It wouldn’t be wrong to say that with high demand, the field of data science also gives enough opportunities to people from different domains. To put it more accurately, data science job roles are going to see a huge spur this year as well. Naturally, since data science is such a good mixture of challenging work and lucrative salary, many people are either switching careers from the domains they already work in or upskilling in order to jump into the field of a data scientist. So, getting to know what mistakes other data scientists make before one enters the field becomes crucial.
Below are addressed some common blunders that an amateur data scientist can commit in his/her career. You can be someone who is just starting out or someone who is already pursuing a data science career. Hopefully, you can avoid committing them:
Wasting Time Concentrating Only On Theory
Many data scientists spend too much time learning concepts related to maths and machine learning like linear algebra, stats, algorithms, derivations, etc. It is good to have a thorough knowledge of these concepts, but in the beginning, if they do not apply and practise these, it would only result in an additional waste of time. One can only learn, read and grasp these concepts, but when one puts the theoretical knowledge to practical use, it is highly likely that they won’t be able to remember everything. So, it becomes imperative to apply these fundamental concepts as they go on learning them.
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Amateur data scientists must strive to find the right balance between theoretical and practical studying. Here are some data sets that can help practise and understand the basic concepts better:
Being Hasty
Many beginners make the mistake of jumping in too deep at an early stage. No matter how passionate one is about learning and applying data science, they have to go through fundamental concepts one by one. Avoiding shortcuts is better in this case.
A beginner can first try to master techniques and algorithms of classical machine learning, which will serve as the stepping stones for further advanced concepts. However, one needs to make sure that they have an ample amount of practice every time they are about to move towards a more advanced concept.
Check these beginner-level machine learning projects.
Coding Without Understanding the Prerequisites
This is another common mistake committed by beginners, where they code an algorithm with the intention of making it perfect, rather than learning it. Coding an algorithm from scratch without complete knowledge will eventually lead to problems in solving the practical problems.
To refine one’s understanding, there are a few concepts that a data science beginner needs to have. These concepts include linear algebra, statistics, probability and calculus. Data science includes all these concepts, so it is recommended not to move forward until someone has a clear picture of these concepts.
Here is a link that one can follow where we have mentioned some of the best free data science courses for beginners: 5 free data science courses for beginners.
Accuracy Over Understanding
It is a common notion among beginners that accuracy is everything. While it is true in some cases, it becomes a huge problem if one cannot explain how the model works, which features have led it to the result and about the thought process behind it to the client. If the data scientist does not address these, then there’s a high possibility that the client may reject it. In this case, for data scientists, it becomes imperative that they not only concentrate on accuracy but also give attention to how the model works.
One way to understand the models better is to consult data science professionals already working in the industry. They can help one out in understanding the process, but when it comes to learning, there is nothing better than hands-on learning. Again, it is recommended that one pick apart a simple algorithm, understand its applications and why it works the way it does. This will also help one understand why simpler models are given preference when it comes to real-life applications.
Trying to Learn Multiple Tools At Once
There are too many tools nowadays that offer a variety of unique features and applications. So, when a beginner leans towards learning all the tools possible at once and also try to apply them at the same time, it will only lead to more confusion and will affect one’s skills when it comes to problem-solving.
Learn a particular tool and master it, and when one is sure that they have perfected it, then they can approach a different tool according to its use case.
And while learning tools are essential, it is necessary to know how it can help with the real-world business problems. Knowing various libraries and tools is critical, but it is crucial in the data science field when all that knowledge can be applied to real-world business problems.
Not Having Structured Thinking
Structured thinking means putting a framework on an unstructured problem. Having structured thinking helps an analyst identify areas where there is a need for deeper understanding and also understand the problem at hand better.
The framework or structured mindset gives one better chance to see ways of solving a problem. Suppose if one is given an assignment, which has a close deadline, having structured thinking makes it easier for the data scientist to come up with right solutions and effective ways to handle tools related to the problem.
Not Emphasising On Storytelling
Whatever insights a data scientist draws from the company’s data has to be effectively communicated to the non-technical teams. These technical insights have to be clear, accurate and must be easily understood by the organisation.
One has to give importance to this aspect because communication, brainstorming and discussion are very important when it comes to data science. What good are the technical skills and tools expertise when one cannot effectively convert it into storytelling?
One of the ways someone can improve their communication skills is to learn how to visually convey their insights. Putting one’s thoughts into a slideshow or any other approved form can become one of the ways to effectively communicate.
