What is Data Science? Why study Data Science?

“Data Science is extraction of knowledge from data”.

“By combining aspects of statistics, computer science, applied mathematics, and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge”.

industry analytics

Data Science gives insights across almost all industries – from retail to government to biotech. Lets have a look on industry-wise use of data science –

  1. Government –
    • Prevent waste, fraud and abuse
    • Combat cyber-attacks and safeguard sensitive information
    • Use business intelligence to make better financial decisions
    • Improve defense systems and protect soldiers on the ground
  1. Internet –
    • Personalized recommendations based on weather, seasonal trends, traffic reports, past purchase history, your dog’s favorite chew toys…
    • Smarter sentiment analysis
    • Product insights gleaned from RFID and sensor data
    • Detailed market basket and video analysis
    • Geo-targeted marketing
    • Real-time pricing and inventory management
  1. Healthcare –
    • Clinical trials
    • Direct observations of other physicians
    • Electronic medical records
    • Online patient networks
  1. Finance –
    • Running live simulations of market events.
    • Build algorithms around market sentiment data that can short the market when disasters occur
    • Track trends, monitor the launch of new products, respond to issues and improve overall brand perception
    • Detect financial fraud
    • Improve credit ratings.
    • Provide more accurate pricing
  1. Biotechnology –
    • Bioinformatics tools for studying biological data
    • Genomics research
    • DNA sequencing
  2. Retail –
    • Sentiment analysis of social media streams
    • Predictive analytics
    • Real-time pricing using “second by second” metrics
    • Tailored offers through online behavioral analysis and web analytics
    • Improved, real-time inventory tracking and management
    • Route optimization and more efficient transportation
  1. Telecom
    • Combine variables to predict the likelihood of change
    • Sentiment analysis of social media to detect changes in opinion
    • Personalized promotions based on historical behavior
    • React to retain customers as soon as change is noted
  1. Manufacturing
    • Predictive  model equipment failure rates
    • Streamline inventory management
    • Target energy-inefficient components
    • Optimize factory floor space
  1. Insurance
    • Personalized Risk Pricing
    • Property Insurance – Moisture sensors that detect flooding or leaks, Utility and appliance usage records
    • Life and Health Insurance – Transactional data – e.g., where and what (junk food?) customers buy, Body sensors – i.e., devices that monitor consumption or alert the wearer to early signs of illness, Exterior monitors – e.g., data from workout machines, Social media – e.g., tweets about one’s personal health or state of mind
  1. Travel and Transportation
    • Behavioral targeting – e.g., website visitor behavior
    • Social media – e.g., posts on travel, reviews from friends
    • Location tracking records
    • Previous purchase patterns
    • Mobile device usage data
  1. Pharmaceutical
    • Drug Discovery
    • Avoiding Negative Outcomes: Predictive modeling can also be used to short-circuit potential disasters such as deaths from risk factors.
    • Data-Based Patient Selection:  identify which populations would work best in trials.
    • Real-Time Monitoring: Companies now monitor real-time data from trials to identify safety or operational risks and nip problems in the bud.
    • Drug Safety Assurance
    • Sophisticated Sales
    • Patterns in drug-drug interactions
  1. Utilities
    • Real-time sensor data
    • Operating history
    • Technician notes
    • Predictive models (e.g., expected earthquake effects)
    • Public datasets (e.g., weather reports)
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