Most of the quality time of a Data Scientist is spent in data collection, cleaning, and converting the data into valuable business insights. Cleaning the data is one of the most important aspects among them. However, this task needs a detailed understanding of working with data and using various tools and techniques like statistics, computer programming skills, and more. It is important to understand the bias in the data which could be used for the purpose of debugging output from the code.
Once the data is cleansed, then the data exploration part starts wherein the Data Scientist will be converting the data into visual insights through the tools of data visualization. It is all about finding the right patterns, building the optimal model, and having cutting-edge algorithms so as to get clear insight and work with it at a much deeper level.
For a Data Scientist, there is a need to have a very good grasp of mathematical computation, an analytical bent of mind, curiosity, and creative thinking. He/she should be able to discover hidden opportunities, trends, patterns, and more. It all starts with asking the right question, connecting the dots, and searching for the right answer from various results available. He/she should be able to devise the right model and computer algorithms that can answer the most pressing business questions. A big majority of Data Scientists have a master’s degree, and nearly half of them have PhDs. Being able to think like an entrepreneur is also part of the job skill.
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Two of the most important programming languages that a Data Scientist is supposed to know are R and Python. Most of the time, the Data Scientist has to work in an interdisciplinary team consisting of Business Strategists, Data Engineers, Data Specialists, Analysts, and other professionals. Most of these other roles work as a supporting panel to the Data Scientist. The Data Scientist should be able to devise his own methodologies. He/she should slice and dice data and come up with value addition through the use of algorithms. He/she should also know how to visualize the data through data visualization tools and more.
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What are the various job roles in Data Science?
Data Scientist
This is the role that includes understanding the statistical and mathematical models in order to apply them to the data. They apply their theoretical knowledge in the domains of statistics and algorithms to find the best way to solve a certain problem. Also, know about Data Science job profiles and build your career in Data Science.
There are Data Scientists who fine-tune the statistical and mathematical models that are applied to data. When somebody is applying their theoretical knowledge of statistics and algorithms to find the best way to solve a Data Science problem, they are filling the role of Data Scientist. The Data Scientist is able to build a data question into a business proposition, solve the business problem, create the predictive models, answer the pressing problems that the business is facing, and do a little bit of storytelling when it comes to manifesting the findings.
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When Statisticians are able to create statistical models and implement them to approach the data to parse it, Data Scientists are able to bridge between the computer programming and those that take the business decision, convert the theory into practical knowledge, and apply it for solving real-world business problems.
Some of the skills needed by a Data Scientist here include a thorough knowledge of statistics, mathematics, and complete knowledge of various computer programming languages. He/she should be able to ask the right questions and structure the data problem so that it can be solved and the results can be communicated to the right stakeholders in the organization.
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Data Engineer
One of the most important differences between a Data Scientist and a Data Engineer is that Data Engineers are able to handle large amounts of data using their excellent software engineering and programming skills. Thus, they are more often than not concentrating on coding, cleaning the data that is available, and working in close coordination with Data Scientists. If a Data Scientist is taking the predictive model and implementing the code, then they are in effect taking on the role of a Data Engineer.
Data Architects are professionals who are well adept at coming up with the data model. They are database administrators focusing on structuring the technology, implementing the data storage problems, and working in close coordination with the Data Engineers.
Some of the skills that are needed for a Data Engineer are to have a knowledge of data storage and data warehousing skills and an understanding of SQL and NoSQL. They should also be adept at other Big Data frameworks like the Hadoop or Apache Spark in order to gather data from various sources, and they should process big data and derive meaning from it.
Data Analyst
Data Analyst is another important role that falls under the category of Data Science. This role includes the aspect of analyzing the data and creating reports and other compelling visualizations in order to help others easily understand the analysis that has been done. If a Data Scientist helps other people in the organization by creating good charts, maps, etc., then they are in effect fulfilling the role of a Data Analyst.
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The role of a Business Analyst comes within the purview of the Data Analyst job role. The Business Analyst is more concerned with the business implications of a data analysis process. It is more about giving the right data-driven implication of showing which is the best path forward for any organization, like choosing between path A and path B. The Data Analyst is supposed to know about data manipulation using various tools like MS Excel and communicate the findings through the right visualization.
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