15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Financial Analysts
- Investment Analysts
- Risk Analysts and Risk Managers
- Data Scientists
- Financial Data Analysts
- Business Intelligence Professionals
- Economists and Economic Analysts
- Quantitative Analysts
- Portfolio Managers
- Investment Professionals
- Banking and Financial Services Professionals
- Credit Risk and Credit Analytics Professionals
- Fraud and Financial Crime Analysts
- Treasury and Finance Professionals
Session Objectives
- Understand the fundamentals of data science for finance.
- Master time series forecasting for financial data.
- Utilize risk modeling for financial risk management.
- Implement algorithmic trading strategies with data science.
- Design and build financial data analysis pipelines.
- Optimize financial models for accuracy and profitability.
- Troubleshoot and address complex financial data challenges.
- Implement model evaluation and validation techniques for finance.
- Integrate data science into real-world financial systems.
- Understand how to handle financial data ethically and compliantly.
- Explore advanced data science techniques for finance (e.g., natural language processing for financial news).
- Apply real world use cases for data science in finance.
- Leverage financial data libraries for efficient model implementation.
About the Course
This comprehensive training course provides participants with the knowledge and practical skills to apply data science, statistical analysis, machine learning, and predictive analytics to financial decision-making. It explores how financial institutions and organizations can transform large and complex financial datasets into actionable insights for investment, risk management, forecasting, fraud detection, customer analytics, and strategic planning.Participants will learn how to collect, clean, transform, analyze, and visualize financial data while applying statistical and machine learning techniques to real-world financial problems. The course covers financial forecasting, credit risk analytics, portfolio analysis, fraud detection, customer segmentation, algorithmic decision-making, and financial time-series analysis.Through practical exercises and industry-based case studies, participants will develop analytical models, evaluate their performance, interpret financial data, and communicate data-driven findings to decision-makers. The course also addresses data quality, model risk, bias, explainability, governance, and responsible use of AI in financial environments.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of data science for finance.
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Workshop 1.2:
Overview of time series forecasting, risk modeling, and algorithmic trading.
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Workshop 1.3:
Setting up a financial data science development environment.
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Workshop 1.4:
Introduction to financial data libraries and tools.
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Workshop 1.5:
Best practices for finance data science.
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Workshop 2.1:
Implementing time series forecasting models for financial data (ARIMA, LSTM).
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Workshop 2.2:
Utilizing technical indicators and financial features.
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Workshop 2.3:
Designing and building financial forecasting pipelines.
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Workshop 2.4:
Optimizing forecasting models for market prediction.
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Workshop 2.5:
Best practices for time series.
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Workshop 3.1:
Implementing risk modeling techniques (VaR, Monte Carlo simulations).
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Workshop 3.2:
Utilizing credit risk and market risk models.
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Workshop 3.3:
Designing and building risk management systems.
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Workshop 3.4:
Optimizing risk models for financial stability.
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Workshop 3.5:
Best practices for risk modeling.
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Workshop 4.1:
Implementing algorithmic trading strategies with data science.
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Workshop 4.2:
Utilizing machine learning for trading signals.
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Workshop 4.3:
Designing and building automated trading systems.
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Workshop 4.4:
Optimizing trading models for profitability.
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Workshop 4.5:
Best practices for algorithmic trading.
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Workshop 5.1:
Designing financial data analysis pipelines.
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Workshop 5.2:
Implementing data preprocessing and feature engineering.
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Workshop 5.3:
Utilizing financial APIs and data sources.
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Workshop 5.4:
Optimizing pipelines for financial data processing.
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Workshop 5.5:
Best practices for data pipelines.
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Workshop 6.1:
Optimizing financial models for accuracy and profitability.
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Workshop 6.2:
Utilizing hyperparameter tuning for financial models.
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Workshop 6.3:
Implementing model validation and cross-validation.
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Workshop 6.4:
Designing scalable financial data solutions.
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Workshop 6.5:
Best practices for model optimization.
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Workshop 7.1:
Debugging complex financial data issues.
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Workshop 7.2:
Analyzing model performance and errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for model improvement.
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Workshop 7.4:
Resolving common financial data challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing evaluation metrics for financial models.
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Workshop 8.2:
Utilizing backtesting and stress testing for model validation.
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Workshop 8.3:
Designing and building model validation pipelines.
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Workshop 8.4:
Optimizing model evaluation strategies.
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Workshop 8.5:
Best practices for model evaluation.
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Workshop 9.1:
Integrating data science models into real-world financial systems.
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Workshop 9.2:
Utilizing APIs and deployment tools for financial data.
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Workshop 9.3:
Implementing real-time financial analytics.
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Workshop 9.4:
Optimizing models for deployment environments.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Implementing ethical considerations for financial data.
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Workshop 10.2:
Utilizing compliance frameworks for data handling.
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Workshop 10.3:
Designing and building secure financial data pipelines.
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Workshop 10.4:
Optimizing data handling for regulatory compliance.
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Workshop 10.5:
Best practices for ethical handling.
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Workshop 11.1:
Implementing natural language processing (NLP) for financial news analysis.
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Workshop 11.2:
Utilizing graph neural networks for financial network analysis.
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Workshop 11.3:
Designing and building advanced financial data models.
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Workshop 11.4:
Optimizing advanced techniques for specific applications.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing data science for fraud detection in finance.
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Workshop 12.2:
Utilizing data science for portfolio optimization.
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Workshop 12.3:
Implementing data science for credit scoring.
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Workshop 12.4:
Utilizing data science for market sentiment analysis.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing pandas-datareader and yfinance for financial data.
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Workshop 13.2:
Implementing financial models with scikit-learn and TensorFlow.
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Workshop 13.3:
Designing and building financial data pipelines with libraries.
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Workshop 13.4:
Optimizing library usage for efficient implementation.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing model interpretability techniques for financial models.
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Workshop 14.2:
Utilizing visualization tools for understanding financial predictions.
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Workshop 14.3:
Designing and building interpretable financial models.
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Workshop 14.4:
Optimizing model transparency for stakeholder understanding.
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Workshop 14.5:
Best practices for interpretability.
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Workshop 15.1:
Emerging trends in data science for finance.
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Workshop 15.2:
Utilizing federated learning for financial data.
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Workshop 15.3:
Implementing AI for personalized financial advice.
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Workshop 15.4:
Best practices for future financial applications.
