Course Description
This course explores the integration of economics with data science methodologies, mastering tools and techniques for real-world analysis. Delve into causal estimation methods and data analysis in policy contexts, leveraging machine learning and AI for insightful interpretations. Engage in a collaborative project, culminating in poster presentations.
Course Prerequisites
A solid foundation in Basic Econometrics is required. Familiarity with Python will be beneficial but is not mandatory.
Measurable Learning Outcomes
- Master Data Science Tools: Gain proficiency in using data science tools for analyzing economic and social data.
- Understand Economic Policy: Develop the ability to critically analyze economic policies using advanced data science techniques.
- Apply Causal Estimation Methods: Enhance skills in robust economic analysis through causal estimation methods.
- Integrate Machine Learning and AI: Learn to implement cutting-edge AI and machine learning tools in economic research.
- Execute Data Preprocessing and Visualization: Master the art of preprocessing and visualizing data for impactful communication.
- Conduct Applied Research Projects: Apply theoretical insights to practical scenarios, showcasing collaborative research and presentation skills.
Textbooks and Resources
No required textbook. Recommended readings include "Data Science with Generative AI for Economic and Social Issues" by M. Jahangir Alam, “Econometric Data Science” by Diebold, “Mostly Harmless Econometrics” by Angrist & Pischke, and “Applied Econometric Time Series” by Enders.
Explore additional course contents like quizzes and problem sets:
Empirical Research Databases
Access an array of databases to enrich your empirical research:
Financial Databases
- Yahoo Finance: Access comprehensive financial information, including market data and stock prices.
- Compustat: Delve into detailed financial and market data for deep quantitative analysis.
Labor and Population Data
- Current Population Survey (CPS): The go-to source for labor statistics in the U.S., conducted by the U.S. Census Bureau for the Bureau of Labor Statistics.
Macroeconomic and International Data
- Federal Reserve Economic Data (FRED): Explore a comprehensive collection of economic data from various national and international sources.
- International Monetary Fund (IMF) Data: Access global financial data, including balance of payments and international financial statistics.
- OECD Data: Find a wide range of statistics and data from the Organisation for Economic Co-operation and Development.
- World Bank Open Data: Free and open access to a wealth of global development data.
- Penn World Table: National income accounts converted to international prices, offering consistent expenditure shares and factor shares.
- NBER-CES Manufacturing Industry Database: A rich database on manufacturing industry productivity with data on productivity, employment, wages, and more.
Development Data
- World Development Indicators (WDI): A primary database for comprehensive development data from officially recognized international sources.
ChatGPT Prompts
Discover the power of ChatGPT Prompts for generating relevant and context-specific responses. These prompts, ranging from questions to commands, are instrumental in deriving meaningful insights from the AI model.
Fundamentals of Data Management: Cleaning, Preprocessing, and Visualization
This section covers the essentials of data management, starting with data cleaning and preprocessing to ensure accuracy and reliability. It delves into techniques for handling missing values, outliers, and errors to prepare datasets for analysis. The section on data visualization emphasizes creating impactful and informative visual representations, enhancing the interpretability of data insights. Additionally, it introduces summary statistics as a pivotal tool for initial data exploration, providing a snapshot of key trends and patterns, vital for informed decision-making in data analysis projects.
Example Prompt: "Could you assist me in obtaining historical stock prices for the ten largest companies by market capitalization from Yahoo Finance, for 2018 to 2023, using VS Code and Jupyter Notebooks? The tasks involve data cleaning and preprocessing to handle missing values, outliers, and errors, visualizing the cleaned data to identify trends and patterns, and generating summary statistics for a detailed dataset overview, aiding initial analysis and decision-making." Here is the ChatGPT responses of this prompt.
Causal Inference
Engage with prompts that delve into causal inference and its applications in data science.
Machine and Deep Learning
Access prompts that assist in understanding and implementing machine and deep learning models.
Natural Language Processing
Investigate prompts focusing on natural language processing and its relevance in data analysis.
Report Writing
Find prompts that guide the process of report writing, from structure to content. Poster Template
Sample Research Projects
Engage in impactful research projects like “News Sentiment and Stock Price: A DeepLearning Approach.” This comprehensive project, involving LSTM networks and sentiment analysis, offers an in-depth study of stock price movements, integrating data science and machine learning for real-world financial insights.