COURSE UNIT TITLE

: MATHEMATICS FOR DATA SCIENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
VYA 5015 MATHEMATICS FOR DATA SCIENCE COMPULSORY 3 0 0 6

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR SERKAN ARAS

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

Using electronical table is taught for analyzing business problem with quantitative point of view in this course. Applications in finance, marketing and process management are discussed. Case studies about planning, cash flow management, portfolio optimization, supply chain management and other subjects are applied.

Learning Outcomes of the Course Unit

1   1- To be able to realize complicated quantitative analysis.
2   2- To be able to solve complicated problems without hard and unknown mathematical notations
3   3- To be able to use Microsoft Excel Solver, Matlab, Ds for Windows,Pro-model
4   4- To be able to model real world problems.
5   5- To be able to make decision with the help of comparing different scenarios.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1. Introduction to linear algebra: Linear systems of equations, matrices
2 2. Vector spaces, Linear independence, Base and rank concepts
3 3. Norm concept, inner product, angles and orthogonality
4 4. Rotation in vector space
5 5. Matrix Decomposition: Determinant and trace
6 6. Eigenvalue and eigenvector, cholesky decomposition
7 7. Diagonalization
8 8. Singular value decomposition, matrix convergence
9 9. Partial derivatives and gradients, gradients of vector-valued functions
10 10. Gradients of matrices
11 11. High-grade derivatives
12 12. Linearization and multivariate Taylor series
13 13. Final Exam
14 14. Final Exam

Recomended or Required Reading

- Strang, G. (2016). Introduction to Linear Algebra. Cambridge Press.
- Deisenroth, M.P., Faisal, A.A. and Ong, C.S. (2019). Mathematics for Machine Learning. Cambridge Press.
- Stewart, J. (2005). Multivariable Calculus. Thomson Learning.

Planned Learning Activities and Teaching Methods

1- Lecture Method,
2- Demonstration Method with Applications,
3-Determined Cases Discussed Analysis Method

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 * 0.20 + STT * 0.30 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.30 + RST* 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

Ara sınav notu, dönem içi çalışmalar ve Final notunun ağırlıklı ortalaması 75 ve üzeri olmalıdır.

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 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 147

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6
LO.11
LO.21
LO.31
LO.41
LO.5

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

HOME / ABOUT US / DEGREE PROGRAMMES / GENERAL INFORMATION FOR STUDENTS / INTERNATIONAL RELATIONS