- Data-Driven Decisions: Understanding the philosophy and methodology behind making informed decisions based on data. Exploring the landscape of potential personal investments and addressing risk and reward considerations.
- Practical Data Process: Work with financial data using Finance APIs: yfinance
- Data Types and Manipulation: Handling various data types and generating dummy variables for in-depth analysis.
- Feature Generation: Creating additional features like time-based attributes and technical indicators using TaLib. Incorporating predictive elements for future growth analysis.
- Data Cleaning: Implementing effective strategies for data cleaning and preparation, and mastering dataset joining for a comprehensive view.
- Descriptive Analysis: Performing thorough descriptive analysis and exploring data correlations to derive meaningful insights.
- Framing Hypotheses and Unraveling Time-Series Predictions
- Heuristics and hand rules for practical predictions.
- Predicting time-series data: trends, seasonality, and remainder decomposition.
- Regression techniques for understanding data relationships.
- Binary classification to determine growth direction.
- Neural networks in analytical modelling.