In data science, just like many other fields, you learn more by doing than by reading books or studying the technical aspects of the field. When anyone starts their data science learning journey, you will mostly spend a lot of time and effort learning many aspects, skills, and terminology. You will learn to code, maths and statistics, algorithms, visualization, and business basics.
And although all these concepts and topics are extremely important, knowing the theoretical side of a field doesn’t mean you will succeed in the field or can implement projects without a flaw. Sometimes, as beginners, we tend to do some simple to avoid mistakes that we only do because we lack the experience or we just weren’t taught to avoid these mistakes.
But, once we start building more and more projects, work on different themes with different teams. Then, on different datasets, we will develop an intuition on how to approach any problem, plan specific steps to reach the solution, and be able to solve any problem that arises in your way. So, although you will find your own way to avoid mistakes by building projects, you can also gain this knowledge by talking to data scientists further ahead in their careers.
I have been where you are, and I talked with many data scientists about their learning journey and what they wished they knew earlier in their career that would’ve helped them progress faster and better. But as I heard a lot, you learn better by doing; when you experience something, it sticks in your mind better than when you hear it out. That being said, reading and gaining information will never be a bad thing.
In this article, we will walk through 9 common mistakes often done by newbies and sometimes experts intentionally or unintentionally that lead to false results or cause the project to take much longer to finish. You can find these mistakes and more in many blog posts such as SamrtBoost, JigSaw, CIO, and other online resources.
Let’s start things off with the most commonly made mistake, even as professional data scientists, is to go ahead with a project without having a “plan of attack.” Often, when we are given a data science problem, we need to answer “why” is the data behaving the way it does, and to answer that equation, we need to be clear on what to do. That’s having a plan and an idea of what are the steps we need to take.
If there’s something that I…
Continue reading: https://towardsdatascience.com/9-mistakes-you-should-avoid-in-your-data-science-projects-8e258c5e9374?source=rss—-7f60cf5620c9—4