In only 2 years, the role of data scientist has gained traction from various organizations leading to increased employment of data scientists. With the evolution of data scientist roles, organizations are adding structure and detailed definition to the job roles of data scientists so as to make the best of analytics in data science projects. With increasing awareness among organizations on the importance of data science and its related job roles for business growth-understanding the difference between the two broad categories of data scientists is crucial to formulate a successful big data strategy. This will also help prospective data scientists understand where their skills stand and allow them to make a right career choice.
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The phrase “Who is a Data Scientist” is still in its evolving phase. Data scientists come from various backgrounds and they can be accountants, programmers, mathematicians, business analysts, statisticians, visualization experts, machine learning practitioners, data miners, data engineers, etc. A data scientist from a software programming background can specialize in dozens of different programming languages like Python and R for doing data science. Similarly, data scientists from statistics background can specialize in econometrics, mathematical statistics, biostatistics, business statistics, etc. There is not a single data scientist who knows all of these programming languages and uses all the various methods for analysing organizations’ big data to leverage analytics. It is difficult even for the universities to decide on the curriculum for training data scientists with a broader skillset.
There are many people who fling around different terms like data analysis, big data, data mining, data science, data scientists and even some of the experts have trouble defining and differentiating between them. In reality, there are several types of data scientists as there are the number of professionals working in the data science domain. However, we elucidate the two broad categories of data scientists who share similar outlooks, methods, skills and responsibilities. Professionals who fall under these two categories of data scientists share a lot of common traits. In this topology of different data scientists, it is necessary to understand the differences between the two –Type A Data Scientist and Type B Data Scientist that bring value to an organization.
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The A in ‘Type A Data Scientists’ refers to analysis. These kind of data scientists majorly deal with producing meaningful insights from the big data or rather they work with the data in a static way. The role of a Type A data scientist can be matched to that of a statistician but with vast experience in working with data that is not a core part of the statistics curriculum. A ‘Type A Data scientist’ -works with different methods to handle huge data sets, has in-depth knowledge of a particular domain, and is responsible for data cleaning and so on. Type A data scientists are the most common kind of data scientists that we see around.
Type A Data Scientists are the people who have good technical skills required for working on data science projects. They could be predictive modellers, Python or R programming experts, Business Intelligence experts, Statisticians, Machine Learning practitioners, Data miners, etc. These are the data scientists who have in-depth knowledge about their specific subject and can help an organization explore the data using the best methods in their give field.
One need not have a Master’s or PhD to become a Type A data scientist. It is also not necessary to be from a statistics background to call himself/herself, a Data Scientist, however some knowledge about statistics is always beneficial.
Type A Data Scientists can be from a mathematics or statistics background. People from a non-quantitative background can also supplement the role of a Type A data scientist. They just need learn various tools like R programming, SQL, SAS, etc. that can help them with number crunching.
Some of the important skills and tools that a Type A Data Scientist needs to know include - R or SAS, SQL, Data Modelling, Data Warehousing, Data Mining, Data Analysis, Reporting and Database Management.
The job role of Type A Data Scientists can be related to that of a Data Analyst. According to Glassdoor, the national average salary of data analyst is $62,379 but in San Francisco the average salary of a data analyst is $75,833.
The B in Type B Data Scientist refers to building. Type B data scientists share some common statistical grounds with Type A data scientists but they are programming geeks. Type B data scientists mainly work with the big data in production to build models that interact directly with users. The kind of models they build can be serving recommendations for products, listing relevant advertisements based on browsing history, suggesting people who you may know - to add in your friend list, customizing music or movie recommendations, etc.
Type B Data Scientists predict the unknown, by asking questions from different perspectives of the business, writing complex algorithms and developing statistical models. Type B data scientists have proficiency in building their own automation tools and frameworks.
“Some say a data scientist is a statistician who can program, and data science is statistics on a Mac.”- said famous Mathematician, John D Cook
Type B data scientists approach business problems from a different angle. They mainly focus on business goals to identify the kind of data science project they have to work on - to achieve the business objectives with a measurable outcome. Type B data scientists are the go-to-people as they effectively form the communication channel between the business problem and the technical environment required to build models as per business needs.
Type B data scientists prioritise findings from the data so that various business units can act on these findings accordingly. The findings can relate to sources from where good data can be captured, verifying business knowledge beliefs, improving the existing business applications or any other process, that helps businesses make better decisions in the future. Type B data scientists are the data science team leaders who have the capability to manage Type A data scientists to ensure that the data science team in an organization is focused on delivering measurable business benefits.
Type B data scientists can be described as professionals who have statistical and mathematical knowledge along with a taste of hacking skills and possess good substantive expertise.
Some of the important skills and tools for Type B Data Scientists include - expertise in Python and R language, Hadoop, Java, Data Analysis, Object Oriented Programming paradigms, NoSQL, Machine Learning, and Software Development.
According to Glassdoor the average salary of a data scientist is $118,709.
Data Science does not just deal with big data. There is a lot that an organization can achieve with data science, without getting involved with big data. Type B data scientists can help organizations focus on the actual data problem without getting impacted by the marketing hype around data science. If you want to become a data scientist then it is necessary to understand the differences between Type A and Type B data scientists to make better career choices and decisions.
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