CS417 Introduction to Data Mining

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
CS417 Introduction to Data Mining 3 2 6 Thursday 15:00-17:50
Prerequisite CS302 It is a prerequisite to

None

Lecturer Emine Yaman Office Hours / Room / Phone
Monday:
12:00-15:00
Wednesday:
12:00-15:00
Thursday:
10:00-12:00
B F2.7C
E-mail eyaman@ius.edu.ba
Assistant Assistant E-mail
Course Objectives The aims of this course are to presents to students well-known data mining techniques and their application areas. Specifically, the course
demonstrates basic concepts, principals and methods of data mining. It also demonstrates the process of Knowledge Discovery in Databases (KDD) and presents a review of available tools.
Textbook Introduction to Data Mining, Pang Ning Tan, Michael Steinbach, Vipin Kumar, Pearson, 2005.
Additional Literature
  • Data Mining: The Textbook, Charu C. Aggarwal Hardcover, Springer.
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Deal with data issues that will be need for successful application of data mining
  2. Demonstrate knowledge of statistical logic of data mining algorithms
  3. Apply knowledge in database technologies which is necessary in data mining apps
  4. Apply pre-processing, transformation and interpretation methods for given data
  5. Apply clustering, association rules and classification algorithms
Teaching Methods Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Home assigments. Projects that involve a data mining aplication from real life
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
WEEK TOPIC REFERENCE
Week 1 Introduction to Course Chapter 1
Week 2 Introduction to Data Mining Chapter 1
Week 3 Data, Implementation of Real Life Examples in Weka, Python Chapter 2
Week 4 Exploring Data, Implementation of Real Life Examples in Weka, Python Chapter 3
Week 5 Classification: Basic Concepts Chapter 4
Week 6 Classification: Specific Algorithms, Implementation of Real Life Examples in Weka, Python Chapter 5
Week 7 Association Analysis: Basic Concepts Chapter 6
Week 8 MIDTERM EXAM
Week 9 Association Analysis: Specific Algorithms, Implementation of Real Life Examples in Weka, Python Chapter 7
Week 10 Cluster Analysis: Basic Concepts Chapter 8
Week 11 Cluster Analysis: Specific Algorithms, Implementation of Real Life Examples in Weka Chapter 9
Week 12 Anomaly Detection Chapter 10
Week 13 Implementation of Real Life Examples in Weka,Python in details
Week 14 Presentation of Projects
Week 15 Presentation of Projects
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 35 1,2,3,4,5,6,7,8,9,10
Semester Evaluation Components
Midterm Exam 1 30 1,2,3,4,5
Homeworks 5 15 1,2,3,4,5,6,7,8,9,10
Term Project/Presentation 1 20 1,2,3,4,5,6,7,8,9,10
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Homeworks 5 5 25
Home Study 1 15 15 Midterm Exam Study 20 1 20
Final Exam Study 25 1 25 Term Project/Presentation 20 1 20
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 09/11/2023
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