Data Science And Its Applications

Introduction

 
Data Science is a widely-discussed topic nowadays. Data science deals with extracting meaningful data out of data warehouses and carrying out useful interpretations to handle real-world complex problems.
 

What actually is Data Science?

 
Data Science is a data-driven science that involves the following:
 
 
 
Assessing the right question
 
This is the first step among a series of steps involved in Data Science. We are surrounded by a large amount of data; however, we do not always require the whole data but the precise and structured data to solve our purpose. To achieve this task, it becomes extremely significant to know what our real demand or business requirement from huge data is.
 
Processing the data
 
The next step considers processing huge amounts of data with the help of various algorithms and processes. Internet of things or IoT (which includes various devices and tools connected to each other through the internet such as smartphones, PCs, laptops, smartwatches, etc.) are generating a trillion bytes of data and growing continuously at a rapid pace. It is extremely difficult to handle such vast data. There is a need for complex algorithms that are devised carefully to handle advanced queries.
 
It involves various steps
  1. Collecting data from various sources
  2. Cleaning the unnecessary data
  3. Data modeling using various machine learning algorithms
  4. Data validating
Communicating the results with the user
 
After successfully cleaning the data, exploring the data, modeling the data using complex algorithms, and validating it by comparing it with historic results, the next step is to deploy or communicate the results of applying data science to intended users.
 
 
 
Let’s understand the whole process of Data Science with the help of the real-world example of an application
 
Consider an application that records employee’s data such as their personal information, departments, designation, various tasks handled by them, time is taken to complete various modules in a task, daily check-in and check-out time, holidays, salaries, appraisals, and other rewards, monthly and annual targets, their monthly expenditure from an organization’s account on the cafe, tutorials, hiring cabs and so on.
  • Here the first step is to analyze the business requirement of the organization regarding employee data. Let’s say they wish to predict an estimation of the completion date of their specific project.
  • All this information fed into the application acts as data to apply Data Science on it. If there are thousands of employees working at various levels and in different departments performing various tasks, then this data can become really complex.
  • The next step will be to clean the unwanted data such as their holiday record, salaries, appraisals, their monthly expenditure on café and cabs. The usage data may include various modules handled by different employees, time is taken by them to achieve sub-goals in that task, monthly targets achieved, and so on.
  • The next step is to analyze the data using various complex algorithms in machine learning, natural language processing, image analysis that will help in refining your data to get the desired results.
  • The required result can be shown to the user on the dashboard using graphs and tables.
Let’s discuss some of the applications of Data Science
 
 
  • Data Science is very helpful in predictive analysis such as weather forecasting, polls result,s, and vehicular traffic.
  • It is also helpful in doing a cost-benefit analysis. For example, it is widely used by cab service providers to determine the high requirement of cab services in a particular area and hiking the prices according to demand.
  • Data science is widely used in e-commerce to generate traffic on their websites. They track various product searches conducted by the user and provide the advertisement based on a user’s search history. This helps them to attract more customers and earn huge profits.
  • Data Science is significantly used in determining the fastest routes by airline service providers to avoid delays and inconvenience to customers.
  • Data Science is used in smart devices such as smartwatches that track user activities, sleep patterns, heart rates, and breathing activity to generate useful alerts on time such as predicting the risk of a heart attack.