5 Programming Tips for Data Scientists




There is a huge demand for data scientists at various levels in almost every industry. Programming is one of core skills, every hiring manager would like to see in potential data science applicants. The importance of programming skill in data science will not disappear anytime soon regardless how much headwinds and transformations happen to data science and machine learning. Programming is one of fundamental skills required for any data science project. Without solid programming skills, it is unimaginable to achieve objectives of a data science project. Almost every step of data science process, from data extraction, to transformation and processing, to model building and deployment involves certain amount of programming.
Whether you are an employer or data science enthusiast, you would need to keep any eye of following 5 traits. This would allow to keep your programming skill up to date and relevant.

1. Orthogonal Data Science Development

Whether you are beginner or experience programmer, you must alway stick to one programming task at a time and take that task to the end. If it is programming of machine learning model, only focus on modeling part of your project and least worry about model validation and deployment. Our brain also tend to procrastinate and this is more so when a programming is programming. This happens because the brain is super active at peak of programming exercise and so is procrastination. Therefore, the challenge is to keep procrastination phenomenon in check and be focused on the main task and get it done.

2. Be Ready to Be Challenged

Data science development and programming is not easier. As a data scientist you have to work with entire ecosystem of technologies (OS, SQL, development languages, automation, deployment) rather than just working with one programming language or library. Therefore it would always be a steep learning curve for beginners. It requires a lots of effort to acquire adequate development skill set required for a typical data science project. It is utmost important for beginners to not give up despite complexity of learning and challenges because we all have different strengths and weaknesses. In data science learning, at some skills you would be good and on others you might be doing okay - which is quite normal.

4. Build Data Science Coding Values

Being a data scientist you must build some healthy values in every aspect of your professional personality and good coding ethics is one of them. There are different coding styles out there. You should try to pick one which is prevalent in data science community and stick to it. For many new data scientists particularly those coming from academia, it takes time to adapt naming conventions etc. but gradually with some extra effort one can learn how to write fast readable code.
It is important to know what standard your data science team follows. Different data science teams have different coding standards. When you work in an abstract way, the coding becomes even more difficult regardless of company or guidelines.

4. Learn to Ask Google a Question

Google is a great source which can help you to improve at data science programming and its style. Many great data science developers spend good amount of their time on Google while coding and debugging their projects. These days Googling is an important skill for any data science. It is important to ask right question to get correct any answers and ideas about programming and modeling strategies in data scientists.

5. Read, Write, Mentor

The saying, “nothing is constant but change”, perfectly applies to data science. People across academia and industry are so excited and involved in development of data science technologies that everyday we have something new to learn and use to solve our day-to-day data science problems. Therefore, it is very important to subscribe to important data science forums and blogs. You can take you learning to next level by starting writing about what your data science experience and learning. This would help you to bring more clarity to data science and programming concepts. If you are an experienced data scientist, mentoring and teaching programming for data science can be another useful way to broaden your perspective.

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