Why is data science widely used?

Revolution of Data Science has changed the world with its significant influence. It is a study of data or information, what they represent, where they are obtained from, and how they can be transformed into a valuable method when formulating business and IT policies. It is considered as the greatest asset of any organization in today’s competitive world.

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

  1. The healthcare industry has benefited from Data Science as it creates down-to-earth treatment issues, diagnostics, patient monitoring, such as clinic administration costs and a general health care cost. It has been a powerful weapon to fight diabetes, various heart disease and cancer.

  2. Computer science offers a huge opportunity for the financial company to reinvent the company. In the field of economics, the application of data science is Automation of risk management, predictable analysis, customer data management, fraud detection, real-time analysis, algorithmic trading, consumer analysis.

  3. In the manufacturing sector, it can be used in many ways as companies need to find the latest solutions and use cases for this data. It has also been beneficial to manufacturing companies as it speeds up execution and generates large-scale process.

  4. The retail area has evolved rapidly. It helps the retailer manage data and create a psychological image of the customer to learn their sore points. Therefore, this trick used by the retailer tends to affect the customer easily.

Types of Jobs Offered in Computer Science.

Demand from people with good skills in this area is high and will continue to increase. Data Science professionals are hired by the biggest names in the business, who tend to pay massive wages to the skilled professionals. Job types include:

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

  2. Business Intelligent Analyst: To check a company’s current status or where it stands, a Business Analyst uses data and looks for patterns, business trends, conditions and comes with a visualization and report.

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

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

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

What are the requirements for being a computer science professional?

In the IT industry, the educational requirements of computer science are staggering. Data Scientist places demands on advanced degrees such as master’s degree, Ph.D. or MBA. Some companies accept a four-year bachelor’s degree in computer science, engineering and hard science, management information system, math and statistics, economics. Data Science resources are also available online, and some education providers also offer online training of the course. These programs concentrate on the technologies and skills required to be a computer scientist such as 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 recognizing specific data. When a data set is given as input using specific algorithms, the machine gives us the desired output.