Data Mining

Course Description:
This course introduces undergraduate students to the basic and fundamental concepts of Data Mining (DM) with real-world applications, to practice theoretical concepts with a problem-solving approach. The students will have group-based projects. They will also learn Python programming language and use it for various DM tasks/steps.

Course Info:

  • Lectures in Room#    ; Mondays @9am-11am 
  • Lab#   ;  in Mondays GA @ 11am – 1pm ; GB @ 1pm – 3pm 
  • Office Hours: Thursday 10 AM – 12PM
  • Teaching Assistants/Contacts: Mashhood and Qaydar
  • Appointment (use email): ; emailing Form in Contact page
  • Way of Contact: 
    • Announcement will be on this page.
    • Whenever needed, I will broadcast emails to you. 
    • Make sure your email are working properly and given to me.
    • ​Check your emails and this page frequently! for lecture notes + assignment and lab materials + due dates … etc.

Important Dates (tentative): TBA

  • Midterm : Dec 10th
  • Course Projects (steps):
    • Project proposals due
    • Project review meeting
    • Draft paper/report due
    • Project progress 1st presentations
    • Project progress 2nd presentations
    • Project progress 3rd presentations
    • Project progress 4th presentations
    • (before the final exam): Final Submission Due (paper/report + Code)
    • Project final presentations
  • Final Course Exam: May 20th
  • ​Note: course deadlines are solid/exceptions need solid verification.


1Provide your email in lect#1
Oct. 8th, 2018

Course Schedule:

LTopic(s)Lab WorkNotes
1Course Introduction
​Introduction to
Data Mining 
Lab Policy
Start Python: Environment Setup and
First Program
Get to Know Each Other and Understand the Course
HW for next week: Practice  Python Basics
2Statistics and Algebra: ReviewPython: Strings, Lists,
Grouping for Course Project
HW for next week: Practice  Python Basics
3Data: Knowing, Finding and
Your Data
Python: Dictionaries, Classes,
Explaining Project Requirements and Assigning Projects to Student Groups
HW for next week: Use Python to perform simple statistical analysis on your dataset.
Data Pre
In-Lab Exam on PythonSee Your Data: Performing Simple
Exploratory Statistical Analysis and
Visualization on Project Dataset
Data Mining 1-A: Pattern
Python for Pattern MiningIn-Lab Exploring and Mining Your Dataset.
Data Mining 1-B: Pattern
Python for Pattern MiningMining Your Data
Data Mining 2: ClassificationPython for
Mining Your Data
Python for DEEvaluate Your Models
Python for DVVisual Your Results
10Data at ScalePython for
Big Data
Learn Big Data Concepts

Prerequisites:Review whatever you see you need through out the course…

  • Programming Fundamentals and Data Structures
  • Linear Algebra
  • Statistics and Probability 

​Homework:Students will be provided with weekend assigments and will be graded on the understanding and knowledge of the students for the given week material and earlier material.

Projects:Students will perform a number of individual and group course projects for different Data Mining tasks, like pre-processing, or cluster analysis. 

Grading (tentative) 100%:

  • Homework Assignments: 30% (including a weekly small project 10%)
  • Class Participation/Effort: 5%
  • Midterm #1: 10%
  • Midterm #2: 10%
  • Course Project & Presentation: 25% (presentation counts 10%)​
    • Towards the end of the course you will work on a data-mining project. The goal of the project is to go through the complete knowledge discovery process to answer one or more questions you have about a topic of your own choosing. You will acquire the data, formulate a question of interest, perform the data analysis, and communicate the results.
  • Final Course Exam: 20%
  • NOTE: all submission online.

Books and Other Resourses:

  • Books: 
    • “Introduction to Data Mining (2nd Edition)”, By: Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, (2018)
    • “Data Mining and Analysis: Fundamental Concepts and Algorithm (1st edition)”, By: by Mohammed J. Zaki, Wagner Meira Jr, Cambridge University Press. (2014)
    • “Data Mining: Concepts and Techniques” , Jiawei Han and Micheline Kamber , Morgan Kaufmman Publishers, (2000)
    • “Data Mining: The Textbook”, By: Aggarwal, Charu C., (2015)
  • Datasets: 
    • Kurdistan Region datasets (to be listed) … institutions i.e. statistics from ministry of planning email me any you find​