The Importance of Data Science in Everyday Life

The Importance of Data Science in Everyday Life
The Importance of Data Science in Everyday Life

Data is a component of information containing any quantitative or qualitative detail about any object, person, or location. The importance of data can be seen in any organization.

The proper flow of data between the various units of an organization (databases, departments, systems, etc) is essential for the efficient working of the organization. Data helps an institution or firm to enhance their work process through a better understanding of customers, sales, their employees, and overall business thereby reducing the required time, money, and effort.

But, unless data is converted into meaningful information, it is useless. Conversion of unstructured information into valuable data means removing the unwanted bits, transforming it into a readable form, and presenting it in a structured manner. All this is done with the help and knowledge of Data Science.

What is Data Science?

As the majority of organizations around the globe started generating massive amounts of data, called Big Data, the need for data storage and data processing increased tremendously. The need for storage was fulfilled by various frameworks like Hadoop, Spark, Stanza, etc while data science played a key role in the processing of data.

Data Science is the fusion of various fields constituting science, predictive analysis, algorithms, statistics, system tools, and machine learning principles. It is an interdisciplinary branch of science that emphasizes the extraction of knowledge from huge data sets or big data.

It applied over a range of data to find hidden patterns and get valuable insights.

Data Science uses three major factors in making its prediction and concluding a final decision:

  • Predictive analytics: Current and historical data are used to make predictions of performance using modeling techniques and statistics. It looks for recurrent patterns in the data and informs companies about future risks and opportunities. For example, In a restaurant, we can look for various attributes like occupancy, number of employees, day of the week, etc, and predict the waiting time of the customer.
  • Prescriptive analytics: It uses mathematical and technological tools to analyze the raw data. It takes into consideration all the available resources and past and present performances to suggest the best possible outcomes or actions of the situation. For example, in the case of self-driving cars if the prescriptive analysis is run of the dataset containing the directions to a destination, then the car would be able to decide on when to turn.
  • Machine Learning: A model implementing machine learning learns from its past experiences without any external help. It continuously performs better by reusing the data and keeping in account the previous events it encountered. For example, while shopping from e-commerce websites you can see certain products which are recommended for you. These recommendations are based on your search history or the data of other users who bought the same product as you.

All these activities are performed by a Data Scientist.

The role of a Data Scientist is to transform the available raw data and present it in a useful and understandable form. They work in all disciplines of Data Science (including computer science, mathematics, statistics, etc) and try to derive valuable business insights from big data.

The primary responsibilities of a data scientist are listed below:

  • Collecting and Cleaning data
  • Should accumulate data from various internal and external data sources. Since the raw data can contain various unnecessary features, it should be cleaned.

  • Data analysis and processing

Find all possible relevant information in the data by observing various patterns using statistical skills.

  • Understanding business requirement

A data scientist should be thorough with the demands of the business and should transform business goals into data analysis results.

  • Training and deployment of the model

Data flow is represented through data modeling. They should test and train the existing data and then deploy the obtained model on the new data.

  • Documentation, Visualization, Presentation

All methodologies and findings along with metadata should be documented. Data should be visualized for the understanding of the organization and presented before the board.

If you aspire to become a data scientist and are considering building a career in data science then you should check out the Data Science Course in Bangalore at FITA Academy who provides training on concepts of the entire data science lifecycle under expert mentorship.

Why Data Science?

The simple answer for this question is that earlier the organizations used to have small and structured data which the analysts were easily able to analyze using the different business intelligence tools. But in today’s world, more than 80% of data is unstructured.

Also unlike earlier, now the information is collected from various data sources resulting in massive data of different varieties. This kind of data can not be processed using simple business intelligence tools, therefore advanced analytics tools are required which use high-level algorithms for processing.

Importance of Data Science

Over the past few years, data is viewed as a valuable asset that makes data science data generation and the collection has become a critical part of the economy. Data Science helps in facilitating the organizations with the ability to process large volumes of data.

Here are a few reasons why data science is crucial to the industry:

  • Build personal connections with the customers and have a better understanding of customer needs.
  • Allows the companies to know their target audience.
  • Facilities effective use of resources by providing the best possible solution.
  • The findings of data science can be applied in various sectors.
  • Helps several sections of the industry to achieve their goals with minimal trouble.

We have discussed the importance of Data Science in various sectors below.

Healthcare Sector

Data science helps in the interpretation of medical images such as X Rays, MRIs, CT scans, etc. If the medical history of a patient is known, data science can predict future medical health. Diagnosis of various diseases like cancer, schizophrenia, Alzheimer's, etc can be predicted with help of pattern matching and spectrum analysis. It can even provide a better understanding of genetic tissues and the reaction to specific drugs or diseases.

Automation Industry

Data science plays a crucial role in the development of the automation field. The concept of self-driving cars is based largely on data science. The various algorithms of data science ensure a safe drive by allowing a car to acknowledge the objects around it like signals, other cars, directions, etc.

Ecommerce businesses

Various e-commerce websites like Amazon and Flipkart use data science algorithms for a better customer experience. Customers have suggested products based on their previous purchases or, if they buy any of the items which some other customer has purchased, it shows recommendations of those other items.

Tourism Industry

Data Science can give enhanced experience in tourism as well. It can predict flight delays in advance and send messages to the passengers beforehand. It can track any deals or offers on the dynamic pricing of hotels and flights and timely notify the customers about those deals.

Corporate World

It analyzes data to provide valuable insights to the company and enables them to make important data-driven decisions based on facts, statistical numbers, and requirements. The benefits a company can get from data science are reduced budget allocation, attractive advertisements, improved efficiency, short project times, better customer and employee experience, etc.

Finance and Insurance Sector

Data Science algorithms can help prevent fraud in the finance sector. It can check the whole financial history and present situation of a person to predict if he would be able to pay the debt thus minimizing the risk of any financial losses or debts.

Some everyday examples of data science

  • While watching videos on youtube, have you ever noticed that it automatically suggests a list of recommended videos to play next. Data science’s recommendation algorithm tracks our history of the previously watched videos and creates new suggestions based on them. It reduces our effort to manually search for a related video.
  • We have already discussed the use of data science in e-commerce websites. Video streaming platforms like Netflix, Amazon prime videos, Hotstar, etc take advantage of data science like youtube and put forward the shows or movies which are similar to the ones we have already watched.
  • The sports industry also benefits from various algorithms of data science. Since all the data about the mental and physical health of an athlete is available, they can be prepared and coached for the game accordingly.
  • Various virtual voice assistants like Siri and Alexa make use of the natural language processing algorithm to convert voices into texts. They understand the text and perform the desired action, like setting an alarm or calling someone.
  • Health care experts use deep learning algorithms to analyze a cell or group of cells to diagnose a problem in advance and take appropriate action. 

In today’s digital world where billions of tonnes of data are generated every day, data science has become an obligatory requirement. If you wish to pursue a career in data science and desire to be a data scientist, check out the Data Science Course in Chennai at FITA Academy is an extensive course that equips the students immensely on the concepts of Data Science in different fields and its use cases efficiently with real-time training practices.

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