Why do we use Data Science widely?

Revolution of Data Science has changed the world with its substantial impact. It is a study of data or information, what it represents, from where it is obtained and how to transform it into a valuable method when formulating business and IT policy. It is considered as a biggest asset by every organization in today's competitive world.

It is one of the fields that find applications across various businesses, including communication, finance, manufacturing, healthcare, retail, etc.

The healthcare industries have benefited from Data Science as it creates a down-to-earth treatment issues, diagnostic, patient monitoring such as clinic administrative expenses, and a general cost for health care. It has been a powerful weapon for fighting diabetes, various heart disease, and cancer.
The data science provides a huge opportunity for the financial firm to reinvent the business. In finance, the application of data science is Automating Risk Management, Predictive Analytics, Managing customer data, Fraud detection, Real-time Analytics, Algorithmic trading, Consumer Analytics.
In the manufacturing sector, it can be used in a lot of ways since the companies are in need to find the latest solutions and use cases for this data. It has also been beneficial to the manufacturing companies as it speeds up execution and generates large scale processes.
The domain of retail has developed rapidly. It helps the retailer to manage data and create a psychological picture of the customer to learn their sore points. Therefore, this trick used by the retailer tends to influence the customer easily.

Types of Jobs Offered in Data Science.

The demand of individuals with good skills in this field is high and will continue to increase. Data Science professionals are hired by the biggest names in the business that are inclined to pay massive salaries to skilled professionals. The types of jobs include:

Data Scientist: A data scientist is someone who deciphers huge amounts of data and extracts meaning to help an organization or company to improve its operations. They use various tools, methodologies, statistics, techniques, algorithms, and so on to further analyze data.

Business Intelligent Analyst: In order to check the current status of a company or where it stands, a Business Analyst uses data and looks for patterns, business trends, relationships and comes up with visualization and report.

Data Engineer: A data engineer also works with a large volume of data cleans, extracts, and creates sophisticated algorithms for data business.

Data Architect: Data Architect works with system designers, users, and developers to maintain and protect data sources.

Machine Learning Engineer: A machine learning engineer works with various algorithms related to machine learning like clustering, decision trees, classification, random forest, and so on.

What are the requirements to be a Data Science professional?

In the IT industry, the educational requirements of data science are precipitous. Data Scientist position demand for advanced degrees like Master's degree, Ph.D., or MBA. Some companies will accept a four-year bachelor's degree in Computer Science, Engineering and Hard Science, Management Information System, Math & Statistics, Economics. Data Science resources are also available online and some educational providers also offer online training of the course. This training concentrate on the technologies and skills required to be a data scientist like Machine learning, SAS, Tableau, Python, R, and many more.

Machine Learning vs Data Science

Machine Learning is a practice of studying algorithms and statistics and training the computer to perform a specific task for the recognition of specific data. When a set of data is given as input by applying certain algorithms, the machine gives us the desired output.

Source: HOB

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