• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IÜE 5039 DATA SCIENCE AND DATA ANALYSIS ELECTIVE 3 0 0 5

Offered By

Production Management and Industrial Business Administration

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR HILMI YÜKSEL

Offered to

Production Management and Industrial Business Administration

Course Objective

To ensure that students have the necessary knowledge and practical experience to be able to take part in projects that require big data and analytics.

Learning Outcomes of the Course Unit

1   To be able to comprehend the concept of data 2   To be able to comprehend the concept of algorithm 3   Learning software packages used in data science and business analytics 4   To ensure that students taking the course have the knowledge and practical experience to immediately participate effectively in big data and other analytics projects.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction and Course Description 2 Data concept and data science, data types 3 Data Analytics in Industry Sectors 4 Introduction to Big Data and Analytics 5 Algorithms in Business Analytics 6 Package programs used in data analysis and business analytics 7 Package programs used in data analysis and business analytics 8 Linear regression models 9 Nonlinear regression models 10 Classification approaches 11 Classification approaches 12 Clustering approaches 13 Clustering approaches 14 Conclusion and Evaluation

Recomended or Required Reading

To be announced.

Planned Learning Activities and Teaching Methods

Power point supported lecture, case study technique

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 3 42 Preparation for midterm exam 1 10 10 Preparation for final exam 1 15 15 Preparing assignments 1 15 15 Preparing presentations 1 3 3 Midterm 1 2 2 Final 1 3 3 TOTAL WORKLOAD (hours) 132

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 LO.1 1 LO.2 1 1 LO.3 1 LO.4 1 1