What are data scientist job trends
What does a data analyst do?
The tasks of data analysts and Data scientists overlap in many parts. Understanding from the outside why one position is advertised for a data scientist and another for a data analyst is not that easy. This is partly due to the fact that the entire professional field around big data is developing rapidly and new job titles are constantly emerging. However, these are not yet uniformly defined. Therefore, it often just comes down to what a particular company prefers. In addition to many similarities, there are also differences in the job profile of these two professional fields:
1. Who is asking the questions?
As a rule, a data scientist formulates the questions for the company that he would like to answer with his database himself. The data analyst, on the other hand, receives the task from other teams (e.g. from sales or marketing) and looks for a solution to their questions.
2. Bachelor or Master?
Data analysts can start their careers with a bachelor's degree. A master’s degree is usually expected from a data scientist. Because he has to be fit with the models and theories from mathematics, statistics and information technology.
3. What role does machine learning play?
The data analyst must be fit in SQL Queries and Oracle databases, business intelligence tools like Power BI and data visualization like Tableau or Shiny. The data scientist also develops his own Machine learningModels.
Machine learning has become a central part of data science and analysis. Algorithms use data sets as a basis for training to learn new things. The quality of the database is central here, because without a solid foundation, you will not get any meaningful results. As a result, data scientists and data analysts increasingly need technologies, methods and skills that are also relevant in machine learning, including software such as Matlab or programming languages such as Python and R.
The following graphic illustrates the similarities and differences between the two professions. Data analysts specialize in reporting, summarizing and interpreting the data. The data scientist takes care of the extrapolation (i.e. the statistical extrapolation of individual characteristics to e.g. the entire population) and the strategy development (the so-called data prescription).
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