Data Science/ Where we can utilize DATA SCIENCE?

Where we can utilize DATA SCIENCE?

“In the simplest definition of data science is the pulling out actionable insights from raw data or unprocessed data”.

Data science commonly has a five stage lifecycle consisting of:

  1. Capture: Data acquisition, data entry, signals reception and data extraction.
  2. Maintain: Data warehousing, data cleansing, data staging, data processing and data architecture.
  3. Process: Data mining, clustering/classification, data modeling and data summarization.
  4. Communicate: Data reporting, data visualization, business intelligence and decision making.
  5. Analyze: Investigative, predictive analysis, regression, text mining, and qualitative analysis.

Frequently referred to as the “oil of the 21st century,” digital data carries the most importance in every field. It has immeasurable benefits in business, research and our everyday lives. For example your route to work, your most recent Google search for the nearest coffee shop, your FB/Instagfram/Twitter post about what you wear and eat, and even the health data from your fitness tracker or smart watch are all important to different data scientists in different ways. Sifting to the massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering advance insights and making our lives more convenient. 

Data scientists must have expertise in data engineering, statistics, math, advanced computing and visualizations to be able to effectively sift through messed up masses of information and communicate only the most vital bits that will help drive innovation and effectiveness.

Data scientists normally rely on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques. 

Where we can utilize DATA SCIENCE?

  • Anomaly detection (scam, disease, crime, etc.)
  • Automation and decision-making (background checks, credit score, etc.)
  • Classifications (example : in an email server, this could mean classifying emails as “important” or “junk”)
  • Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)
  • Pattern detection (commodity trading, weather patterns, financial market patterns, etc.)
  • Recognition (facial, voice, text, etc.)
  • Forecasting (sales, revenue, targets and customer retention)

Some of the major DATA SCIENCE companies

  • Numerator
  • Cloudera
  • Splunk
  • Alteryx
  • Civis Analytics
  • Sisense
  • Oracle
  • Looker
  • Teradata

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