Why is data science widely used?

Revolution of Data Science has changed the world with its substantial impact. It is a study of data or information, what it represents, where it is obtained and how to transform it into a valuable method when formulating business and IT policies. All organizations regard it as a most important asset in today’s competitive world.

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

1. Healthcare industries have benefited from data science as it creates problems of treatment, diagnosis, patient monitoring, such as clinic administrative expenses and an overall cost for healthcare. It has been a powerful weapon in fighting diabetes, various heart diseases, and cancer.

2. Data science provides a great opportunity for the finance company to reinvent the business. In finance, the application of data science is the automation of risk management, predictive analytics, customer data management, fraud detection, real-time analytics, algorithmic trading, consumer analytics.

3. In manufacturing, 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 for manufacturing companies, as it speeds up execution and generates large-scale processes.

4. The domain of retail has developed rapidly. It helps the retailer to manage the data and create a psychological image of the customer to know their pain points. Therefore, this trick used by the retailer tends to easily influence the customer.

Types of jobs offered in data science.

The demand for people 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 who are willing to pay massive salaries to qualified professionals. Types of jobs include:

1. Data Scientist – A data scientist is someone who deciphers large amounts of data and extracts meaning to help an organization or business improve its operations. They use various tools, methodologies, statistics, techniques, algorithms, etc. to further analyze the data.

2. Business Intelligent Analyst: To verify the current state of a company or where it is located, a Business Analyst uses data and looks for patterns, business trends, relationships and generates a visualization and a report.

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

4. Data Architect: Data Architect works 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 clustering, decision trees, classification, random forest, etc.

What are the requirements to be a data science professional?

In the IT industry, the educational requirements of data science are hasty. Demand for data scientist positions for advanced degrees like master’s, doctorate or MBA. Some companies will accept a four-year bachelor’s degree in Computer Science, Engineering and Hard Science, Management Information System, Mathematics and Statistics, Economics. Data science resources are also available online, and some educational providers also offer online course training. This training focuses on the technologies and skills required to be a data 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 the recognition of specific data. When a set of data is provided as input by applying certain algorithms, the machine gives us the desired output.