The Student Exchange Program of Dong A University offers four introductory technology courses tailored for non-IT students: Database Management Systems, Machine Learning with Python, Data Science, Anomaly Detection using Machine Learning and Deep Learning for Computer Vision. These courses provide simplified explanations, practical exercises, and accessible tools, enabling students to build essential foundations in data processing, machine learning, and modern AI applications.
I. DATA SCIENCE
Subject title: Data Science
1. Course Description
This course offers an introductory and practical overview of Data Science.Students will learn how data is collected, cleaned, analyzed, and visualized to support decision-making in various fields.The course focuses on real-world datasets, intuitive explanations, and hands-on experience using Python and essential data tools. No advanced programming or mathematics background is required.By the end of the course, students will understand how data-driven insights are created and how basic analytical techniques can be applied to solve problems
2. Course Objectives
After completing this course, students will be able to:
- Understand the data science workflow: data collection, preparation, analysis, modeling, and visualization.
- Use Python to load, clean, and explore datasets.
- Apply basic descriptive statistics to summarize and interpret data.
- Create meaningful visualizations to communicate information clearly.
- Perform simple exploratory data analysis (EDA).
- Understand the difference between correlation and causation.
- Apply basic machine learning techniques for classification or prediction
- Present data-driven insights using charts, summaries, and short reports.
3. Course Modules
| Modules |
Detail |
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Module 1: Introduction to Data Science
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- What is data science?
- Data types, datasets, and data sources
- Overview of common applications in business, engineering, and society
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Module 2: Python for Data Work
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- Introduction to Jupyter Notebook / Google Colab
- Basic Python commands for data manipulation
- Essential libraries: NumPy, Pandas, Matplotlib
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Module 3: Data Collection & Cleaning
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- Importing data from CSV/Excel/web sources
- Fixing missing data
- Removing duplicates
- Data formatting and type conversion
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Module 4: Exploratory Data Analysis (EDA)
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- Understanding distributions
- Detecting outliers (basic approach)
- Analyzing relationships between variables
- Correlation vs. causation
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Module 5: Data Visualization
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- Creating charts with Matplotlib / Seaborn
- Histograms, bar charts, scatter plots, heatmaps
- Choosing the right chart for the right data
- Storytelling with data
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Module 6: Basic Statistical Concepts for Data Science
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- Mean, median, standard deviation
- Basic probability concepts
- Sampling and bias
- Simple hypothesis testing (conceptual)
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| Module 7: Introduction to Predictive Modeling |
- What is a predictive model?
- Train/test split
- Basic models: Linear Regression, Logistic Regression, Decision Trees
- Evaluating simple models
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Module 8: Mini Project (End-of-Course Project)
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- Students work with a real dataset
- Tasks: clean data, analyze patterns, visualize insights
- Final short report or presentation
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4. Course Materials
- Book: Practical Statistics for Data Scientists
II. MACHINE LEARNING WITH PYTHON
Subject title: Machine Learning with Python
1. Course Description
This course introduces students to the fundamentals of machine learning using Python. Learners will explore basic concepts such as features, labels, training, testing, and common machine learning model types. The course focuses on simple, intuitive explanations and practical exercises using accessible tools. No prior programming or mathematics background is required.
2. Course Objectives
By the end of this course, students will be able to:
- Understand basic machine learning concepts and terminology.
- Load and process simple datasets using Python.
- Train basic machine learning models
- Evaluate models using simple metrics and visualizations.
- Use scikit-learn to build and test machine learning pipelines.
- Recognize real-world applications of machine learning.
3. Course Modules
| Modules |
Detail |
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Module 1: Introduction to Machine Learning
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- What is machine learning?
- Types of learning (supervised vs. unsupervised)
- Real-world applications of ML
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Module 2: Working with Data in Python
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- Loading datasets
- Splitting data into training and testing sets
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Module 3: Basic Classification Models
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- k-Nearest Neighbors
- Decision Trees
- Logistic Regression
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Module 4: Regression Models
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- Linear Regression
- Visualizing regression results
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Module 5: Model Evaluation
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- Accuracy, confusion matrix
- Train-test split
- Avoiding overfitting
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Module 6: Machine Learning Pipelines
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- Using scikit-learn pipelines
- Scaling data, feature preprocessing
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Module 7: Model Evaluation & Interpretation
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- Train-test split
- Accuracy vs. precision vs. recall (intuitive explanation)
- Understanding false positives and false negatives
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Module 8: Mini Project
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- Students build a simple classifier or regressor
- Real-world examples across multiple fields
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4. Course Materials
- Book: Introduction to Machine Learning with Python
III. ANOMALY DETECTION
Subject title: Anomaly Detection applying Machine Learning
1. Course Description
This course provides an accessible introduction to anomaly detection - techniques used to find unusual or abnormal patterns in data. Students will learn fundamental concepts, common anomaly types, and basic statistical approaches. In the later modules, the course introduces simple machine learning–based anomaly detection methods to create continuity with the broader program.
2. Course Objectives
By the end of this course, students will be able to:
- Understand the concept and importance of anomalies.
- Identify different categories of anomalies.
- Visualize and explore datasets to spot irregular patterns.
- Apply basic statistical anomaly detection methods.
- Implement simple machine learning–based anomaly detection models.
- Evaluate and interpret anomaly detection results.
- Recognize practical applications across various fields.
3. Course Modules
| Modules |
Detail |
| Module 1: Introduction to Anomaly Detection |
- Why anomaly detection matters
- Point, contextual, and collective anomalies
- Applications: finance, healthcare, IoT, manufacturing
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| Module 2: Data Exploration & Visualization |
- Identifying possible anomalies visually
- Histograms, scatter plots, time-series plots
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Module 3: Statistical Methods
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- Mean, standard deviation
- Z-score method
- Threshold-based detection
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Module 4: Rule-Based & Simple Heuristic Methods
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- Fixed thresholds
- Pros & cons in practical scenarios
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Module 5: Introduction to Machine Learning for Anomaly Detection
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- Why ML can improve anomaly detection
- Supervised vs. unsupervised anomaly detection
- When to use each approach
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Module 6: Unsupervised Machine Learning Methods
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- Isolation Forest
- Local Outlier FactorChoosing thresholds
- Clustering-based anomaly detection (k-Means)
- Hands-on with scikit-learn
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Module 7: Anomaly Detection in Time-Series
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- Basic moving average + anomaly flagging
- Rolling windows
- Detecting unusual spikes
- Applying ML methods to time-series sensors
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Module 8: Mini Project — Unified ML Anomaly Detection
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- Students select a dataset (sensor, transaction, time-series)
- Compare results and discuss use cases
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4. Course Materials
- Book: Introduction to Machine Learning with Python
- Book: Network Anomaly Detection: A Machine Learning Perspective
IV. DATABASE MANAGEMENT SYSTEM
Subject title: Database management system (DBMS)
1. Course Description
This course introduces students to the fundamental concepts of databases and database management systems. Students learn how data is organized, stored, retrieved, and managed in modern information systems. The course emphasizes practical skills such as understanding database structure, designing simple data models, and using SQL to query and manage data in a relational database. No prior background in computer science is required beyond basic computer literacy.
2. Course Objectives
After completing this course, students will be able to:
- Understand what a database is and why organizations use database management systems.
- Recognize the basic components and structure of a relational database.
- Create simple database tables and define relationships between them.
- Use basic SQL commands to retrieve, insert, update, and delete data.
- Read and create simple Entity–Relationship (ER) diagrams for data modeling.
- Apply basic concepts of data integrity, security, and good data management practices.
- Gain awareness of popular database technologies (MySQL, PostgreSQL, cloud databases, NoSQL).
3. Course Modules
| Modules |
Detail |
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Module 1: Introduction to Databases
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- What is data? What is a database?
- Database vs. traditional file systems
- Overview of Database Management Systems (DBMS)
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Module 2: Relational Databases Basics
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- Tables (relations), rows, columns, primary keys
- Understanding relationships between tables
- Real-world examples (e.g., student records, inventories)
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Module 3: Data Modeling
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- Entities and attributes
- Simple ER diagrams
- Converting an ER diagram into database tables
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Module 4: Introduction to SQL
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- Basic SQL SELECT queries
- Filtering, sorting, and joining tables
- Inserting, updating, deleting data
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Module 5: Basic Database Design Principles
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- Why data consistency matters
- Introduction to normalization (only 1NF & 2NF for conceptual understanding)
- Common design mistakes and how to avoid them
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Module 6: Database Tools & Applications
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- Getting familiar with MySQL Workbench
- Creating and managing databases
- Exporting, importing, and backing up data
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Module 7: Overview of Modern Database Technologies
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- Introduction to NoSQL
- Cloud databases
- Applications of Database (mobile apps, e-commerce, social media)
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4. Course Materials
- Book: Database Systems: A Practical Approach
V. DEEP LEARNING FOR COMPUTER VISION
Subject title: Deep Learning for Computer Vision
1. Course Description
This course introduces students to the fundamentals of deep learning and how it is applied to computer vision. Learners will explore how computers interpret images, how neural networks recognize patterns, and how modern techniques such as convolutional neural networks (CNNs) enable applications like image classification, object detection, and facial recognition. The course emphasizes intuitive explanations and practical exercises using Python and beginner-friendly deep learning tools.
2. Course Objectives
By the end of this course, students will be able to:
- Understand the basic ideas behind deep learning and neural networks.
- Explain how computers process and “understand” images.
- Work with image datasets in Python (loading, resizing, simple preprocessing).
- Build simple image classification models using pre-trained neural networks.
- Use transfer learning to train models with minimal code.
- Evaluate model performance and visualize predictions.
- Understand real-world applications of computer vision.
- Identify limitations and ethical considerations in AI and image analysis.
3. Course Modules
| Modules |
Detail |
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Module 1: Introduction to Deep Learning & Computer Vision
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- What is deep learning?
- Why images are challenging for computers
- Overview of computer vision applications
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Module 2: Images as Data
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- Pixels, channels, resolution
- Loading and displaying images in Python
- Basic image preprocessing
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Module 3: Introduction to Neural Networks
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- Intuitive explanation of neural networks
- Layers, weights, activation functions
- What “training” a model means
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Module 4: Convolutional Neural Networks
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- What is a CNN?
- Why CNNs work well for images
- Feature extraction explained simply
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Module 5: Using Pre-Trained Models
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- What is transfer learning?
- Working with common pre-trained models
- Training a small image classifier with few lines of code
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Module 6: Building Simple Computer Vision Models in Python
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- Loading datasets
- Training and testing a simple image classifier
- Visualizing predictions and errors
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Module 7: Introduction to Object Detection
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- What is object detection vs. classification
- Examples of popular models (YOLO, SSD)
- Basic demonstration using pre-trained detection models
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Module 8: Applications, Ethics & Mini Project
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- Applications in daily life: health, retail, security, automation
- Bias & fairness in computer vision
- Mini-project: students build a small image classification demo using transfer learning
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4. Course Materials
- Book: Deep Learning with Python