Data Science Learning: Three Things Every Aspiring Data Scientist Must Avoid

Three things one should avoid while learning data science.

The application of data science is not just concerned with one field rather its application is disseminated across various sectors. The field of data science includes data modeling, data transformations, machine learning, statistical operations including descriptive and inferential statistics. Companies are realizing the importance of data science. Most of the companies make major investments in data-related solutions. This has resulted in the growth of data science. People are choosing data science as their career option and even many professionals are making a career change to work in the field of data science. But this is not a simple and easy transition because data science is a broad field. There are certain things that every intending data scientist should avoid while learning data science.

Avoid Competition on Kaggle

Although Kaggle is a great platform to learn, the competitions on Kaggle are not for ones who are new in this field. The samples and notebooks shared by people contain great learning material, the platform also provides data sets that can be used for practicing. But there are competitions with prizes which should be avoided by the new ones in the field. Competition for a prize is very difficult if you lose it might break your interest in learning. There are some playgrounds on the platform which are great for practicing and learning. When you start with a data science career you must invest your time in learning and practicing and not spending time on competitions. Even if you spend a lot of time on competitions, you might not even get close to the leader board, which is normal. But not improving or getting better can break you and you can feel demotivated and inadequate. So, for beginners, Kaggle is a platform for learning fundamental concepts and not for testing your knowledge.

Avoid Choosing Between R and Python

In the field of data science, there are enormous software tools that help in completing the tasks effortlessly and effectively.  But it is important to note, if the tools are not properly used then it can turn into a disadvantage. As there are multiple tools to perform a task, the selection of tools becomes difficult. It results in a discussion that involves some kind of comparison. The most popular comparison that we often witness is the comparison between R and Python.

But if you are a beginner then you should not waste your time thinking about which one to choose. Almost all the options will be good for you to learn the basics.

While doing data analysis you can use both R and Python, anyone you choose will not matter when you are learning. Once you are done with our learning and get your job, you can then make preferable decisions.

Avoid Emphasizing on Theory

Sometimes, graduates can overestimate the value of their education. While a strong degree in a related field can definitely uplift your chances, it's neither sufficient nor is it usually the most important factor. In many cases, people focus on the theory part. What's taught in a theory is simply too different from the data science applied in businesses. Working with deadlines, clients, and technical hurdles necessitate practical trade-offs that are not as urgent in academia. Therefore, one must avoid spending too much time on theories.