Course Outline
Introduction to Artificial Intelligence in the Financial Sector
- Overview of AI applications in finance, including fraud detection, algorithmic trading, and risk assessment for government operations.
- Introduction to data analysis principles and types of financial data relevant for government use.
- Ethical considerations and regulatory compliance in the implementation of AI systems for government purposes.
- Setting up a Python/R environment for financial data analysis in public sector contexts.
Data Collection and Preprocessing
- Data sources in the financial sector, including stock data, market indices, and customer information for government use.
- Data cleaning, normalization, and transformation techniques suitable for government datasets.
- Feature engineering to enhance data analysis for government applications.
- Preprocessing a financial dataset for analysis within public sector operations.
Machine Learning Algorithms for Financial Data
- Supervised learning algorithms such as linear regression, decision trees, and random forest for government financial data analysis.
- Unsupervised learning methods for anomaly detection, including k-means clustering and DBSCAN, tailored for government use.
- Case study analysis: Credit scoring models and risk management strategies for government agencies.
- Building a supervised model to predict stock prices for government decision-making.
Advanced AI Techniques and Model Optimization
- Deep learning models for financial data, including LSTM for time-series forecasting in public sector applications.
- Introduction to reinforcement learning for decision-making in trading strategies for government purposes.
- Hyperparameter tuning and model validation techniques for government datasets.
- Implementing LSTM for financial time-series data in a government context.
Visualization, Interpretation, and Reporting
- Data visualization best practices using libraries such as Matplotlib, Seaborn, and Tableau for government reports.
- Interpreting model outputs to derive business insights for government operations.
- Creating comprehensive reports for stakeholders in the public sector.
- Analyzing and presenting financial data using a complete AI workflow suitable for government use.
Summary and Next Steps
Requirements
- Basic proficiency in Python or R programming for government
- Familiarity with financial terminology and fundamental statistics
Audience
- Financial analysts for government agencies
- Data scientists supporting public sector initiatives
- Risk managers within government organizations
Testimonials (4)
Deepthi was super attuned to my needs, she could tell when to add layers of complexity and when to hold back and take a more structured approach. Deepthi truly worked at my pace and ensured I was able to use the new functions /tools myself by first showing then letting me recreate the items myself which really helped embed the training. I could not be happier with the results of this training and with the level of expertise of Deepthi!
Deepthi - Invest Northern Ireland
Course - IBM Cognos Analytics
Share example of application
Course - Alteryx for Data Analysis
Very clearly articulated and explained
Harshit Arora - PwC South East Asia Consulting
Course - Alteryx for Developers
Linear regression - the algorithm to predict the trend
