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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| STATISTICS (MASTER) (WITH THESIS) | |
| Code | ISB569 |
| Name | Statistical Modeling and Data Science with Python |
| Term | 2026-2027 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of this course is to provide an understanding of data processing and modeling, as well as the underlying mathematical principles, and to develop the ability to make predictions about the future.
Course Content
The content of this course is python ecosystem, exploratory data analysis, data preprocessing, probability distributions, hypothesis testing, confidence intervals and sampling, linear regression, logistic regression, time series analysis, supervised learning, unsupervised learning, model evaluation, machine learning applications.
Course Precondition
Students are expected to have an understanding of statistics and be able to use Python at a fundamental level.
Resources
T Hastie, R Tibshirani, J Friedman (2009). The elements of statistical learning
Notes
https://www.python.org https://www.anaconda.com
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Uses Python programming language. |
| LO02 | Performs exploratory data analysis. |
| LO03 | Performs data preprocessing steps. |
| LO04 | Solves problems related to probability distributions using Python. |
| LO05 | Performs hypothesis testing using Python. |
| LO06 | Conducts time series analysis using Python. |
| LO07 | Manages model estimation and prediction tasks using Python. |
| LO08 | Applies machine learning algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have in-depth theoretical and practical knowledge about Probability and Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | They have the knowledge to make doctoral plans in the field of statistics. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge about analysis and modeling methods used in statistics. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge of methods used in statistics. | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Make scientific research on Mathematics, Probability and Statistics. | |
| PLO06 | Bilgi - Kuramsal, Olgusal | Indicates statistical problems, develops methods to solve. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply innovative methods to analyze statistical problems. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Designs and applies the problems faced in the field of analytical modeling and experimental researches. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Access to information and do research about the source. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Develops solution approaches in complex situations and takes responsibility. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Has the confidence to take responsibility. | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | They demonstrate being aware of the new and developing practices. | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | He/She constantly renews himself/herself in statistics and related fields. | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Communicate in Turkish and English verbally and in writing. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Transmits the processes and results of their studies clearly in written and oral form in national and international environments. | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | It considers the social, scientific and ethical values in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Uses the hardware and software required for statistical applications. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Python ecosystem: Advanced data manipulation with NumPy and Pandas | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Exploratory data analysis | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Exploratory data analysis 2 | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Data preprocessing | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Probability distributions | Reading sources, research | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 6 | Hypothesis tests | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 7 | Confidence intervals and sampling | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 8 | Mid-Term Exam | Review of the topics covered using lecture notes and other sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Linear regression and logistic regression | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 10 | Time series analysis | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Supervised learning | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Model evaluation | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Unsupervised learning | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Machine learning applications | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Machine learning applications | Reading sources, research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Review of the topics covered using lecture notes and other sources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review of the topics covered using lecture notes and other sources | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 10 | 20 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 139 | ||
| Total Workload / 25 (h) | 5,56 | ||
| ECTS | 6 ECTS | ||