Graduate Education Institute · Big Data and Business Analytics · Master
Course Objective
Students will learn how to explore new data sets, implement a comprehensive set of machine learning algorithms from scratch, and master all the components of a predictive model, such as data preprocessing, feature engineering, model selection, performance metrics, and hyperparameter optimization.
Course Content
- Predictive Modeling
Regression, Classification, Data Preprocessing, Model Evaluation and Ensembles
- Data Mining
Dimensionality Reduction, Clustering, Association Rules, Anomaly Detection, Network Analysis and Recommender Systems
- Specialty Topics
Data Engineering, Natural Language Processing, and Web Applications
Required Resources
Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 963-974.
Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. Springer
Recommended Resources
Commenges, D., & Jacqmin-Gadda, H. (2015). Dynamical biostatistical models (Vol. 86). CRC Press.
Lavielle, M. (2014). Mixed effects models for the population approach: models, tasks, methods and tools. CRC press.
Course Learning Outcomes
- Discover the different components of a Big Data cluster and how they interact.
- Understand Big Data paradigms
- Understand the benefits of open-source solutions.
- Develop a Big Data project from scratch.
- Learn how to use Spark to analyze data and develop Machine Learning pipelines.
- Understand and implement distributed algorithms.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 0 | 0 | 0 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Assignment | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Final | 0 | 0 | 0 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to Data Science | Lecture Notes |
| 2 | Basic Statistics for Data Science | Lecture Notes |
| 3 | Introduction to Data Processing in Python 1 | Lecture Notes |
| 4 | Introduction to Data Processing in Python 2 | Lecture Notes |
| 5 | Introduction to Data Processing in Python 3 | Lecture Notes |
| 6 | Introduction to Machine Learning 1 | Lecture Notes |
| 7 | Machine Learning Applications with Python 1 | Lecture Notes |
| 8 | Midterm | Lecture Notes |
| 9 | Introduction to Machine Learning 2 | Lecture Notes |
| 10 | Machine Learning Applications with Python 2 | Lecture Notes |
| 11 | Introduction to Machine Learning 3 | Lecture Notes |
| 12 | Machine Learning Applications with Python 3 | Ders Notlari |
| 13 | Introduction to Machine Learning 4 | Lecture Notes |
| 14 | Machine Learning Applications with Python 4 | Lecture Notes |
| 15 | Machine Learning Applications with Python 5 | Lecture Notes |
| 16 | Final | Lecture Notes |