With businesses turning towards data-driven strategies, data scientists have become the most demanded professionals of the industry. Not only data scientists help companies to make informed decisions but also allow them to make effective strategies to sustain this uncertain time. Despite the demand, landing on a job isn’t always a hunky-dory situation for data scientists. In fact, with businesses looking to gain a competitive advantage amid this crisis, companies are mostly hiring experienced professionals with advanced skill sets.

In fact, in a recent interview with Analytics India Magazine, Paulami Das, Principal Data Scientist at Brillio stated that there are numerous professionals in the market. However, very few of them are actually skilled to cater the business needs, and therefore, there is a critical need for professionals who understand the industry.

Although these exceptional skillsets act as a critical differentiator between experienced and amateur data scientists, there are some other factors that clearly make a distinction between the two for companies. Considering businesses need to spend a lot to create a core data science team, it is always critical for them to identify the differences between an experienced and an amateur data scientist. In this article, we will share a few key things that an experienced data scientist would have, but an amateur won’t.

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Abundance Of Practical Knowledge

An important thing for companies to understand is that an amateur data scientist would invest time in learning theories of tools and technologies in their courses, whereas an experienced data scientist would actually know how to apply them in solving business problems. Learning critical theories isn’t always beneficial, instead it’s the application that matters. Such an ability to know the techniques, understand the basics and implement them to solve problems, clearly differentiates a skilled professional from an amateur.

Alongside, the majority of learning for an amateur would rely on online courses, certifications or to the max, via mentorship. However, the learning curve for experienced data scientists would be through their job and the daily work they are doing in their organisation. Experienced data scientists use real-world data sets, and make real analysis that makes a difference in the society, and thus have a better understanding of the working than young professionals. Plus they would also have knowledge of advanced technologies like computer vision, natural language processing, classical ML and DL. However, a junior who is aspiring in the industry or just starting his career would only have expertise over one of them.

Goes Beyond Tools And Technologies

Being in the industry for long, experienced data scientists understand the importance of going beyond tools, libraries and technologies. As a matter of fact, being a data science unicorn requires practitioners to not only understand the technical aspect but also require them to get a grasp of business knowledge. With data being the key for businesses, data science has been involved in the core of the processes, and therefore, having a business acumen has been critical for data scientists. And that’s what will give experienced data scientists a leg up in the industry, where they have acquired soft skills like communication over the years. Experienced data scientists also have a fundamental knowledge of storytelling where they use a combination of data, visuals and narrative to highlight data insights to stakeholders, whereas, amateur data scientists usually lack that, a majority of them have just started playing with data.

Alongside, amateur data scientists usually work on getting the accurate results rather than defining the problem and creating a structured approach. Such an approach will hamper productivity as well as can develop hassles in solving complex business problems that have layers of solutions. On the other hand, experienced data scientists have an ability to break down the problem statement and its parts to create a structured approach, which will not only help them in getting to results faster but will also allow other non-technical folks to understand the procedure.

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More Experience Of The Ground Reality

Data science, in courses and books, is entirely different from data science in the ground reality. Where amateur data scientists would only know the industry via courses, certifications, competitions, and mentorship, an experienced data scientist would know the real working of the field. While studying data science and participating in competitions, amateur data scientists get to work with polished data and with way less complicated problems. However, in real life, data science projects involve way more complexity and more unstructured data to work with. In fact, according to reports, 80% of world data that companies work with are unstructured. Therefore, only an experienced professional can handle such a massive amount of data and bring out necessary insights from the same.

Parallelly, another aspect that distinguishes experienced data scientists from an aspiring one is the ability of data exploration; this step is crucial for data scientists before building a model. However, amateur data scientists usually skip the step of understanding the data, in the excitement of building the model, which, in turn, hampers the productivity and efficiency of the model. But, experienced data scientists usually spend hours understanding and exploring data and present graphical visualisation for his team and other non-technical folks to understand data insights in a better way.

Understand The Importance Of Ethics

Many amateurs believe that AI has been built for obtaining accuracy, and that should always be the key focus of a business. However, with recent discussions in AI ethics, amateur data scientists need to understand that accuracy is not the only aim of organisations. It has now been critical for businesses to practice responsible AI, which has brought the necessity to master for experienced data scientists. Amateur data scientists don’t spend time on understanding the data, and that creates a black box while developing the model, where data scientists and their AI cannot explain the results. On the other hand, experienced data scientists spend good time on data exploration, which helps them in understanding the data and its associated problems. Such a process of data exploration will help these experienced professionals to explain their models, and thus, will be more preferred among business leaders and clients. 
With the recent news, AI in many cases has also been termed biased and racist to people of colour and gender, even in this case, data scientists must work with data that is diverse, and that’s where expertise comes into play. Experienced professionals are skilled in gathering relevant data from the massive amount of sources available in the market. Amateur data scientists, however, lack such knowledge and expertise, as many of them don’t get to practice their hands-on real data sets.