ECTS - Stochastic Process for Data Science

Stochastic Process for Data Science (ECON554) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Stochastic Process for Data Science ECON554 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language Turkish
Course Type N/A
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
  • Dr. Dersin Öğretim Üyesi
Course Assistants
Course Objectives The goal of lectures is to introduce statistical inference for time series taking into account both the theoretical/mathematical aspects and their practical application to data analysis.
Course Learning Outcomes The students who succeeded in this course;
  • Upon the completion of this course, the student will be able to: Define and analyze the data structure by using the mathematical tools;
  • use mathematical models and solve for equilibrium.
Course Content Essentials of stochastic integrals and stochastic differential equations; probability distributions and heavy tails, ordering of risks, aggregate claim amount distributions, risk processes, renewal processes and random walks, Markov chains, continuous Markov models, Martingale techniques and Brownian motion, point processes, diffusion models, and applications in various subject related data science.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Stochastic integrals and Stochastic differential equations Lecture notes available
2 Probability distributions and heavy tails Lecture notes available
3 Ordering of risks Lecture notes available
4 Aggregate claim amount distributions Lecture notes available
5 Risk processes Lecture notes available
6 Renewal processes and random walks Lecture notes available
7 Markov chains Lecture notes available
8 Markov chains Lecture notes available
9 Martingale techniques and Brownian motion. Lecture notes available
10 Point processes Lecture notes available
11 Diffusion models Lecture notes available
12 Asymptotic theory of nonstationary variables and Brownian Bridge Lecture notes available
13 Density Functions Lecture notes available
14 Fınal Exam

Sources

Course Book 1. Ders Notları / Lecture notes available

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation 14 10
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation 2 20
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 50
Toplam 18 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 To compare main theories and/or approaches in political economy and make a critical evaluation of each X
2 To compare main macroeconomic theories and/or approaches and make a critical evaluation of each X
3 To use complementary approaches from other relevant disciplines (e.g. political science, sociology) in order to solve problems requiring scientific expertise
4 To develop the skills for establishing a micro-macro link in human and social sciences X
5 To analyze the main economic indicators and comment on them X
6 To acquire theoretical knowledge through literature survey and derive empirically testable hypothesis X
7 To be able to develop new approaches/theories for complex problems in political economy
8 To apply critical thinking, statistical/econometric tools or other relevant quantitative and qualitative tools to new areas/problems X
9 To make a research design and carry it out within predetermined time frames X
10 To formulate and present policy recommendations based on academic research X
11 To continue learning and undertake advanced research independently X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 14 3 42
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration 1 21 21
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 20 20
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 150