What is Data Science?


  • We may define data science as ” Science of extracting knowledge and insights from data”.
  • It is a field that uses processes, algorithms, and systems to extract data.
  • It is also related to big data and machine learning.
  • There is a huge impact of big data in industries in the modern era and it has changed the shape of industries.
  • Different technologies and techniques are being used for data science for application.
  • Machine learning, clustering, Support vector machine (SVM), logistic regression, and linear regression are the most common techniques of data science.
  • Computer programming languages that are popular for data science are Python, R, and Julia.
  • Similarly, some frameworks which are supporting the data science projects including the tensor flow developed by Google, Pytorch, Jupyter notebook, and Apache Hadoop.
  • Data science software platforms are available which are providing good facilities to handle data science projects.
  • Matlab, Anaconda, Data bricks, Data iku and Rapid Miner are famous data science platforms.
  • Anaconda is free and open-source software that provides distribution for Python and R computer programming languages.
  • “Hey, did you recognize if you have got two houses of an identical size, they have an identical square footage, if the house has three bedrooms, then they cost a lot more than the house of two bedrooms, even if the square for this is the same?”  “Did you know that newly renovated homes have a 15% premium, and if help you make decisions like, given identical square footage, does one want to create a two-bedroom or three-bedroom size in order to maximize value? “ 
  • “Is it worth an investment to renovate a range in the hope that the renovation increases the price you can sell a house for?” The output of a knowledge science project may be a set of insights that will assist you to make business decisions, such as what type of house to build or whether to invest in renovation.