π 2023-06-09 β Session: Developed comprehensive course outline for Data Mining
π 10:10β10:30
π·οΈ Labels: Data Mining, Economics, Finance, Course Design, Predictive Modeling
π Project: Teaching
β Priority: MEDIUM
Session Goal: The goal of this session was to develop a comprehensive and detailed course outline for a course titled βData Mining in Economics and Financeβ. This course aims to equip students with the skills to apply data mining techniques specifically in the economic and financial sectors.
Key Activities:
- Reviewed and outlined the course content distribution over 25 sessions, focusing on data mining, predictive modeling, time series analysis, and practical case studies.
- Developed a revised course outline detailing modules covering various data mining techniques, applications, and ethical considerations.
- Enhanced the course outline by integrating economics-focused topics and applications, including predictive modeling, time series analysis, and sentiment analysis.
- Expanded the curriculum to include finance-related topics such as fraud detection and algorithmic trading.
Achievements:
- Successfully created a comprehensive 64-hour course outline covering key topics such as data preprocessing, supervised and unsupervised learning algorithms, and ethical considerations.
- Integrated advanced techniques and applications into the course structure, ensuring a robust educational framework.
Pending Tasks:
- Finalize the detailed syllabus for each session, including specific learning objectives and assessment methods.