| 1 |
Lecture |
Introduction to Data Science and Applications in Economics and Business
- Core concepts in data science
- Data analysis vs. data science
- What is modeling?
- Introduction to Python
Class activities
- Introduce course resources and platforms
- Deliver lecture content
- Guide steps in a data science workflow: use Hanoi house prices, Iris classification, or customer search in a marketing campaign
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Chapters 1, 2, 3
Notebook
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In-class discussion
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| 2 |
Lecture |
Python Programming Language
- Variables in Python
- Vectors and sequential data types
- Conditional statements
- Loops
- Functions
Class activities
- Guide installing Jupyter Notebook or other Python execution platforms
- Deliver lecture content
- Demonstrate running Python on SmartDoc or Jupyter Notebook
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Chapter 4
Notebook
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In-class discussion; homework
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| 3 |
Practice |
Python Programming Practice
- Variables in Python
- Vectors and sequential data types
- Conditional statements
- Loops
- Functions
Class activities
- Q&A from lecture
- Guide practice exercises
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Basic Python practice
Notebook
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In-class discussion; homework
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| 4 |
Lecture |
Python Libraries for Data Science
- Data science libraries overview
- NumPy arrays
- Pandas DataFrame
Class activities
- Guide installing Python add-on libraries
- Deliver lecture content
- Support executing example code
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Chapters 5, 6
Notebook
Supplement (PDF)
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In-class discussion; homework
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| 5 |
Practice |
Python with NumPy and Pandas
- Work with NumPy arrays
- Evaluate NumPy performance vs. native arrays
- Practice basic Pandas DataFrame operations
Class activities
- Q&A from lecture
- Guide practice exercises
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NumPy practice; Pandas practice
Notebook
Answer key
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In-class discussion; homework
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| 6 |
Lecture |
Data Input and Storage
- Read and write text formats
- Web data collection
- Read from Microsoft Excel
- Interact with Web APIs
- Interact with databases
Class activities
- Introduce accessible data sources
- Introduce libraries for multi-source ingestion
- Deliver lecture per textbook
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Chapter 7
Notebook
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—
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| 7 |
Practice |
Data Input and Storage Practice
- Read and write text formats
- Web scraping/collection
- Read Excel
- Interact with Web APIs
- Interact with databases
Class activities
- Execute SmartDoc commands to pull data in different formats
- Modify parameters to collect data
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Practice on data input and storage
Notebook
Answer key
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—
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| 8 |
Midterm |
Midterm 1
Class activities
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Answer keys
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Midterm 1
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| 8 |
Lecture |
Data Preprocessing Lecture
- Format data aligned to research goals
- Handle outliers and missing values
- Data cleaning techniques
- Feature engineering
Class activities
- Lecture on data preprocessing
- Demonstrate cleaning techniques
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Chapter 9
Notebook
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In-class discussion
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| 9 |
Practice |
Data Preprocessing Practice
- Clean data, transform to desired formats
- Detect and handle outliers with boxplots, Z-scores
- Handle missing data by deletion, imputation, etc.
Class activities
- Guide practice on data cleaning
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Preprocessing practice
Notebook
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In-class discussion; homework
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| 10 |
Lecture |
Data Transformation and Feature Engineering
- Reshape data between wide and long formats
- Encode categorical variables
- Normalize quantitative variables
- Create new features from raw attributes
Class activities
- Deliver content
- Q&A and examples
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Chapter 8
Notebook
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In-class discussion; homework
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| 11 |
Practice |
Data Transformation Practice
- Reshape wide/long
- Encode categorical variables
- Normalize quantitative variables
- Create new features
Class activities
- Apply practical data transformation techniques
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Data transformation practice
Notebook
Dataset
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In-class discussion; homework
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| 12 |
Lecture |
Data Visualization
- Basic charts: histogram, scatter, bar
- Principles for selecting appropriate charts for one or multiple variables
- Use of color, shape, and size to enhance interpretability
- Interpret data via visualization
Class activities
- Present theory and examples
- Guide data storytelling
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Chapter 10
Notebook
Slides
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In-class discussion; homework
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| 13 |
Practice |
Data Visualization Practice
- Plot with Matplotlib and Seaborn
- Apply visualization in analysis
- Create simple dashboards in Python
Class activities
- Implement illustrative charts
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Visualization practice
Notebook
Answer key
Submit
All exercises
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In-class discussion; homework
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| 14 |
Lecture |
Modeling with Data (Machine Learning)
- Core ML concepts
- Linear models for regression and classification
- Decision trees
- Tree ensembles
- Model evaluation metrics
Class activities
- Supervised learning lecture
- Example: house price prediction or customer classification
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Chapter 11
Notebook
T14 notebook
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In-class discussion; homework
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| 15 |
Midterm & Practice |
Midterm 2 & Machine Learning Modeling Practice
- Midterm Exam 2
- Linear models for regression and classification
- Decision trees
- Tree ensembles
Class activities
- Run scikit-learn models
- Interpret model outputs
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Modeling practice
T15 notebook
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Midterm 2
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