10 Wrong Reasons To Become A Data Scientist | by Bharath K | Dec, 2020


Data Science is regarded as the sexiest job of the 21st century. It has an abundance of vacant positions. A well-reputed job is something everyone desires. If you have no interest in data science, but you feel that it is one of the easier jobs to get due to the large requirement of skilled data scientists, then you are mistaken.

There is a high-level skill ceiling that is a compulsory requirement for securing a data science job. You either need to have an awesome resume with tons of cool data science projects that you have built or a degree from a reputed university with solid foundations of the basic concepts.

Data science can sometimes be a hard subject. There is a lot of effort required to learn the basic concepts of math, coding, machine learning, and various other areas of studies. It is not to say that you can’t study this within a few months of time. You definitely can, but only if you have the dedication and interest to pursue this wonderful field.

If you think studying data science will be a cake-walk and your primary reason for picking up the subject of data science is that you feel that you have an opportunity to secure one of the many vacant positions for the job, then that would be an incorrect consideration.

Even if you manage to secure a job as a data scientist, without any actual interest and love for the subject, it will be hard for you to continue working with perfection. We will cover this point in more detail in a later section of this article.

Coding and programming knowledge is almost a compulsory requirement to have an in-depth understanding of the field of data science. Even if you lack the coding or programming skills, but you have to interest to learn and pursue these techniques, then this should not be a major concern.

However, if you are not interested in coding or programming by any means, then there are a lot of awesome subjects out there that really don’t demand programming skills. Unfortunately, data science is not one of those fields, and coding is a necessary requirement.

Python is the best way for anyone, even people with no prior experience with programming or coding languages to get started with machine learning. In spite of having some flaws like being considered a “slow” language, python is still one of the best languages for AI and machine learning. There are tons of newer upcoming languages which might be highly impactful in the future as well.

Practice becomes significantly to keep yourself updated with all the latest trends and process the on-going techniques in this tremendous field. There is a lot of scope in every aspect with continuous developments. So, you need to keep coding and keep working on practical implementations!

Try to actively participate in competitions on websites. Kaggle is one such site that hosts some of the best data science, related competitions. Don’t worry about which place you finish. It does not matter much as long as you learn something new.

There are a lot of websites to improve your coding as well as participate in competitions like HackerRank, which you should consider. Involving in the community is helpful to consistently learn more from fellow data science enthusiasts.

But, if you have no interest in programming or coding whatsoever, then even with newer libraries like autoML or tools that offer you to work on data science projects, you will lack a solid foundation to build your own projects from scratch and lack an overall basic understanding of the subject.

The job of a data scientist is probably one of the highest paid jobs in the software related fields. You can earn a solid 6-digit USD figure, and even if you don’t secure/are not interested in a full-time data science job, you still have lots of opportunities to build various projects and earn money by freelancing or with startups.

However, similar to the first point of this article, if you have no interest in the field of data science, and you are just in this field for the purpose of making money, then it won’t be a long time before you are mentally exhausted. The majority of successful data scientists love the subject and creating newer projects for the betterment of our society.

The field of artificial intelligence and data science is humungous. There is so much out there to be curious about and explore. There are lots of concepts of mathematical functionalities, in-depth theory on multiple aspects of machine learning and deep learning.

If money is the only thing that you are concerned with, then there are probably easier fields and methods to earn money out there in the world. Data science is a subject that requires constant learning and progression to keep up with the skills and the various developments that are taking place in the world.

Without being too rhetorical or hypocritical, money is an essential aspect of life, but if the primary reason you choose data science to earn money and rake in the dollar ($$$), then you are probably in for a tough time if you don’t enjoy the field.

The main reason for this statement is because Data Science can be quite hard sometimes, and in fact, in a lot of cases, there are so many people who drop out because they can’t keep up with the constant pressure. You could earn a lot of money if you are an overall skilled data scientist, but if you are not happy doing it, then there is no point in pursuing the desire any further.

Mathematics, I find, is one of those subjects you either learn to love or end up loving to hate. Some find math as an amazing subject while others find all these number’s thing kind of boring. It does not matter which side of the spectrum you are on because math is, fortunately, or unfortunately, one of the most fundamental requirements for machine learning and data science.

Mathematics is an essential requirement for data science. Linear algebra, calculus, probability, and statistics are the most significant concepts that you need to know in order to conquer all the mathematical aspects of data science.

A high school understanding of the basics of these concepts would suffice for a beginner to enter into the universe of data science. However, if you are not too confident with these concepts or need a brief brushing, then I would highly recommend checking out reading some articles on TDS because they explain most concepts with simplicity and ease. YouTube videos are also a great alternative option to learn these concepts.

Mathematics is required for building predictive machine learning models, understanding probabilistic and deterministic approaches to solving Bayesian and other similar problems, understanding backpropagation in deep neural networks, analyzing gradient descent, and so much more.

A majority of machine learning involves important mathematics. If you absolutely hate math and are totally not interested in it, then you are surely in for a tough time as a data scientist.

“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.” — Atul Butte

Most of the successful data scientists I have spoken to have one main thing in common — Passion for data science and the incredible newer discoveries to be made in the future.

Passion and love for the subject of Data Science is an absolute necessity. It goes without saying that if you are not passionate and lack the overall desire for data science, then you should probably consider another field.

Creative, critical, and analytical thinking are some of the most intriguing characteristics of a data scientist. The ability to think outside the box and implement innovative ideas is a necessary and requirement for a successful data scientist to perform. These attributes are some of the key aspects of performing outstandingly on an industry level.

However, a more important quality that I have observed in successful data scientists who are pioneers in their areas of study is the continuous need for self-improvement and a student of the subject for their entire life. Most data scientists are humble who have a purpose for learning and sharing their extreme knowledge with society and other data science enthusiasts.

You need to have a constant drive and sheer passion for data science to last longer in this field and have a long and bright future with this spectacular subject.

Data Science, as the name suggests, has a lot of DATA.

Ranging from data mining, data extraction, data collection, data visualizations, data explorations, and so much more, data science is all about data. The term data may be loosely thrown out sometimes, but it is the most valuable resource for any project. The fields of big data, data science, and data analytics are growing tremendously. Tech giants are investing more in the collection of useful data.

Data can be considered as any useful resource or information available that is suitable for performing machine learning or deep learning tasks. There is a ton of data available for every model you want to construct. It is important to scrape and find only the valuable data required for the completion of the assessment.

The most interesting part of data science projects to me is building machine learning or deep learning models and making sure they work perfectly and feel good about it. Then, deploy those models built once they are meeting the appropriate requirements.

However, a large part of Data Science is actually dealing with the data at hand. Most of the data available naturally on the web is not clean. A lot of cleansing and pre-processing must be done for the extraction of useful data.

Most complex tasks require critical analysis and computational processing to obtain desirable outcomes. Persistence is extremely important in every scenario especially in the field of data science.

If you don’t like working with data, which can sometimes be considered a slightly lesser likable thing out of the many interesting aspects of data science, that it is totally fine. However, if you are absolutely annoyed and turned off by working with data, which is a quintessential part of data science, that should be something to take into consideration.

Data Science is not a stagnant field. It is a rapidly progressing and continuously developing field. Every other day a new technology or trends is introduced to the world of data science, and every top-notch data scientist needs to stay updated with these latest emerging developments.

The best part about Artificial Intelligence and Data Science is the continuous evolution of these subjects each day. The improvements in technologies are rapidly increasing. Hence, It becomes significantly more important to stay updated on the latest trends and emerging developments that occur in the field of data science.

Researching is an integral part of any Data Science Project. It is crucial to have some knowledge or at least a brief idea of what are expansions occurring in the AI field. Researching on a project or any particular task or even just a simple data science terminology is enormously essential.

Consistently learning and reading research papers is something most data scientists do on a daily or weekly basis. There is no surprise that this requirement is compulsory. I have heard a lot of people say that they have studied enough for almost 20 or so years, and they just want a profession where they can use the skills they have learned over the years.

If you are one of those people who don’t like learning a lot of new things, then becoming a data scientist is probably not the best choice for you. There are lots of fields where you can use the skills developed in your university or a previous job over and over to develop things and earn a living.

Unfortunately for those people, data science is a rapidly developing topic with newer innovations coming in each day, and it is impossible to progress in the field of data science without developing and enhancing your skills constantly.

Effective interaction is a key concept for most things in life and also in most jobs as well. Especially in data science, communication skills play a key role. To perform a complex project efficiently while coordinating and communicating effectively is a must requirement for every data scientist.

A data scientist must acquire the ability to listen carefully to the instructions provided by the supervisor, the employee, or anyone else. Once you finish listening, it is essential to process the information and communicate effectively by conveying your statements and thoughts through intense, meaningful, and thoughtful exchanges.

You are also able to guide your teammates, coordinate effectively, and work with your crewmates on the particular task at hand. We will cover more about this part in detail in the upcoming section. We will talk more about teamwork in the next section, which is yet another quintessential necessary.

However, if you are not willing to work on your communication skills, especially in the field of data science, it is impossible to reach greater heights. Having a good sense of communication will help you gauge critical situations better and conduct more civilized actions considering every single retrospective and intricate detail.

The lack of communication skills is something that can be worked on with practice and confidence. So, it is not the end of the world if you are not able to communicate effectively with your peers. This point is one that can be worked on, and you can achieve success. However, this only applies if you are actually interested in self-development.

If you don’t like sharing your ideas or talking to people about newer innovations, or you just hate conversating in general, you are going to have a tough time as a data scientist!

More often than not, big data science projects require a group of functional, active, and effectively data scientists to perform a particular business venture or task with utmost efficiency. It is so essential for them to provide the best service to their employees by producing high-quality models for the specific project.

Working together as a team is significant because there needs to be a consistent exchange of information on the ongoing project. And hence, this work requires data scientists to work collectively to figure out the best possible solutions, improve the model accuracy, and produce top-quality results during deployment.

The ability to process the complex situations of computational tasks and assess the quality that will be produced by various models is extremely important at the industry level. Hence, strong decisions must be made on what are the best choices and best resources available for solving the complex tasks at hand for both you and your team.

It is not impossible to develop some spectacular mind-blowing projects from scratch on your own. You can work as a lone wolf and create some awesome projects! However, speaking more realistically, for bigger projects in either a company or a start-up, a team of individuals needs to coordinate their efforts to develop innovative creations.

So, if you hate working in a group or team, it is probably not the end of the world. Nonetheless, it is an extremely important quality to have to develop bigger projects and be more successful as a data scientist!

This point is a no-brainer.

If you are not interested in working on a multitude of new projects as well as exploring a variety of new things in the spectacular field of Data Science, then are so many other better options that you might like in the world out there. And it would probably be best for you to choose one of them!

An extremely useful attribute that an exceptional data scientist possesses is the quality to solve complex tasks by adapting to the modern or unique techniques for achieving the best possible results as well as being creative to solve the job and finish it with lower space and time complexity, i.e., effectively completing the work with high efficiency as fast as possible consuming the least resources available.

Every task to be solved by a data scientist is unique in its own way, and these complex tasks have various solutions and hence, even the best ways to solve them will differ accordingly. Therefore, adaptability is an essential aspect of producing the best results.

Theoretically understanding the intuition of machine learning concepts and math behind these concepts of data science is crucial. To appreciate the true beauty of data science, you need to try out lots of projects. The tasks that can be achieved and the problems you can solve are absolutely fantastic.

However, you also need to know how you can implement the following projects in a real-life practical scenario. Don’t be afraid to get your hands dirty with some code and implement these projects on your own.

Sometimes companies might require data scientists to work on multiple projects at the same time. Hence, it is significant to have the interest to develop, explore, and learn newer things. Undoubtedly, this point is one of the most significant factors.

If working on newer projects does not really appeal to you, then it is probably not hard to figure out that there are so many better choices and options available out there in the world. Unfortunately, data science is not one of them because developing new projects and exploring is the most quintessential aspect that any successful data scientist must possess.

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