Top 5 Data Science Mistakes You will Never Make Again

You already know that “Data Scientist ” is the sexiest job of the 20th century. What makes it sexist? The answer is very simple.  The analysis of data, finding the pattern in the data, new things to learn in machine learning, Artificial intelligence e.t.c are all fun things for them. It seems good to them but the main problem comes when the newcomers in the data science are unable to find their data science mistakes to avoid.

In this article, you will learn how to avoid the common basic data science mistakes before starting your data science career or learning data science. You will also know the best solutions to overcome these mistakes.

Top 5 Data Science Mistakes

Data Science Mistakes

1. Keep learning new things on data science without any practical work.

It is the number one in data science mistakes. From childhood, you must be listening from your parents or teachers that you should learn theory but also do practical work on it. It is 100 percent correct. In the data science fields, you should not only learn and focus only on concepts but also do some practical work on the knowledge you gained.

Let’s take an example. You are learning theory from some video courses or books. You keep seeing videos or reading the theories only. Then you will learn only a few things that do not meet the conditions for the real world.  For example. while applying a machine learning algorithm, you already know the parameters required for creating machine learning model. But in the real world, all parameters are not defined and you have to find the solution to the problem. A perfect data scientist is able to find constraints and analyze the solution of the problem in deeply way.

Therefore you should apply and learn both while learning the theory. Whenever you learn new something then try to find the dataset and apply all your knowledge on it. There is a complete list of websites for the datasets you can find it here.Top 15 Datasets for Machine Learning 

2.  You think data science is all about machine learning algorithms.

Many new students learning data science think data science means machine learning. But it means different. Machine learning is a particular skill in addition to data science. Then, What is the job of a data scientist? Their job is to do analysis on the data. Thus they must have knowledge of linear algebra, statistics and domain knowledge. In addition, Machine learning algorithms used for making predictions.

It means A problem is given to Data Scientist. Then the task of the data scientist is to collect data for the problem and find the best solutions for the problem. Inside the problem, they use the required machine learning algorithms for predictions.

3. Wants to learn all the things at once and fast.

I consider it as one of the huge mistake data scientists make. Although You know that there are various fields inside the data science, you want to learn everything quickly and at once. It prevents you from learning the depth of the field. Therefore, You should be patient while learning. Let me make more clear to you. You should try to choose the interesting part from the various fields of data science. Start with a small problem and move slowly to the larger problem. It helps you to become an expert in a particular field.

4. You don’t do well research for the problem.

The other data science mistakes are that data scientist does not do well research on the problem they are given. They don’t know about the problem and domain and consider the task as easy. Due to this, they are unable to find the solution of the problem easily. Even if they are the success at some points but at the end they unable to reach the final completion stage of the problem.

Therefore I suggest the data scientist to first get a domain knowledge of the problem. You must do complete research on that problem before starting to find the solution to the problem.

5. Data Scientist is all about Coding Skills

According to me it will the general data scientist mistakes are done by the developers. People think data scientist is all about coding experience. But this is not hundred percent correct. Data Scientist is the problem’s solution finder. They try to find the solution of a given problem in creative ways. This doesn’t require that you must have the coding skills for becoming a data scientist. Therefore if you want to become a data scientist you should not give full attention to coding skills. Today there are many tools and libraries developed for helping data scientist that does their work in an efficient way. I advise you to learn other skills also with coding skills.

End Notes

Data Scientist will become  the sexiest job in the coming years. But this also increases competition among developers. Therefore you should get know more and more. More you have skills in statistics, communication skills. coding skills e.t.c, then your chances of success will increase.

I think this article on 5 data science mistakes helped you to prevent you from making mistakes:)! Hope you enjoyed the article. If you have any suggestion please contact us or comment below.


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Meet Sukesh ( Chief Editor ), a passionate and skilled Python programmer with a deep fascination for data science, NumPy, and Pandas. His journey in the world of coding began as a curious explorer and has evolved into a seasoned data enthusiast.
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