Stock Markets Analytics

Module 1:

1. 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.
2. Practical Data Process: Work with financial data using Finance APIs: yfinance

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Module 2

1. Data Types and Manipulation: Handling various data types and generating dummy variables for in-depth analysis.
2. Feature Generation: Creating additional features like time-based attributes and technical indicators using TaLib. Incorporating predictive elements for future growth analysis.
3. Data Cleaning: Implementing effective strategies for data cleaning and preparation, and mastering dataset joining for a comprehensive view.
4. Descriptive Analysis: Performing thorough descriptive analysis and exploring data correlations to derive meaningful insights.

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Module 3

1. Framing Hypotheses and Unraveling Time-Series Predictions
2. Heuristics and hand rules for practical predictions.
3. Predicting time-series data: trends, seasonality, and remainder decomposition.
4. Regression techniques for understanding data relationships.
5. Binary classification to determine growth direction.
6. Neural networks in analytical modelling.

Source:

• Code Sample
• Course page