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 | General Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | ||