Ragav Domain

Data Science Areas of Application
The field of Data Science is filled with wonderful applications. In this modern era of digitization, Data Science is making a huge difference in making businesses successful. Not only in business but fields like healthcare, aeronautics, robotics, medicine, etc., Data Science is the game changer. Here is the list of applications of Data Science that you will be introduced to in this blog:

Education
Airline Route Planning
Healthcare Industry
Delivery Logistics
Banking and Finance
Filtered Internet Search
Product Recommendation Systems
Digital Marketing and Advertising

Data science tools
Data scientists must be able to build and run code in order to create models. The most popular programming languages among data scientists are open source tools that include or support pre-built statistical, machine learning and graphics capabilities. These languages include:

R: An open source programming language and environment for developing statistical computing and graphics, R is the most popular programming language among data scientists. R provides a broad variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.

Python: Python is a general-purpose, object-oriented, high-level programming language that emphasizes code readability through its distinctive generous use of white space. Several Python libraries support data science tasks, including Numpy for handling large dimensional arrays, Pandas for data manipulation and analysis, and Matplotlib for building data visualizations.

Data Science
Data science combines the scientific method, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.

What is data science?
Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms, analytics and AI models. It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision-making. The accuracy of these predictions must be validated through scientifically designed tests and experiments. And the results should be shared through the skillful use of data visualization tools that make it possible for anyone to see the patterns and understand trends.