We are in a technology-driven age and we are always managing the growing needs of businesses and consumers in relation to it. In this scenario, the role of data analysts becomes very dangerous to manage the demands. A data analyst is someone in charge of collecting and analyzing the data, responsible for performing a statistical analysis of the data. It is not essential that the skills of a data analyst be as advanced as a data scientist, a data analyst may or may not create algorithms. Although they share the same goal of discovering data knowledge and using it strategically to create solutions.
Typically, the data scientist working with IT teams, scientific data or management to define the organization’s objectives, extract data, identify new trends and opportunities, design and create databases. Now, these skills are useful when considered as a basis for progressing in various directions in the analytical field.
There are several professional possibilities that can be easily manipulated by a professional data analyst. Whether you are a newcomer to this field, or trying to explore the field of data analysis, and wondering about future options for career advancement, or any alternative to analytical work, this article will help you assess opportunities a field of data analysis.
Professional Data Management
Affiliated with the role of a database administrator, this function is a possibility, but it has nothing in common with the role of the data analyst. It is not necessary to have skill in programming languages like R or Python. SQL guidance is, however, an advantage. This is an IT function, in which the person manages the data and the infrastructure that manages it.
While you, as a data management professional, will manage the data infrastructure, as a data engineer, will design and implement the data infrastructure. A data engineer is a step ahead in the complexity of the data management professional and is a non-analytical career opportunity for Big Data. You cannot say that one of the two is superior, it is your knowledge, skill and preference that should be the deciding factor. Both roles are similar in technology and skills to some extent. However, the application and the complexity of them are different.
If you thrive while working with large data structures, analysis and presentation, creating dashboards, database querying is your forte, then this is the perfect career opportunity for you. The two previous options help you manage data and project data, the business analyst function will extract information from different sources of data that is said and superficially. There are unique skill requirements that can be learned if you wish to continue in this field.
Data research is based on a role as a professional in machine learning, in addition to this ability, should also be a statistical, writing machine learning algorithms, etc., this is where the great data becomes sophisticated and insightful, where tools and experience are used together to leverage the data. Therefore, statistics and programming become essential assets for an auto-learning professional. If these are your interests then the integration of machine learning into technologies will be huge in the coming years.
This term does not mean anything specific in general but uses all the roles and technologies listed above. From fluency in programming languages to query capabilities and statistics, to extraction, management and design, and implementation of the initial exploratory analysis and the choice of machine learning algorithm to be used for predictive analysis of the results visualization until the presentation to the management with the final result, everything is under the labour responsibilities of this role, besides having the domain knowledge.
The options mentioned above are just a few of the possibilities but will serve as a good starting point for anyone exploring to understand the options available to a data analyst.
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