, Excel Worksheet, Knime, Qlikview, H2O, Talend helps you to code explicitly by simply dragging and dropping the required functions.

8. Resume having complex technical terms:

Many students make the mistake of suffocating their resume with too many unwanted data science terms but it changes the mind of hiring managers. They easily understand what is true and what you are faking. Your resume should tell the recruiter how and in what way you can add benefit to the organization.

The first impression a recruiter looks for into a profile is the educational background and the projects done by the candidate write neatly and understandably. Heavy and vague data science terms in resumes may not get shortlisted in the screening round.



The Ending Note

There are a lot many tasks that a data scientist needs to manage such as mining data from different databases, developing algorithms and data models, working with stakeholders, developing tools for accessing and increasing data accuracy. So, after reading the above eight common mistakes, we can draw the conclusion that not all beginners but expert data scientists do also commit critical errors in the field of data science.

However, the good side is that we get an opportunity to introspect and learn from every mistake we commit in our working hours. To end up, we can say that the errors that we encounter over time can be easily fixed and left behind with deep analysis and effective research.


1. Top 10 Coding Mistakes Made by Data Scientists
2. 4 Common Mistakes Amateur Data Scientists Make
3. 12 Mistakes that Data Scientists Make and How to Avoid Them

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