A Few Things To Remember Before Going To Start a Data Science Course

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Image by Денис Марчук from Pixabay

Over the last few years, the data science domain has evolved exponentially. In present times, it has become the backbone of many organizations across various industries. Data Science is also entitled as the ‘sexiest job of the 21st century’. It is witnessed that more and more people are transitioning to data science as a career.

The first set of mistakes seem to be undercover and they are very hard to spot. Slowly, but surely they will drain your time and energy without giving any prior warnings and spawn from the misconceptions around this field. If you too wish to begin a career in data science then you can save yourself days, weeks and even months of dis-ease by avoiding some of the costly beginner mistakes. Let’s begin.

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Spending unnecessary time on Theory

We have seen many beginners falling into this trap of spending more time on theory be it math related linear algebra, statistics or machine learning related algorithms, derivations and more.


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This theoretical approach is slow and daunting as you can feel overwhelmed and likely to sink into this trap. You cannot even retain the concepts as well because data science is an applied field and the best way to approach is by more practicing. There might be a greater risk of becoming demotivated and giving up to see if you don’t know how learning connects to the real world.

How Can You Avoid This?

First of all, you need to balance your studies with projects that provide you hands-on practice. You should learn to be comfortable with partial knowledge and slowly fill in the gaps as your progress naturally. It is vital to learn how every small piece fits into the big picture.

Don’t Begin to Code from Scratch

This next approach is very dangerous for students as it is likely to feel like losing the forest for the trees. At the initial level, you really don’t need to code every algorithm from scratch.


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Well, it is nice to implement a few of them only for leaning purposes, the reality is that algorithms can become commodities. All big thanks to the mature machine learning libraries and cloud-based solutions which allows most practitioners never to code algorithms from scratch. It is essential to understand how to apply the right algorithms in the right way.

How Can You Avoid This?

You can try picking up some general-purpose machine learning libraries like Caret for R and Scikit-Learn for Python. If you still wish to code an algorithm from scratch, then do so with the intention of learning instead of just perfecting your implementation. Try to understand the landscape of modern machine learning algorithms and their pros and cons.

Jumping off to deeper Conclusions

We have seen some people entering this field because they want to build the technology of the future which is self-driving cars, advanced robotics, computer visions and so on which are powered by using techniques of deep learning and natural language processing. Hence, it is important to master the fundamentals ever Olympic diver needs to learn how to swim first and so do you.

How Can You Avoid This?

First of all, master the machine learning techniques and algorithms which serves as building blocks for advanced topics. While the algorithms are already so mature you need to discover some fruitful ways to use them. Try learning a systematic approach for structuring machine learning projects.

The Road Ahead

To become a full-stack Data Scientist, many institutes are offering Data Science course which can be taken into consideration by looking at its relevant curriculum, total hours of practical sessions, mentors with the significant experience, placement records and last but not least, honest reviews from alumni. Prior to joining any of the data science course, you need to check that not only the course but also the institute is well-reputed enough which can help you attend interviews. Keep Learning!


Note: This is a guest post, and opinion in this article is of the guest writer. If you have any issues with any of the articles posted at www.marktechpost.com please contact at asif@marktechpost.com  

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