Contents
Artificial intelligence
Machine learning [MI]
Cyber security
Coding Python, C++, Java, Javascript, HTML
A LIST OF DATA SCIENTISTS OF DIFFERENT KINDS
Data scientists go under many different names in different organisations. The following section examines various data scientists and the corresponding tasks they perform:
1) Scientists in machine learning
Researchers in machine learning are interested in experimenting with fresh ideas and evaluating novel procedures. They develop algorithms used for demand forecasting, massive data analysis, and suggesting pricing strategies and items.
2) Statistician
To achieve business objectives, statisticians work with both theoretical and applied statistics. The ability to gain knowledge in specific data scientist domains can be extrapolated from the fact that statisticians possess some of the key abilities, such as confidence intervals and data visualisation.
3) Actuarial Scientist
Actuarial scientists occupy a special position because their expertise is dependent on data analysis to gauge and control the outcome. Excellent command of statistical and mathematical techniques is necessary for actuarial science.
4) Mathematicians
Due to their extensive expertise in operational research and applied mathematics, mathematicians are becoming more and more accepted in the corporate sector. Businesses seek their heavenly services to implement optimization and analytics in a variety of areas, including inventory management, supply chain management, pricing algorithms, etc.
5) Data engineers
The task of designing, constructing, and managing the information collected by an organisation falls to data engineers. They are given the task of establishing a data handling infrastructure to analyse and process data by the needs of a company.
6) Programming Analysts for Software
Programming-based calculations are a skill of software programmers. They adopt cutting-edge programming languages like Python and R, which offer data analytics and visualisations.
7) Digital Analytics Advisor
To succeed, a digital analytics expert needs to possess both strong commercial and marketing skills in addition to technological aptitude. Key capabilities include setting up websites to gather data, sending it to analytics tools, and then visualising it through filtering, processing, and developing dashboards.
8) Experts in business analytics
Business analysis is both an art and a science, and one cannot be led by either business savvy or in-depth information gained from data analysis. Business analysts focus on critical decision-making procedures such as dashboard creation, ROI analysis, high-level database design, ROI optimization, etc.
9) Spatial Data Analyst
Spatial engineers are a distinct subset of data scientists that have emerged as a result of the growing use of GPS devices. Spatial data is used by several applications, including Google Maps, Bing Maps, vehicle navigation systems, and others, for navigation, localization, site selection, etc.
10) Quality Engineer
In the manufacturing sector, statistical process control has a connection to quality analysts. Modern analytical technologies have expanded this position, enabling data scientists to create interactive visualisations that are essential decision-making inputs for sectors including business, management, sales, and marketing. (Data Science)
