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He described skills that each of these types of specialists needs to possess, tools they use, and also cited specific examples. There may be other names for these specialists in your company, but it is usually impossible to work effectively with the data without the described skills.
Analysts
This is the broadest and most commonly accepted term, at least in comparison with narrower professional roles, which will be discussed later. In most cases, their experience can be conditionally represented in the form of the letter "T": they have a modest experience in a whole range of skills, but very deep knowledge and skills in their main professional field. Depending on their professional experience, analysts can be both newcomers who mainly deal with collection and preparation of data, as well as highly qualified analysts with specialization in a particular subject. These analysts are often the main experts in various fields, be it customer care, loyalty programs, e-marketing, geospatial military intelligence, or individual segments of the stock market. A specific role in the company depends on its size, maturity, specialization and market.
In any case, result of the analyst's work is likely to be a combination of analysis and reports. Analysts may differ in the degree of proficiency in technical skills and knowledge of the professional field.
Engineers in data processing and analysis
These specialists are primarily responsible for collection and processing of data and their translation into a format suitable for analysis. They are responsible for aspects of operational activities, such as information processing speed, scaling, peak loads and logging operations. In addition, they can be responsible for developing tools that analysts use.
Business intelligence
These specialists usually act as a link between the management (for example, department heads) and the technology department (for example, software developers). Their functions are to improve business processes or help in developing new or improving existing backend systems. For example, such a specialist can be responsible for improving the sales funnel.
Data Scientists
This broad term is used to refer to specialists working with large data, mathematical or statistical knowledge, usually with a higher level of education in the exact sciences, as well as advanced programming skills.
Specialists in Statistics
These are qualified employees who engage in statistical modeling in the company. Usually they have at least the master's degree in statistics, most often they are in demand in such areas as insurance, health care, research and development, public administration.
Quants
Quantitative analysts usually have good mathematical background and work in the financial sector, modeling risk management and stock market movement on the part of both buyers and sellers. For example, a pension fund can hire this professional to form an optimal bond portfolio that can cover the fund’s future liabilities. Former mathematicians, physicists or technical specialists can become quants. Some of them - especially analysts of algorithmic trading (the most highly paid specialists from all analysts) - have confident programming skills in languages such as C ++, they are able to process data and take actions with very little waiting time.
Based on "Creating a Data-Driven Organization" by Carl Anderson
Analysts
This is the broadest and most commonly accepted term, at least in comparison with narrower professional roles, which will be discussed later. In most cases, their experience can be conditionally represented in the form of the letter "T": they have a modest experience in a whole range of skills, but very deep knowledge and skills in their main professional field. Depending on their professional experience, analysts can be both newcomers who mainly deal with collection and preparation of data, as well as highly qualified analysts with specialization in a particular subject. These analysts are often the main experts in various fields, be it customer care, loyalty programs, e-marketing, geospatial military intelligence, or individual segments of the stock market. A specific role in the company depends on its size, maturity, specialization and market.
In any case, result of the analyst's work is likely to be a combination of analysis and reports. Analysts may differ in the degree of proficiency in technical skills and knowledge of the professional field.
Engineers in data processing and analysis
These specialists are primarily responsible for collection and processing of data and their translation into a format suitable for analysis. They are responsible for aspects of operational activities, such as information processing speed, scaling, peak loads and logging operations. In addition, they can be responsible for developing tools that analysts use.
Business intelligence
These specialists usually act as a link between the management (for example, department heads) and the technology department (for example, software developers). Their functions are to improve business processes or help in developing new or improving existing backend systems. For example, such a specialist can be responsible for improving the sales funnel.
Data Scientists
This broad term is used to refer to specialists working with large data, mathematical or statistical knowledge, usually with a higher level of education in the exact sciences, as well as advanced programming skills.
Specialists in Statistics
These are qualified employees who engage in statistical modeling in the company. Usually they have at least the master's degree in statistics, most often they are in demand in such areas as insurance, health care, research and development, public administration.
Quants
Quantitative analysts usually have good mathematical background and work in the financial sector, modeling risk management and stock market movement on the part of both buyers and sellers. For example, a pension fund can hire this professional to form an optimal bond portfolio that can cover the fund’s future liabilities. Former mathematicians, physicists or technical specialists can become quants. Some of them - especially analysts of algorithmic trading (the most highly paid specialists from all analysts) - have confident programming skills in languages such as C ++, they are able to process data and take actions with very little waiting time.
Based on "Creating a Data-Driven Organization" by Carl Anderson