Syllabus

Key Points on Syllabus

• Instructor Information and Office Hours
   
• Course Content
   
• Attendance
   
• Homework
   
• Course Project
  -  Proposal
  -  Final report

Instructor Information and Office Hours

• Hui Hu, PhD
   
• Office: CTRB 4224
   
• Email: huihu@ufl.edu
   
• Phone: 352-294-5944
   
• Office hours: By appointment

Tell us

• your name
   
• what program you are in
   
• what programming languages you are familiar with
   
• what data engineering skills you want to learn from this course

Course Content

• Course Overview
  - VM setup, Python crash course
• SQL
  - Basic SQL
  - Data models and relational SQL
  - Many-to-many relationships in SQL
• NoSQL
  - NoSQL databases
  - Access web data
• Different Types of Data
  - Spatial data
  - Text data
  - Image and time-series data
  - Big data

Attendance

• Attendance is mandatory
   
• UF policy for excused absences applies (must notify instructor in writing before class when possible)
   
• Each unexcused absence results in a 1.5% deduction from the final grade
   
• >3 unexcused absences results in failure

Homework

• 6 homework assignments
  -  10% each
  -  the highest 5 grades will count towards the final grade
   
• Often simple programming exercises
   
• Requirements:
  -  turn in assignment no later than 11:59 pm on the day it is due
  -  late assignment will NOT be accepted
  -  no handwritten assignment
  -  DO NOT copy others' work

Course Project

• Requirements:
  -  must include at least 1 non-traditional data source (i.e. spatial data, text data, image data, time-series data)
  -  uses of semi-structured and unstructured data are encouraged
  -  uses of web data accessed by API or scraping are encouraged
   
• Some examples (from last year):
  -  Side Effects and Adverse Reactions to Painkillers: Analysis with FDA Adverse Event Reporting System
  -  Utilizing nontraditional data sources for near real-time estimation of Zika virus case trends during the 2016 Florida USA Zika outbreak
  -  Twitter Mining for Cocaine Use
  -  Medical Marijuana Laws and Change of Number of Tweets towards Marijuana: A Time Series Analysis Using Data from Twitter

Course Project (continued)

• You can work individually or work as a team
   
• If choose to work as a team:
  -  each team can have up to 2 members
  -  clearly delineate roles and responsibilities of each team member
   
• Project Due:
  -  Feb 5, 2018: form a project team
  -  March 12, 2018: midterm presentation and project proposal
  -  Apr 16, 2018: final presentation
  -  Apr 23, 2018: final project report

Midterm

• Project proposal:
  -  Abstract: up to 1 page
  -  Project description: up to 5 pages
      ~ Specific Aims/Objectives
      ~ Background and Significance
      ~ Approach/Research Design
      ~ Timeline
  -  Citations: no page limit, use the Vancouver style
  -  Single column, single spacing; Arial or Times New Roman font; font size no smaller than 11 point; tables and figure labels can be in 10 points; minimum 0.5 inch margins
   
• Proposal presentations:
  -  up to 15 slides
  -  up to 15 minutes presentation with 5 minutes Q&A
  -  send the slides to instructor at least 3 days in advance

Final

• Final Report: up to ten pages (including references)
  -  Abstract: no more than 250 words summarizing the project
  -  Introduction: a short background and objective(s) of the study
  -  Methods: design, setting, dataset, approaches, and main outcome measurements
  -  Results: key findings
  -  Discussion: key conclusions with direct reference to the implications of the methods and/or results
  -  References: please follow the Vancouver style
   
• Final presentations:
  -  up to 15 slides
  -  up to 15 minutes presentation with 5 minutes Q&A
  -  send the slides to instructor at least 3 days in advance
   
• Note: analyses are required, but you should focus more on the data accessing and engineering part.

Grading

• Attendance: 5%
   
• Homework: 50%
   
• Midterm (project proposal and presentation): 15%
   
• Final (project report and presentation): 30%

Virtual Machine Setup

https://ufl.instructure.com/courses/349028/pages/virtual-machine-setup

Python Crash Course

Python vs R

Python 2 vs 3

git clone https://github.com/benhhu/PHC7065SPR2018.git