Duration: 2 days

Introduction to Python/R for financial analysis, predictive modelling, clustering, and risk scoring

Duration: 2 Days (Instructor-led, hands-on workshop)
Target Audience: Finance analysts, risk managers, actuaries, accountants, investment analysts, internal auditors, data analysts in banking and insurance
Level: Beginner to Intermediate (no prior coding required but familiarity with finance concepts expected)
Tools: Python (via Jupyter Notebooks), Excel, scikit-learn, pandas, matplotlib/seaborn

Course Objectives

By the end of this course, participants will be able to:

Understand key concepts in data science and machine learning as they apply to financial decision-making and analysis.

Apply data preprocessing techniques to clean, transform, and prepare financial datasets for analysis.

Use machine learning algorithms such as regression, classification, clustering, and decision trees in financial modeling.

Leverage Python and key libraries (e.g., Pandas, Scikit-learn, NumPy, Matplotlib) to build, evaluate, and interpret predictive financial models.

Develop models for credit risk scoring, fraud detection, customer segmentation, and algorithmic trading.

Interpret model outputs and communicate findings through data visualization and storytelling techniques tailored to finance stakeholders.

Understand ethical considerations and model governance in the application of AI and machine learning in financial services.

1. Foundations of Data Science in Finance

Session 1: Introduction to Data Science in Financial Services

• Overview of data science and its applications in banking, insurance, and asset management
• Use cases: fraud detection, credit scoring, risk modeling, robo-advisory, algo trading
• The data science workflow: Business problem → Data → Modeling → Deployment

Session 2: Data Handling & Cleaning with Python

• Introduction to Python for finance: Jupyter Notebook environment
• Data types, data structures, importing CSV/Excel files
• Handling missing data, outliers, duplicates
• Feature engineering: transformation, encoding, normalization

Session 3: Exploratory Data Analysis (EDA) & Financial Visualizations

• Understanding data distributions, correlations, trends
• Time series visualization (line plots, heatmaps, histograms)
• Practical EDA exercise using financial data (e.g., stock prices, credit data)

Hands-on Labs:

• Clean and analyze sample credit data
• Visualize trends in market data and identify anomalies

1. Machine Learning Applications in Finance

Session 4: Supervised Learning for Finance

• Regression models for forecasting financial KPIs
• Classification models for credit scoring and fraud detection
• Model types: Linear Regression, Logistic Regression, Decision Trees, Random Forest
• Model evaluation: accuracy, confusion matrix, ROC-AUC

Session 5: Unsupervised Learning & Clustering

• Introduction to clustering for customer segmentation
• K-means clustering: identifying borrower segments
• Dimensionality reduction with PCA (Principal Component Analysis)

Session 6: Model Building & Deployment Simulation

• Building a credit scoring model using real-world data
• Cross-validation, hyperparameter tuning
• Interpretability of ML models (feature importance, SHAP)
• Ethics, bias, and responsible AI in finance

Capstone Exercise:

Participants will build and present a mini end-to-end project:

• Define a problem (e.g., credit scoring, fraud risk)
• Clean, analyze, and visualize the dataset
• Train and evaluate a predictive model
• Present insights and implications for financial decision-making

Methodology

• Interactive lectures with demonstrations
• Guided hands-on programming labs
• Group-based case simulations
• Access to preloaded notebooks and datasets

FOR COURSE SCHEDULING AND CUSTOMIZATION:
Please contact our office to discuss your specific training needs and preferred dates. Programmes can be delivered in-house or virtually, depending on your team’s requirements.

FOR GROUP REGISTRATIONS OR DIRECT PAYMENTS
If you prefer to arrange payment directly with our office (especially for group bookings or if you do not wish to process payments online), please contact us for assistance:
📞 +27 78 119 6889 | ✉️ training@lwaleadinstitute.lwacorporate.com

Our team will be happy to guide you through the registration and payment process securely.

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