The Data Daily

Breaking the Data Science Myths for a Better Career

Breaking the Data Science Myths for a Better Career

Data Science is a gift to the modern world. The technology complements the existing data sources by making use of them. Recently, data science is being widely adopted by organizations to make predictive decisions on their behalf.

Data science is a blend of various tools, algorithms and machine learning principles with the goal to discover hidden partners from raw data. The technology is primarily used to make decisions and predictions making use of predictive casual analytics, prescriptive analytics and machine learning. It involves large sets of data with statistical methods to extract trends, patterns or other relevant information.

Data science was not on the league till the 1990s. But since then, the field is widely acting as an attractive platform of AI. Data scientists are always on demand and are highly valued. Harvard calls data scientist profession as the ‘sexiest’ of all. However, it is not easy to become a data scientist, at least in case of breaking the myths that the profession shields before entering it.

Myths are natural. Wherever humans find things attractive, they start gossiping which turns out to be myths that stand blocking the gateway. Data science is no different. Transitions in data science are difficult, but what is more difficult is overcoming the mythical cluster that is created over it. Henceforth, here are the basic myths of data science and some tips to overcome it.

It is no wonder that holding a Ph.D degree is an achievement. The process involves hard work, dedication and a lot of time. But the question here is, ‘Is it compulsory to hold a Ph.D degree if you want to be a data scientist?’

The doubt needs to be clarified by showing the inner circle of the profession. Data scientists play two kinds of roles, applied data science role and research role. The applied data scientists basically apply techniques in their projects. They function with the existing algorithms and understand the way they work. A doctorate is not mandatory for this role.

However, if you look at the research role, it is a creative job where the data scientist is expected to create new algorithms from scratch, research paper, write scientific papers, etc. Here, Ph.D plays a major role. For example, a doctorate in linguistic will help the data scientist set his/her career in Natural Language Processing (NLP).

This is similar to the Ph.D myth. Data science is recently attracting a lot of young minds with its wings in technology. The thing one should think about before believing that a full-time degree in data science is necessary to become a data scientist is that ‘Is it mandatory?’

The answer is ‘not really.’ Practical experience and interest are what makes a data scientist. An aspiring person should find a problem they are passionate about and solve it. He/she should do the same with data science and compare the results. There are a lot of free and paid online courses that could complement the developing data science skill of an aspirant.

This is a vague myth that puts a shadow before an aspirant. A data science seeker may have experience of 5-10 years in a field relatively of the link to the technology. But still, they used to believe that their experience is what matters and not on what domain. Keep away the thought. There are two ways to change your career to data science.

One is to change the domain entirely to get into data science. Here, your resume’s work experience will not be taken into account. You should remember that you are totally switching the sector you are working in. Henceforth, a new data science career chooser is a newcomer in the field. They are valued to a starter in the profession even when they have worked as a software engineer for a long time. A data scientist needs to at least understand the real-world data which will impact the final decision. A company can’t expect that from a newcomer.

The second thing is staying in the same domain and looking for a data science role. This could be taken as a safe heaven. You have time and advantage of knowing the industry better. You will look around all the nuances and will understand the data you are working with. This will make you feel less alien when you step into data science as a professional.

Data science is a platform where a wide range of topics is covered. It is essential to have a pinch of knowledge in everything. Data science has four pillars by which the technology stands.

• Programming- Data scientists should be aware of the program data hierarchies and datasets to code algorithms and develop models.

• Mathematics- Data science involves a lot of mathematical structures which a data scientist should encounter. It is highly essential for modelling experimental designs.

• Computer science- Basic knowledge of computer science is essential to the field as it incorporates coding and devising.

• Communication- Reaching out to the audience is a major task. The wise work and efforts are kept aside, a data scientist should work viable for all audience by telling the story through right visuals and facts to convey the importance of their work.

However, it is not mandatory to be from any of the backgrounds. The whole point here is that a data scientist must poses skills in all these sectors, but necessarily not be an expert. One thing for sure is that people who come from these platforms will perform better. The beginner should understand that they are still learners and need to work hard in order to obtain the place that a person with relevant background guards.

Knowing coding languages like Python and R are important for a data scientist. Data science involves coding in every process starting from data inputs to arranging, filtering, analyzing and visualizing content. However, that doesn’t make it mandatory to hold a good coding skill. It is vice versa as well. Being good at coding doesn’t mean you can shine as a data scientist.

Data science requires a combination of multiple skills. Knowing to program is good, but it is not the centre of the data science spectrum. A data scientist is expected to have two qualities, technical and non-technical or soft skill qualities.

Technical qualities involve understanding the functions of how the data is processed, using an algorithm for scratch, how parameter will impact the final mode, etc. However, soft skills are quite different from this. Problem-solving, structured thinking and communication skills are something that is can’t be taught by anyone. Still, it is valued highly in data science platform.

Aspiring data scientists should be aware of the general misconception that mandatory data science degree, Ph.D, work experience from other fields and essential coding knowledge are needed in data science. It is solely ones hard work and compassion that could drive them to be a good data scientist. Everything is about learning. If an aspirant is passionate and is ready to learn new techniques in data science, then he/she can break any barrier.

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