How Data Science is Changing the World?

Data Science is Changing the World

How Data Science is Changing the World : It is an interdisciplinary field that utilizes traditional scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured, as well as unstructured data. Then apply that knowledge across a wide range of application domains. Today, in this blog we will be discussing future of data science with real-world examples of Data-Science in –


->Google, YouTube, and Netflix.

->Manufacturing Industries.

->Healthcare and Agricultural Industries.

->Monitoring Weather Conditions.

->E-Commerce and Virtual Assistants.

And how it is implemented in various industries to increase the productivity of each sector.

Let’s see how it benefitted the most-prestigious organization Google. Google uses the Big data shared by sites and apps to deliver –

  • Personalized Services.
  • Provide new services.
  • Measure the effectiveness of Advertising and Social-Networking.
  • Shielding against fraud and abuse.
  • Customize content as well as Ads visible on Google and on other sites and apps.

Another application is Google Analytics which is a popular web analytics service that visualizes statistics and provides basic analytical tools for search engine optimization (SEO) and efficient marketing purposes.

Basically, what it does is track and understand customers’ behavior, user experience, online content, device functionality, and many more. We need Google Analytics in our eCommerce site or an informative site, to understand the behavior of our visitors to deliver effective results/reports.

With the help of Data-Science developers were able to develop a Workflow Automation System (WAS) for NASA which is extremely fast, cheap, and accurate. 

What is WAS?

WAS is an automated system that enables companies to operate remotely in an organized manner. To monitor how NASA is working with external suppliers and automate various aspects of their workflow operations like-

  • Reviewing workflows of various staff to concentrate on their core mission and achieve optimal productivity.
  • Using Automated systems to eliminate repetitive tasks and nurture extra tasks to be allotted at right time.
  • NASA has implemented WAS across many different workflow processes such as finance, HR, assistance, and assets.

How Data-Science is implemented on YouTube and engage more viewers?

  • YouTube uses Data-Science for recommendations based on previous views and thereby attracts more potential users. 

With the concept of Machine Learning YouTube –

  • Deals with fake news.
  • Tests AI-generated video chapters.
  • Automatically removes inappropriate content.
  • Apply new effects on videos.
  • “Up Next” feature.
  • Trains on depth prediction.
  • Enforce Age-restrictions.


Core concepts of Data-Science help Netflix to gather different forms of data, such as the platform used to watch Netflix, the user’s watch history, search queries, and time spent watching a show. 

But how does Netflix find the user personalized details regarding show reviews?

Well, it does this by collecting varied bits of data from diverse sources, such as demographic data, Google keyword-search analytics, and Browsing history. Resultantly, the consumers are recommended as per their interests.

Data science works in several steps –

  1. Extract required data.
  2. Proceed with data analysis and processing.
  3. Finalize drawing insights/results from the data. 
  4. The futuristic purpose of Data Science is to explore different patterns that reside blocks of data we just fed into the ML model.

Let’s check them out – 😉

Healthcare Industry

An AI system called IDX-DR spots serious cases of patients suffering from chronic diseases in seconds, without the necessity for a Doctor. Similar systems have also been developed to spot age-related diseases like eye cancer, heart-attack predictions, etc.

In the healthcare Industry, Data science is making surprising discoveries-

  • Medical Image Analysis
  • Genetics and Genomics
  • Drug Discovery
  • Predictive Modeling for Diagnosis
  • Fitness bots or virtual helpmates
  1. Medical Image Analysis

In Medical Image analysis, data science detects diseases in the human body by examining X-rays, MRIs, CT- Scans, etc. Previously Doctors had to manually search for suggestions in the medical reports. Data Scientists have created heavy-duty image recognition devices that allow doctors to have an in-depth understanding of complex medical images of brain tumors, skin cancers, retina, and other vital body parts.

  1. Genomic Data Science

Genomic Data Science applies powerful computations to DNA sequences, allowing bioinformaticians and researchers to understand the defects in basic DNA structures. It’s also helpful in classifying complications that are hereditary.

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According to SFI Centre for Research Training claims that Data science simplifies genes’ functionalities by suggesting varying kinds of drugs and helps in fast recovery. 

iii. Drug Discovery

The stages of drug discovery are as follows-

  • Step 1: Discovery & Research Development on how new medications are discovered. 
  • Step 2: Preclinical Research. 
  • Step 3: Clinical Development. 
  • Step 4: FDA Review.
  • Step 5: Post-market Monitoring.

What is Drug Discovery?

  • Target identification finds a gene or protein (therapeutic agent) that plays a significant role in disease. 
  • When identified, therapeutic characteristics are recorded. 
  • Targets are efficacious, safe, usable as drugs, and capable of meeting clinical and commercial requirements. 
  • Data Scientists use data of disease association, bioactive molecules, cell-based models, protein interactions, signaling pathways analysis, and functional analysis of genes to validate targets, and vitro genetic manipulation, antibodies, and chemical genomics. The Sanger Whole Genome CRISPER library and Duolink PLA are excellent sources for drug discovery targets.
  1. Predictive Modeling for Diagnosis

With the advancements in predictive modeling, Data scientists help to enhance the predictions of diseases in the body according to the symptoms and the patient’s medical history given by the data of similar cases.

  1. Natural Language Processing(NLP)

NLP is a technology of Data Science that’s concentrated on the analysis of textual information. Using NLP, we can produce intelligent bots that diagnose or be a query-resolver along with nursing the patients. Bots have been produced that advise patients and give them proper health-related guidelines.

Manufacturing Industries

  • In the 21st century, Data Scientists have acquired a crucial position in the manufacturing Industry. Data Science is being considerably used in optimizing products, reducing costs, and boosting gains.
  • Moreover, with the enhancements of technologies like the Internet of Things (IoT), industries can monitor their energy costs and can also optimize their labor hours.
  • With a thorough analysis of client reviews, data scientists can help the Industry to make better opinions and enhance the quality of their products. Another important aspect of data science in Industrialisation is Automation.

Eradicate effects of Climate Breakdown


Our planet is at its peak to face drastic climate crises. The Intergovernmental Panel on global climate change (IPCC) recently stated that CO2 emissions got to fall by about 45% from 2010 levels to stop the damage to our planet from becoming irreversible.

Empowering the Financial index of world

  • According to the planet Economic Forum, data plays a major role in-
  • Satellite launches to watch global climate change from space and therefore provide a clearer picture of the present condition of the earth. 
  • The chains of knowledge received from these satellites, entangled with info from organizations monitoring deforestation, determining precise solutions to global climate change.


  •  Top Tech-giants like Microsoft, Amazon, Facebook, and Google are supporting analytics programs in Data Science to consume the vital features of the present data.
    • In upcoming years, our world will be far better equipped to enhance agricultural performance, mitigate the danger of major weather events, contain outbreaks of diseases like Ebola, extend anticipation, and lift the general quality of life.

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-> The well-known company Starbucks uses Data-Science Models and generalizations that arise from the buying habits across large consumers worldwide. Perceptivity from this data suggests variations and developments from existing products.

Example – There was a brilliant idea over 15 times ago to introduce pumpkin-seasoned drinks at Halloween which was made possible by the consumer’s data and merchandising histories.


-> Likewise, companies dissect the datasets to identify and predict which immolations will more suit client conditions in the future. With a clear vision of understanding client requirements and prospects to increase their satisfaction position and retention, and as a result, businesses grow and induce profit and fame.


-> Data Science is also used for correlating styles of popular products and predicting their trends.

-> With Data science, companies are optimizing their pricing structures for their consumers.

-> Data Science is also being heavily used in cooperative filtering, where it forms the backbone of advanced recommendation systems.

Also, companies are making use of Sentiment Analysis to dissect the feedback handed by the guests. This makes use of natural language processing to dissect textbooks and online checks.

Fraud Detection, which is the central part of machine literacy in diligence is acclimatized for chancing fraud merchandisers and frauds in line transfers.


Data-Science in the field of Education


Data Science in Education helps the industry to have control over the complete pupil’s data for assessing their performance and taking suitable conduct. This analysis helps thee-learning gates to make the applicable changes that will profit the scholars and educationists in all possible ways to unravel their problems and run the institute well.


Revolutionizing Transportation Sectors

  • Another significant operation of data science is transported. In the transportation sector, Data Science is diligently making its mark in making safer driving surroundings for motorists. It’s also playing a crucial part in optimizing vehicle performance and adding exceptional autonomy to the drivers.
  • Through extensive analysis of power consumption patterns, Automobilist behavior, and active vehicle monitoring, data science has created a strong base in the transport industry. Self-driving cars are the most trending subject in the world here and now.
  • Using a variety of variables like consumer profile, locality, profitable indexes, and logistics, merchandisers can optimize delivery routes and furnish a proper allocation of resources.
  • Also, various transportation companies like Ola and Uber are using Data-Science for price optimization and furnishing better facilities to their Customers. Using important predictive tools, they precisely predict the price based on parameters like rainfall pattern, availability of transport, clients, etc.

Enhancing Agricultural Industry and benefitting Farmers 

Big data provides the grain providers/farmers the essential granular data on rainfall patterns, water cycles, fertilizer requirements, and climatic conditions. This provides them the ability to make smart decisions, like appropriate crops to plant for better profitability and monitor proper harvesting cycles. The correct decisions ultimately improve farm yields and improve fertility and the production of eatables.


24/7 Virtual AI Assistants –

  • Cortana Microsoft’s personal productivity assistant is a by-product of Data-Science and ML that helps you save time and focus attention on what matters most.

Amazon’s Alexa is a voice-based AI-powered digital assistant that empowers a smart-device ecosystem. Alexa can be easily put to work by giving it the commands. This Echo device utilizes speech recognition to perform the tasks given by the user.

Data Type
Data Type

Takeaways for Data Scientists –

  • Launched within the year 2013, the worldwide Data Science for Social Good fellowship hosts a yearly event where data scientists enhance the efficiency of reviews in biomedical research and identify the scholars who struggle in a tutorial environment focused on public welfare, like identifying households with the best need for welfare assistance, etc.
  • In the end, we conclude that Data Science has created a vast impact on all the applications. Several industries like banking, transport, e-commerce, healthcare, and many more are using data science to better their products.

The Sci-fi world is expanding its wings to influence data science to grow a day, overcoming circumstances and solving the planet’s biggest challenges. Hope you liked our curated content!

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