Data Engineering I University of Tsukuba
In this course, the students will learn the basics and recent topics in data engineering. First, the students will review the fundamental technology of database systems, followed by learning major techniques in data mining and graph processing and its related topics. The students will understand basic approaches of data engineering in the area of database and data mining, as well as recent trends in the area, i.e., graph processing. The lecture is given in English.
Acquire basic knowledge of modern data engieering techniques in databases and data mining, and understand current trends in advanced data management, such as graph data management.
Knowledge Utilization Skills, Management Skills, Communication Skills, Research Skills, Expert Knowledge
Students are encouraged to have fundamental knowledge about databases and information retrieval.
Evaluated based on final exam (80%) and quizzes (20%).
This lecture covers basic data engineering techniques and some advanced topics. First, we briefly review database technologies which are the basis of this lecture. Then we pick up major data mining techniques and advanced topics on graph data management. The lecture is given in English.Basic database technologies: We review basic database technologies including relational databases and object databases.Data warehouse and OLAP: We talk about information integration, data warehouse, OLAP, and other releted concepts.Basic concept of data mining, association rules (1): We explain background, purpose, and major techniques of data mining., followed by association rules and Apriori algorithm.Association rules (2): Continuation of (1). We learn FP-Growth algorithm.Clustering: We learn the basic concept of clustering and K-Means clustering, hierarchical clustering, density-based clustering, and the basic approaches for evaluating clustering resultsGraph Data Management (1): We learn advanced topics in graph data management.Graph Data Management (2): Continuation of (1)Introduction to Data Streams: We review an overview of data streams, followed by structures of data streams, stream processing frameworks (e.g., Apache Flink, Apache Spark). kData Stream Mining Algorithms (1): We learn frequent pattern mining in data streams and clustering data streams. Data Stream Mining Algorithms (2): We learn streaming classification and streaming outlier detection.
Online Course Requirement
Amagasa Toshiyuki,Shiokawa Hiroaki,Bou Savong
The lectures will be given on-demand style, i.e., the lecture videos will be available. The course materials will be distributed through manaba.
Site for Inquiry
Please inquire about the courses at the address below.
Contact person: Amagasa Toshiyuki
Email address: firstname.lastname@example.org
Link to the syllabus provided by the university