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.
- 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): firstname.lastname@example.org ; 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.
|1||Provide your email in lect#1||Oct. 8th, 2018|
Start Python: Environment Setup and
|Get to Know Each Other and Understand the Course|
HW for next week: Practice Python Basics
|2||Statistics and Algebra: Review||Python: Strings, Lists, |
|Grouping for Course Project|
HW for next week: Practice Python Basics
|3||Data: Knowing, Finding and |
|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.
|In-Lab Exam on Python||See Your Data: Performing Simple |
Exploratory Statistical Analysis and
Visualization on Project Dataset
|5||Data Mining 1-A: Pattern |
|Python for Pattern Mining||In-Lab Exploring and Mining Your Dataset.|
|6||Data Mining 1-B: Pattern |
|Python for Pattern Mining||Mining Your Data|
|7||Data Mining 2: Classification||Python for |
|Mining Your Data|
|Python for DE||Evaluate Your Models|
|Python for DV||Visual Your Results|
|10||Data at Scale||Python for |
|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:
- “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)
- Kurdistan Region datasets (to be listed) … institutions i.e. statistics from ministry of planning email me any you find