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 TUE 15:00-15:50, THU 12:00-13:50
Prerequisite CS302 It is a prerequisite to
Lecturer Office Hours / Room / Phone

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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.
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. Lab session with different softwares. Projects that involve a data mining aplication from real life
WEEK TOPIC REFERENCE
Week 1 Introduction to Course Chapter 1
Week 2 Introduction to Data Mining Chapter 1
Week 3 Data Chapter 2
Week 4 Exploring Data Chapter 3
Week 5 Classification: Basic Concepts Chapter 4
Week 6 Classification: Specific Algorithms Chapter 5
Week 7 Review
Week 8 MIDTERM EXAM
Week 9 Association Analysis: Basic Concepts Chapter 6
Week 10 Association Analysis: Specific Algorithms Chapter 7
Week 11 Cluster Analysis: Basic Concepts Chapter 8
Week 12 Cluster Analysis: Specific Algorithms Chapter 9
Week 13 Anomaly Detection Chapter 10
Week 14 Presentation of Projects
Week 15 Presentation of Projects
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 30 1,2,3,4,5,6,7,8,9,10
Semester Evaluation Compenents
Midterm Exam 1 25 1,2,3,4,5
Quizes 3 15 1,2,3,4,5,6,7,8
Homeworks 5 15 1,2,3,4,5,6,7,8,9,10
Term Project/Presentation 1 15 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 Home Study 1 15 15
Homeworks 3 5 15 Homeworks 3 5 15
Active Labs 2 8 16 Midterm Exam Study 15 1 15
Home Study 1 15 15 Home Study 1 15 15
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 04/03/2020
QR Code for https://ecampus.ius.edu.ba/course/cs302-algorithms-and-data-structures

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