Data SCIENCE

HONG KONG







Small Data is the real Revolution. 





... you can join from the comfort of your laptop.



  Not centralized "big iron" but  

  decentralized data wrangling.  


  the next decade belongs to distributed

  models, not centralized ones; to  

  collaboration, not control; and to 

  Small Data, not Big Data.


 Rufus Pollock 


  THAT's Why for us  

  DATA SCIENCE  IS  

  about data literacy


  (relevant regardless of size)  


  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.  







  WELCOME TO  



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






  sweet, sweet, data  

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

DATA Science WORK FLOW

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




  DATA  SCIENTISTS  

Qualities

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



  OUR SYLLABUS  


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  


      MAKE.03 Open Gov HACKATHON  


      The first 6 weeks of the course! 


      OPEN DATA HONG KONG  





    COURSE DETAILS



    Instructor

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

    Data science course details

    • Runs from 28 April  to  9 July , 2014
    • Meets Mondays & Wednesdays from 19:00 -22:00
    • Classes held at Garage, Central
    • Expected class size is  ~15 students
    • Tuition is HK$ 28,000
    • Payment plan is available
    • Application deadline on 15 April

    General Assembly Benefits

    • Practical, dynamic content developed by curriculum design team & adapted by local  instructors
    • Free 3 month membership to GA Front Row
    • Final project/portfolio 
    • Permanent access to all course resources
    • Strong, global, diverse community of makers
    • Personalized instruction and support





    BENEFITS




    Is it for me?

    Commitment

    "Data Science involves the programmatic implementation of statistical models"

    • Good grasp of statistics required
    • Some knowledge of Python beneficial
    • 10 hours of pre-course work
    • 4 hours of weekly homework 

    System Requirements


    • Bring Your Own Laptop
    • Linux or OSX are preferred
    • Windows can come too
    • Python v2.7.6
    • Chrome Browser

     Question? 

    Small Data

    By Mart van de Ven

    Made with Slides.com

    Small Data

    Info Session for General Assembly's Data Science course taught in Hong Kong by Mart van de Ven.

    • 1,220

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