ECTS - Stochastic Processes for Data Science
Stochastic Processes for Data Science (ECON483) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Stochastic Processes for Data Science | ECON483 | Area Elective | 3 | 0 | 0 | 3 | 6 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
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| Course Objectives | The main contents are; 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. |
| 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 | Midterm Exam | |
| 8 | Markov chains | Lecture notes available |
| 9 | Continuous Markov models | Lecture notes available |
| 10 | Martingale techniques and Brownian motion. | Lecture notes available |
| 11 | Point processes | Lecture notes available |
| 12 | Difüzyon Modelleri | Ders Notları |
| 13 | Asymptotic theory of nonstationary variables and Brownian Bridge | Lecture notes available |
| 14 | Asymptotic theory of nonstationary variables and Brownian Bridge | Lecture notes available |
| 15 | Density Functions | Lecture notes available |
| 16 | Fİnal Exam |
Sources
| Other Sources | 1. Ders Notları / Lecture notes available |
|---|
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 1 | 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 | 5 | 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 | They acquire the skills to understand, explain, and use the basic concepts and methods of economics. | X | ||||
| 2 | Acquires macro-economic analysis skills. | X | ||||
| 3 | Acquire microeconomic analysis skills. | X | ||||
| 4 | Understands the formulation and implementation of economic policies at local, national, regional and/or global levels. | X | ||||
| 5 | Learn different approaches to the economy and economic issues. | X | ||||
| 6 | Learn qualitative and quantitative research techniques in economic analysis. | X | ||||
| 7 | Improving the ability to use modern software, hardware and/or other technological tools. | X | ||||
| 8 | Develops intra-disciplinary and inter-disciplinary team work skills. | X | ||||
| 9 | Contributes to open-mindedness by encouraging critical analysis, discussion, and/or lifelong learning. | X | ||||
| 10 | Develops a sense of work ethics and social responsibility. | X | ||||
| 11 | Develops communication skills. | X | ||||
| 12 | Improving the ability to effectively apply knowledge and skills in at least one of the following areas: Economic policy, public policy, international economic relations, industrial relations, monetary and financial relations | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | 1 | 20 | 20 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 10 | 10 |
| Total Workload | 136 | ||