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Few Data science job roles you must know

A plethora of different roles in data science have been encountered in recent years, and it’s really hard to have a general understanding of the differences between them and the skills they need. To complicate matters, the fact that these different roles of data science are often given different titles, some of which are really imaginative, does not help either.

Fortunately, the labor market for the scientific data industry is in great demand today, which means that one way to better understand the meaning of the different roles of data science is to look in depth at all the offers of data. employment, their description and skill requirements. These lists are often thoroughly detailed and help answer questions such as “How much is the degree of similarity or difference between certain roles?”, “What set of technical skills do I need to master?” And “What is the mentality of I need? I accept this job?

In today’s post, we’ll take a look at this plethora of data science job postings to demystify these cool and fun job titles by comparing different careers related to the Data science.

Data Scientist

One of the most popular job titles that you can proudly display on your business card is that of a data specialist. Data scientists are as rare as unicorns and get to work every day with the mindset of a curious data assistant. They master a range of skills and talents; to be able to manage the raw data, to analyze this data using statistical techniques, to share their ideas with their peers in a convincing way. It is not surprising that these profiles are highly sought after by companies like Microsoft and Google.

Advanced analytics professional

An advanced analysis professional typically conducts simulations, predictive analytics, prescriptive analysis, and other advanced forms of analysis. He/she would be different from data scientists because he/she would not work with exceptionally large data sets or with unstructured data.

Data Analyst

Data Analyst job offers to encompass a wide range of responsibilities, from the creation of systems that enable users to obtain information, to ensure the quality and governance of data, and the performance of data analysis. But the skill sets are similar. Usually, these professionals fall into the same category as advanced analysis and scientific data professionals, since they can all analyze data. However, data analysts can be considered lower-level professionals who are still generalists and can occupy many different roles within an organization.

Data Engineer

Facilitating the work of data scientists and data analysts is what data engineers do; by working quietly behind the scenes. These technology professionals have in-depth knowledge of Hadoop and Big Data technologies such as MapReduce, Hive, and Pig, NoSQL technologies, SQL technologies, and data warehousing solutions.

It can be said that their job is to build the plumbing – data pipelines that clean, aggregate and organize data from different sources, then load them into databases or data warehouses. Data engineers are not the ones who analyze the data. They are the ones who create the software infrastructure that ensures the flow and processing of the data so that the data can be analyzed by other professionals.

Business analyst

Tasks very similar to those performed by data analysts can be performed by business analysts. However, business analysts typically have specialized knowledge of their area of business and then apply that knowledge and analysis specifically to the operation of the business. For example, they could use their analysis to recommend improvements to business processes.

Database Administrator

A database administrator is responsible for everything relating to the monitoring, operation, and maintenance of databases; often SQL or other relational database management systems. Installation, schema definition, configuration, user training and documentation maintenance are their tasks.

Business Intelligence Professional

Those who know how to use OLAP tools, reports, and dashboards to examine historical trends in data sets are business intelligence professionals. Business Intelligence can include data visualization. Qlik, Tableau and Microsoft Power BI are some of the most popular business intelligence platforms.

The Statistician

The historical leader of data and his ideas – the statistician! Replaced by more exotic job titles, although often forgotten; the statistician represents what the field of data science represents – drawing useful lessons from the data.

Armed with solid experience in statistical theories and methodologies, in addition to a logical and statistical-oriented mindset, the statistician collects data and converts it into information and knowledge. Statisticians have the ability to handle all kinds of data. Moreover, thanks to their quantitative background, modern statisticians can master new technologies in no time and use them to improve their intellectual abilities. Statisticians bring mathematics to the table, and their ideas can radically transform businesses.

Data Architect

The importance of a data architect’s work is growing rapidly with the rise of big data. The data architect creates plans for data management systems to integrate, centralize, protect, and maintain data sources. The data architect masters technologies like Pig, Spark, and Hive, and must be on top of every new innovation in the industry.

The Machine Learning Engineer

There are a number of companies for which their data or data analysis platform is their product. If so, data analysis or machine learning can be quite intense. This would probably be the ideal job for someone with a formal background in mathematics, statistics or physics who wants to pursue a more academic path.

“Machine learning engineers would focus more on producing good, data-driven products than they would, giving answers to a company’s operational questions.”

Companies in this group can be consumer-oriented companies with large volumes of data or companies offering a data-driven service. A plethora of different roles in data science have been encountered in recent years, and it’s really hard to have a general understanding of the differences between them and the skills they need. To complicate matters, the fact that these different roles of data science are often given different titles, some of which are really imaginative, does not help either.

Fortunately, the labor market for the scientific data industry is in great demand today, which means that one way to better understand the meaning of the different roles of data science is to look in depth at all the offers of data. employment, their description and skill requirements. These lists are often thoroughly detailed and help answer questions such as “How much is the degree of similarity or difference between certain roles?”, “What set of technical skills do I need to master?” And “What is the mentality of I need? I accept this job?

In today’s post, we’ll take a look at this plethora of data science job postings to demystify these cool and fun job titles by comparing different careers related to the data science. data.

Companies in this group can be consumer-oriented companies with large volumes of data or companies offering a data-driven service.

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