COURSE UNIT TITLE

: DATA WAREHOUSES AND BUSINESS INTELLIGENCE

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CME 4434 DATA WAREHOUSES AND BUSINESS INTELLIGENCE ELECTIVE 2 2 0 6

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR RECEP ALP KUT

Offered to

Computer Engineering

Course Objective

Main goal of this course is to understand and implement classical models and algorithms in data warehousing. Students will learn how to analyze the data, identify the problems, and choose the relevant models and algorithms to apply. They will further be able to assess the strengths and weaknesses of various methods and algorithms and to analyze their behavior.

Learning Outcomes of the Course Unit

1   Learn about data warehouses.
2   Learn different DW modeling.
3   Learn the ETL processes.
4   Learn Business Intelligence.
5   Learn how to develop End User Applications in BI

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic Concepts of BI/DW
2 Dimensional Modeling Introduction
3 Dimensional Modeling: Designing the data warehouse
4 General principles for data cleansing in the context of a Customer Relationship Management (CRM)
5 ETL Processing using SQL Server Syntax
6 The ETL Process: Data Preparation and Cleansing
7 The ETL Process: Loading the data warehouse
8 MIDTERM
9 Python ntroduction
10 Functions, Informats and Formats in Python
11 Python code for editing numeric entries
12 Python code for charts and plots
13 Business Intelligence
14 Developing End User Applications

Recomended or Required Reading

Textbook: M. Golfarelli, S. Rizzi. Data Warehouse Design: Modern Principles and Methodologies. McGraw-Hill, 2009.
Supplementary Book:
R. Kimball, "The Data Warehouse Toolkit", 2nd edition.
W. H. Inmon, "Building the Data Warehouse", 3rd edition.

Planned Learning Activities and Teaching Methods

Presentation,
Problem Solving,
Project,
Laboratory

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + PRJ * 0.25 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + PRJ * 0.25 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam is 25 %, project is %25, final exam is 50% of the course grade.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof. Dr. Alp KUT
alp@cs.deu.edu.tr
0232 3017405

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 10 10
Preparation for final exam 1 10 10
Preparing presentations 3 10 30
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 140

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.13
LO.22
LO.323
LO.42
LO.523