Note from Intro to Data Science

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Note from Intro to Data Science
  • building a cool products involving math and science

  • Data science knows how to use math and statistic
  • He also has some programming background to accomodate his findings.
  • Furthermore data scientist can make a complicated analysis, based on huge pile of data, and visualize his findings to others
  • M&SK. knows how to give insight in huge pile of data, and has the backround programming skills to interpret and visualize the data.
  • Substantive Expertise important as they now what question that should be asking, and take an analysed based on it. With that they know what is the data, visualization of the data in correct way
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  • Based on Instructors' POV, data scientist extract raw data into dataset that can be analyzed
  • He then given two way, either make models, or prediction, and can be report it to others with those methods
  • Data Science Definition by other Computer Scientist:
    • Pi Chuan -SE Google- Observe dataset, and decide what is the behaviour. Model the data, observe the pattern to gain something useful for further applications.
    • Gabor, Data Scientist, Twitter.Use the data to get particular behaviour, in Twitter, human behaviour.

  • It's important to have substantive expertise as Data Scientist. Like asking the target of the data of particular problem.
  • Often work as a team, even just have Substantive Expertise can benefit to other Data Scientist in team.
  • Data Scientist can be accepted with most startups at Sillicon Valley
  • Netflix: User based movie recommendation systems
  • Social Media: recommend friend, movie, or people to follow on Twitter
  • WebApps : recommend user to improve their experience
  • Not Expected as Data Scientist:
    • BioInformatics: processing bio model like human genome, and data sequences.
    • Urban Planning: Using data of amount people that take on a bus
    • Astrophysicist, collecting astronomical elements
    • Public Health: Target\ting main application of electronic medical record.
    • Sports: Using sensor around the court, to collect data about player's skill and style, to make a better coaching decision.