Studies Program

BASIC REGULATIONS:

  1. The program is in accordance with the philosophy of the curricula of European Universities with which the Department cooperates, since it is based on the European Credit Transfer System (ECTS). The basis of this system is the Credit Unit (ECTS). Each course corresponds to a number of ECTS s referred in the program.

  2. To determine each course’s ECTS, the total demands of the course are taken into consideration (lectures, assignments, required preparation, etc)

  3. The student completes his/ her studies and is awarded a degree when he/ she has successfully attended courses corresponding to at least 240 ECTS’s. Credit is either through success in exams or by exemption, or by equivalence as an Erasmus course.

  4. According to the Department's indicative curriculum, each academic year includes educational activities corresponding to 60 academic ECTS’s.

  5. The program offers 14 compulsory courses.

  1. The program’s courses are divided into 2 basic categories:
    1. 14 compulsory courses which must be attended by all of the Department’s students
    2. Optional courses which are of two categories:
      • Courses offered by the Statistics Department
      • Courses offered by other Departments
  2. Compulsory courses are offered during the first 6 semesters (8 in the first year, 4 in the second year and 2 in the third year), so the student establishes the necessary background in order to make his following choices.
  3. In the last two semesters, no compulsory courses are offered. This way the student has the flexibility to form a studies program, which will cover the basic Statistics knowledge (as provided by the compulsory Statistics courses), while at the same time is given the chance to develop a program that meets his individual interests.
  4. During the first two semesters the student may enroll in courses with a maximum of 30 ECTS credits.
  5. From the 3rd to the 6thsemester, the student may enroll in courses with a maximum of 40 ECTS credits per semester.
  6. In the 7th and 8thsemesters the student may enroll in courses with a maximum of 48 ECTS credits per semester.  There can be excess only for the course “Practical Training”.
  7. The same applies after the 4th year of study, as the student may enrol in courses with a maximum of 48 ECTS credits per semester.There can be excess only for the course “Practical Training”.

In particular, the maximum ECTS’s per semesters are displayed in the table below:

Maximum ECTS’s

Year

Winter Semester

Spring Semester

1st’

30 ECTS

30 ECTS

2nd’

40 ECTS

40 ECTS

3rd’

40 ECTS

40 ECTS

4rth’

48 ECTS + Practical Training

48 ECTS + Practical Training

5th and above

48 ECTS + Practical Training

48 ECTS + Practical Training

  1. When the student chooses courses to attend each semester, the obligatory courses of previous semesters which the student has not passed and are offered in the specific semester must precede all other courses.
  2. There are prerequisite courses. “Estimation and Hypothesis Testing” of the 3rd semester is a prerequisite course for “Linear Models” of the 4th Semester. “Linear Models” is a prerequisite course for “Generalized Linear Models” of the 5th Semester as well as “Data Analysis” of the 6th Semester.
  3. Apart from the 14 compulsory courses that amount to 108 ECTS, the student must accumulate at least 72 ECTS from elective courses offered by the Department of Statistics. The remaining 60 ECTS credits, necessary for the degree, can be obtained either from elective courses offered by the Department of Statistics, or by courses offered by other Departments of the University.
  4. The list of the offered courses is announced each year and depends on the availability of the corresponding teaching personnel. Some elective courses may not be offered if there is no available instructor.
  5. By getting the degree, the student can obtain a computer certificate equivalent to ECDL in the public sector, if during his/her studies has successfully attended four of the following courses:

INFORMATICS KNOWLEDGE NECESSARY COURSES

Course Title

Department

INTRODUCTION TO PROGRAMMING USING R

STAT

INTRODUCTION TO PROBABILITY AND STATISTICS USING R

STAT

DATA ANALYSIS

STAT

SIMULATION

STAT

DATABASES

DET or INF

COMMUNICATION NETWORKS

INF

COMPUTER NETWORKS

INF

DATA MANAGEMENT & ANALYSIS SYSTEMS

old title: DATABASE DESIGN)

INF

ARTIFICIAL INTELLIGENCE

INF

MACHINE LEARNING

INF

DATA MINING

INF

INFORMATION RETRIEVAL SYSTEMS INF
  1. Students can enroll in the Teacher Education Program. More information can be found here.
  1. Lastly, the students are given the chance to attend one semester in a similar department in a University abroad through the ERASMUS+ program. The courses that are successfully completed by the student are corresponded to courses of the Department and are included in the student’s analytical total grade. For more information about student mobility you can visit this link. 

PROGRAM STRUCTURE

Α’ Semester

Β’ Semester

  • Probability Ι (C)
  • Calculus Ι (C)
  • Linear Algebra Ι (C) 
  • Introduction to Programming using R (C)
  • Probability  ΙΙ (C)
  • Calculus ΙΙ (C)
  • Linear Algebra ΙΙ (C)
  • Introduction to Probability and Statistics using R (C)

C’ Semester

D’ Semester

  • Estimation and Hypothesis Testing (C)
  • Stochastic Processes Ι (C)
  • Introduction to Mathematical Analysis
  • Bayesian Statistics
  • Introduction to Economics
  • Introduction to Accounting Information Systems
  • ERASMUS BIP: Mixed Mobility for Studies**
  • Linear Models (C)
  • Time Series Analysis (C)
  • Demographic Statistics
  • Sampling
  • Mathematical Methods
  • Actuarial Science Ι

Ε’ Semester

F’ Semester

  • Generalized Linear Models (C)
  • Experimental Design and Analysis
  • Statistical Quality Control
  • Theoretical Statistics
  • Introduction to Operations Research
  • Introduction to Database Management
  • Data Analysis (C)
  • Simulation
  • Multivariate Statistical Analysis
  • Biostatistics I
  • Probability Theory
  • Official Statistics

G’ Semester

Η’ Semester

  • Methods of Statistical and Machine Learning
  • Biostatistics ΙΙ
  • Econometrics
  • Stochastic Processes ΙΙ
  • Actuarial Science ΙΙ
  • Bayesian Inference Methods
  • Special Topics in Probability and Statistics*
  • Bachelor Thesis
  • Practical Training
  • Categorical Data Analysis
  • Advanced Sampling Methods
  • Statistical Methods for the Environment and Ecology
  • Non Parametric Statistics
  • Research Methodology
  • Special Topics in Probability and Statistics 
  • Bachelor Thesis
  • Practical Training

(C): compulsory courses

Notes:

  • The courses noted with a double asterisk "ERASMUS BIP: Mixed Mobility for Studies" will be offered only if there is a Multilateral Inter-Institutional Agreement between Universities with specific choice criteria and for a specific number of students (IKY financing). The student has the right to enroll in the program only once.
  • Elective courses are offered only if there is an available instructor.
  • There are tutorials for all compulsory courses. Tutorials will also take place for elective courses, according to availability.
  • All courses are taught 4 hours weekly, plus 2 hours tutorials (where applicable).
  • Each course examination is determined by the instructor and may involve assignments, exercises, intermediate exams, etc.
  • The student can also choose from a list of elective courses offered by other departments.

The full studies guide for the 2023 - 24 academic year can be found here