6 Do’s And Don’ts for Aspiring Data Science Professionals

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Data science is a fast-growing field. This doesn’t negate the fact that it is also a challenging field. It requires knowledge of a multitude of subjects to work effectively. One of the things that make data science interesting is solving problems. If you’re deterred by the idea of having to solve problems, and instead go with the flow and take how things are, getting into data science is a bad idea.

If you are aiming to get into data science or for that matter have already gotten into it, here are a few do

‘s and don’ts.

  1. Do: Extensive research 

Before you get down to solve a problem, read about it. As a data science professional, you will be solving problems every day. How to tackle a problem will be a critical factor in deciding whether you become a good data scientist, an average data scientist, or a bad one. Read books, watch YouTube videos, and even read blogs from prominent data scientists in the industry.

One of the things that make data science interesting is – you can’t mug up things. You will have to be rational and proactive in solving problems from scratch. The more you read on the subject related to the problem, the clearer you can think and the more effective solution you can find.

  1. Don’t: Don’t give up

Data science is challenging. Once you begin to learn subjects, it is easy to give up when you see yourself going nowhere. It is common in data science. Like many technical professions, data science is sometimes exhausting especially when you’re trying to solve a challenging problem. This happens to the best of data scientists, so don’t feel bad and do not give up.

Consider your failure as a stepping stone. It is as common as it gets. Analyze all the steps and see where you faltered. Learn from your mistakes and improvise as you move forward, but don’t give up.

  1. Do’s: Implement

Data science is the most practical profession ever! It doesn’t matter how well-versed you are in theoretical machine learning and deep learning algorithms. Your proficiency in implementing theoretical concepts to solving problems would matter the most in data science.

Pick up a machine learning problem and implement it. Similarly, do it with a deep learning algorithm. It is necessary to know how to implement theoretical data science concepts in a real-life scenario. Also, don’t be afraid to try your hands at coding. If you’re new at coding, this will be essential to work successfully in data science.

 Further, the projects you pick will help you stand out in the interviews.

  1. Don’t: Hesitate to ask for help 

It is common for data science professionals to get stuck on problems. In such situations, it is good to reach out for help. LinkedIn is a good place to search for data scientists in your area and ask for help.

Similarly, Github, Kaggle, Towards Data Science, etc. are a few more places where you can connect with equally driven data science professionals and seek their help.

Simply quote your problem and you will have numerous suggestions coming in. Just don’t be afraid to ask.

  1. Do: Get a data science certification   

The Data science industry is still new and growing. Employers have a hard time finding a perfect data science professional for their job. A globally –recognized vendor-neutral data science certification proves your competence and demonstrates that you possess the necessary skills required to work effectively in the industry. DASCA (Data Science Council of America), IBM, Cloudera, among others offer globally recognized certification.

A data science certification will be especially helpful in getting the job. So make sure that you get one certification.

  1. Don’t: Do not stop looking for better solutions

Once you have found a solution to a problem, do not stop at it. Optimization is the key to successful problem-solving. So keep looking for better solutions.

If you have built machine learning models, work towards increasing the accuracy of the model. Remember that there’s always scope for improvement no matter what you do. In a field like data science, where development is taking place every day, it should never feel like you have reached the end. Solving a problem ultimately is subject to unspoken conditions and circumstances. You can challenge yourself by putting constraints and then looking for a solution.

Conclusion 

Data science is a fast-growing and challenging field. Consequently, getting into the data science industry is tough. However, with the steps mentioned above, you can smoothen your entry into the data science industry. If you are just starting in data science, stick to these dos and don’ts at all times. These will go a long way in shaping your career in data science.