CODE / DESIGN

HONG KONG





  WELCOME TO  


  our STUDENTS Are individuals 

  who want to  

  •   Learn relevant 21st century skills  

  •   Increase earning potential  

  •   Career change  

  •   Become more marketable  



How it works? 


  Practical, real-world, hands 

  on instruction.  





"General Assembly is an educational institution that transforms thinkers into creators through education in technology, business and design"










GESTALT THEORY

"essence or shape of an entity's complete form"

organize visual elements into groups or unified wholes when certain principles are applied.





   Similarity  

  Continuation 

 






 Closure  



  PROXIMITY  




   Figure/ ground   




  Symmetry and order  







Save the details for last




GESTALT THEORY ELSEWHERE

"essence or shape of an entity's complete form"

organize any kind of elements into groups or unified wholes 
when certain principles are applied.




INFORMATION THEORY

SIMILARITY
CONTINUATION
CLOSURE 
PROXIMITY 
FIGURE & GROUND




APPLICATION ARCHITECTURE

SIMILARITY
CONTINUATION
CLOSURE 
PROXIMITY 
FIGURE & GROUND



 code demo 





COURSE DETAILS






Linear Regression


  MAKE.02 data.one app hackathon  


  Happening this wednesday + weekend  


  make@opendatahk.com  

Qualities

  • Statistical and machine learning knowledge
  • Engineering experience
  • Academic curiosity
  • Product sense
  • Storytelling
  • Cleverness

DATA Science WORK FLOW

  1. Acquire
  2. Parse
  3. Filter
  4. Mine
  5. Represent
  6. Refine
  7. Interact




  DATA  SCIENTISTS  

dATA SCIeNCE USES

  • Stack Overflow tag recommendation and response time prediction
  • Locating ethnic food in ethnic neighbourhoods
  • Building optimal NBA teams
  • Recommending new musical artists
  • Prioritize emergency calls in Seattle
  • Finding the right college for you



Instructors

  • Founder, Open Data HK (2013)
  • FEWD Instructor, GA (2013)
  • Analytical Engineer, Demyst (2013)
  • Data Architect, DAnalytics (2012)


UNIT 1: THE BASICS

  • Python for Data Science.
  • Machine learning (linear models)
  • Data Visualisation


    UNIT 2:     TEXT TO DATABASE

    • Data Acquisition, Manipulation and Preparation
    • MongoDB + JSON
    • API Requests 
    • Python Pandas



    UNIT 3:     SUPERVISED LEARNING

    • Regression Techniques 
      • Regression and Regularisation
      • Logistic regression
    • Classification Techniques
      • Naive Bayes
      • Decision Trees
      • Support Vector Machines


    UNIT 4: real world problems

    • Unsupervised learning
    • Classification Systems
    • Recommendation Systems
    • Decision Systems


      UNIT 5: Your Projects  




    Is it for me?

     Question? 

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