COURSE UNIT TITLE

: DATA ANALYTICS AND MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
END 3523 DATA ANALYTICS AND MINING COMPULSORY 3 1 0 4

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

With this course, it is aimed to explain to DEU Industrial Engineering Department students how data, which has an extremely important place in engineering science and real-life problems, can be used and how information can be produced from data, through data analytics and machine learning methods. With this course, our students will be given basic information about data analytics and machine learning methods. Within the scope of the course, studies will be carried out on data analytics and machine learning approaches, which are the basic sub-topics of artificial intelligence.

Learning Outcomes of the Course Unit

1   To learn about data and analytics
2   To learn about data preprocessing
3   To learn about concepts of machine learning
4   To have the ability of applying fundamental machine learning algorithms
5   To learn about ethics in data analytics

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to data analytics
2 Data types, sample data sets, similarity measures
3 Data preprocessing
4 Data visualization
5 Machine learning: Linear regression
6 Machine learning: Nonlinear, multivariate regression
7 Machine learning: Classification/K-nearest neighbors
8 Machine learning: Classification/Naive Bayes
9 Classification performance evaluation
10 Machine learning: Clustering/K-means
11 Machine learning: Clustering/Hierarchical
12 Machine learning: Artificial neural networks
13 Association rules
14 Dimensionality reduction

Recomended or Required Reading

Witten, Ian H., Eibe Frank, and A. Mark. "Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques.", ISBN: 978-0128042915

Planned Learning Activities and Teaching Methods

Inclass activities and applications

Assessment Methods

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


Further Notes About Assessment Methods

None

Assessment Criteria

25% Midterm + 25% Project + 50% Final Exam

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

burcin.ozsoydan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Tutorials 14 1 14
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing presentations 1 15 15
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 112

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.144
LO.2545
LO.35444
LO.455554
LO.545

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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