Mining of Massive Datasets
Information Systems Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Mining of Massive Datasets | BTM548 | Elective | Master's degree | 1 | Spring | 10 |
Name of Lecturer(s)
Associate Prof. Dr. Süleyman EKEN
Research Assistant Seda BALTA
Research Assistant M.M. Enes YURTSEVER
Learning Outcomes of the Course Unit
1) Learns to use massive datasets
2) Uses bigdata frameworks
3) Tests various algorithms on massive datasets
4) Learns to prepare for big data analysis applications
5) Models and analyzes data using selected techniques
6) Develops big data integrated approaches
7) Understands real-time streaming data analytics
8) Works in a team of big data experts
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Learning Outcomes | ||||||||
| 1 | No relation | Middle | No relation | No relation | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 6 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 7 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 8 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Probability, Data structures and algorithms, linear algebra
Course Contents
Data Mining, Map-Reduce and the New Software Stack, Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets, Clustering, Advertising on the Web, Recommendation Systems, Mining Social-Network Graphs, Dimensionality Reduction, Large-Scale Machine Learning
Weekly Schedule
1) Data mining2) Hadoop MapReduce and Apache Spark
3) Finding Similar Items
4) Akan Veri Madenciliği
5) Link Analysis
6) Frequent Itemsets
7) Clustering
8) Advertising on the Web
9) Midterm exam
10) Recommendation Systems
11) Mining Social-Network Graphs
12) Dimensionality Reduction
13) Large-Scale Machine Learning
14) Project presentations
15) Project presentations
16) Final exam
Recommended or Required Reading
1- RAJARAMAN, Anand; ULLMAN, Jeffrey David. Mining of massive datasets. Cambridge University Press, 2011.
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Simulation
6) Case Study
7) Lab / Workshop
8) Self Study
9) Problem Solving
10) Project Based Learning
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
60% |
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Contribution of Final Examination to Course Grade |
40% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required