Welcome to QM 492

Exploratory Data Analysis and Visualization

• Instructor: Mohammad AlMarzouq
• Website: qmisr.github.io/mis492
• Syllabus: bit.ly/mis492_syl
• Slack : cbaqmis.slack.com

Requirements

• Anaconda Python V3.6 - Download here for mac/pc
• Get your cba.ku.edu.kw email
• Join #492 in slack and enable notifications for all messages (see instructions)
• Send @dralmarzouq a message with your ku.cba email to receive datacamp invite
• Signup to datacamp using the sent invite
• Signup for github.com and learn the basics of hosting your code projects on it

Expectations

• Python review in class first week only
  • Datacamp assignment
  • Explain as we go
  • Ask! (class/office hours)
• Attending class will involve lab work
  • Lab work to be submitted via slack for grading
• Weekly assignments, either through datacamp or problems
  • Received via email, or notified in class/slack
• 2 major projects, first is individual, the second is group based

Course Plan:

• 11 weeks of course work.
• 4 weeks to work on your final project

Course Work Plan:

• Week 1: Intro and Python primer.
• Week 2: Overview of Exploratory Data Analysis
• Week 3: Introduction to Pandas
• Week 4: Data cleansing and transformation
• Week 5: Advanced data transformation
• Week 6: Data Visualization

Course Work Plan (cont.):

• Week 7: Multiviriate Visualization
• Week 8 & 9: Review and Midterm
  • For groups and think about topic for final project
• week 10: collecting data from the net
• week 11: Introuction to Text Analysis*
• week 12: Introduction to Network Analysis*
• week 13 to 15: Final project (3 meetings minimum)

* Time permitting

Grade Distribution

• 40% Homeworks and labwork
• 20% Midterm project
• 40% Final project

Projects:

• Midterm project:
  • Design to evaluate skills from week 1 to 7
  • Unified requirements
  • Most likely inclass midterm

Projects (cont.):

• Final Project:
  • Apply learned and self-learned skills
  • Phased:
    1. Proposal (class presentation)
    2. 1st Submission (meeting)
    3. 2nd Submission (meeting)
    4. Final Submission (class presentation)

Recommended resources:

• Think Python: Your reference for the python language (Free online book)
• Learning with Python 3 (RLE): Another textbook for your reference (Free online book)
• Python for Data Analysis: Excellent book on Pandas and data analysis and visualization (Paid)
• Programming Collective Intelligence: Best introductory book to making use of data (Paid)
• Python Online Documentaion: Must always return to this for reference, see tutorial and library reference (Free).