What are 15 Data Science Mistakes To Avoid As A Data Scientist?August 20, 2018
We all commit mistakes, and there is nothing shameful about it. Learning from those mistakes is what makes your life a step ahead. But, while working as a data scientist, you have to be cautious as one number here, and there will end up giving completely different results.
Here are some of the unintended mistakes you might make while working on piles of data. As a data scientist, you must try to dodge off these mistakes.
- Being too bookish: No doubt, knowledge comes from books but while working as a data scientist, you have to be practical in approach. Not every problem is solved similarly. There is so much to read- Algorithms, derivatives, statistical functions and again books. But, it is of no use, if you are unable to recall it when the time to apply it arrives. Therefore, try to supplement your bookish knowledge with a bit of practicality.
Also, Read – How to Build a Career in Data Science?
- Start from the basics: Don’t jump to machine learning without even knowing to mean, median, mode. Start with the basics of data science. Once you have a good hold over mathematical operations, fundamentals of mathematics, command over a programming language and an aptitude to apply all these skills for executing real-life problems, start with machine learning and AI.
- Choice of wrong visualisation tools: Concentrating on a limited number of technical aspects of data science inhibits your learning process. You should try to diversify your selection of visualisation tools to look at the assigned data with different elements. Because a particular trick may apply for a few data problems but not for every question, you will encounter as a data scientist. Similarly, not every data problem comes with a pre-assigned mechanism to solve it.
- Analyzing data without a decided objective: A data should be analysed keeping in mind what you wish to achieve as a result or else you will end up digging diversified results which might be of no use.
- Do not forget ethical issues concerning data: A data can have sensual entries. While working on the data, try to unearth the results which can be helpful for your organisation. As a data scientist, protection of data is also one of your responsibilities.
- Never be too proud of your certificates and degrees: Having a degree from a renowned institution is right for your academic health but never be too overwhelmed with those certificates in your hand. After all, the experience is an excellent teacher which you will eventually gain through the passage of time.
- Inconsistent learning process: Never pile up your work. Make sure you practice daily whatever you have learnt.
- Paying no heed towards the development of communication skill: Your knowledge is incomplete if you cannot dissipate among others. Once you are done with your graphics, charts and accomplishment of the daily task, try explaining it to others. Even a person from a non-technical background should be able to comprehend what you are trying to say.
- Avoid manipulating the data: Don’t make unnecessary additions to the data to achieve desired goals. Be honest about your job as a data scientist.
- Biased sampling: All the sections of the population must be covered. Otherwise, the results would be considered discriminatory.
- Doing anything to get your work published: This is a cardinal sin which a data scientist would ever commit.
- Never be too obsessed with the data.
- Learn the skill of segregating the data well.
- Never focus too much on model accuracy.
- Have limited sources to achieve the results.