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Data Scientist 

A data scientist is a person who is responsible to analyze and interpret complex digital data They work as a mathematician, computer scientist, and trend spotter. They must have knowledge of advanced analytic technologies, including machine learning. Data scientist is mainly needed for better decision making, predictive analysis, and pattern recognition.  

The proper analysis of data plays a major role in the business. That’s why business and government agencies are rushing to hire a data scientist.

Data scientists do data analysis depending on their industry and the specific needs of the business or department they are working for. Business leaders and department managers must communicate what they’re looking for before a data scientist can find meaning in structured or unstructured. So, in this case, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like. The data scientist must have some additional skills in business intuition, analytical thinking, critical thinking, intuitiveness, and interpersonal skills.

Data Scientist Qualification

A data scientist must have a habit of studying, updating, and learning. A data scientist must have the courage to deal with the problem arises in different phase during data analysis. You definitely, need to learn how to program on Scala (the most performative) or R (the most academic and statistical), or Python (the general-purpose). Along with programming, a data scientist required skills in machine learning, data visualization and reporting, risk analysis, statistical analysis and math, effective communication, software engineering skills, data mining, mugging and cleaning, research, big data platforms, cloud tools, and data warehousing and structure.

Data Scientist Job

There is the various job title of a data scientist. They are data analysts, data scientists, data engineers, business intelligence specialists,s and data architecture. The data analyst is one who manipulates large data sets and uses them to identify trends and reach meaningful conclusions to inform strategic business decisions. A data scientist is a person who works to design data modeling processes to create algorithms and predictive models and perform custom analysis. The data engineer is a professional to clean, aggregate, and organize data from disparate sources and transfer it to data warehouses. Identifying trends in data sets are done by business intelligence specialists. Data architects work in designing, creating, and managing an organization’s data architecture. Most people find similarities in data scientists and data analysts.  But data scientists develop processes for modeling data while data analysts examine data sets to identify trends and draw conclusions. Though both the positions may be attainable with similar educational backgrounds, because of the differentiation and the more advanced nature of data science, the role of a data scientist is usually considered to be more senior than that of a data analyst.

According to various business organizations, becoming a data scientist is a highly desirable career path. For five years, Glassdoor ranked data scientists as one of the 10 best jobs in America, based on median base salary, the number of active job openings, and employee satisfaction rates. Furthermore, Harvard Business Review called data science “the sexiest job of the 21st century,” noting that “high-ranking professionals with the training and curiosity to make discoveries in the world of big data” are in major demand.