Data Science and Predictive Analytics in Finance Training Course

Module 1: Financial Data Engineering and Foundations of Financial Risk

ü  Financial Data Sources, Types, and Structuring (Market, Credit, Customer)

ü  Data Cleaning, Feature Engineering, and Transformation for Models

ü  Review of Key Financial Risk Types (Credit, Market, Operational)

ü  The Importance of Data Leakage and Look-Ahead Bias

ü  Practical Session: Building a Robust Financial Feature Set using Python

Module 2: Predictive Modelling for Credit Risk and Default Prediction

ü  Logistic Regression and Classical Credit Risk Modelling (Scorecards)

ü  Utilizing Ensemble Methods (Random Forests, Gradient Boosting Machines)

ü  Handling Imbalanced Datasets (SMOTE, Class Weighting)

ü  Model Evaluation Metrics: AUC, Gini, and K-S Statistic

ü  Practical Session: Building and Comparing Default Prediction Models

Module 3: Time Series Analysis and Forecasting for Market Risk

ü  Stationarity, Autocorrelation, and Decomposition of Time Series Data

ü  Classical Models: ARIMA, ARMA, and SARIMA

ü  Volatility Modelling: Introduction to GARCH and its Variants

ü  Back-Testing and Evaluating Forecasting Accuracy

ü  Practical Session: Forecasting a Stock Index Volatility using a GARCH Model

Module 4: Algorithmic Trading Strategies and Machine Learning

ü  Overview of Machine Learning in Algorithmic Trading

ü  Momentum, Mean-Reversion, and Statistical Arbitrage Strategies

ü  Building and Optimizing Trading Signals using Classification Models

ü  Backtesting Methodologies and Avoiding Overfitting

ü  Practical Session: Developing a Simple Machine Learning-Based Trading Signal

Module 5: Natural Language Processing (NLP) for Financial Sentiment Analysis

ü  Introduction to NLP for Textual Financial Data (Earnings Calls, News)

ü  Pre-processing Text and Feature Extraction (Bag-of-Words, Embeddings)

ü  Sentiment Analysis using Lexicons and Machine Learning Classifiers

ü  Utilizing Financial Text Data for Predictive Insights

ü  Practical Session: Performing Sentiment Analysis on Financial News Headlines

Module 6: Fraud and Anomaly Detection Systems

ü  Typologies of Fraud in Finance (Transaction, Insurance, Credit)

ü  Unsupervised Learning for Anomaly Detection (Isolation Forest, Autoencoders)

ü  Supervised Learning for Fraud Classification

ü  Alert Triage, Prioritization, and Dynamic Threshold Setting

ü  Practical Session: Deploying an Unsupervised Model to Detect Transaction Anomalies

Module 7: Model Governance, Validation, and Regulatory Compliance (Basel/XAI)

ü  Principles of Model Governance and Documentation (SR 11-7)

ü  The Role of Explainable AI (XAI): LIME, SHAP Values, and Feature Importance

ü  Backtesting, Stress Testing, and Regulatory Validation (e.g., Basel Framework)

ü  Fairness and Bias Mitigation in Financial Models

ü  Practical Session: Interpreting Model Predictions using SHAP Values for a Credit Model

Module 8: Customer Analytics (LTV, Churn) and Behavioural Finance

ü  Modelling Customer Lifetime Value (CLV) using Predictive Analytics

ü  Building Churn Prediction Models for Retention Strategy

ü  Application of Behavioural Finance Insights in Modelling

ü  Hyper-Personalization and Targeted Financial Product Offers

ü  Practical Session: Developing a Basic Customer Churn Prediction Model

Module 9: Deep Learning Architectures in Finance (RNNs, LSTMs)

ü  Introduction to Deep Learning for Complex Time Series (Sequential Data)

ü  Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs)

ü  Application of LSTMs for Financial Forecasting

ü  Autoencoders for Advanced Anomaly Detection

ü  Practical Session: Implementing a Simple LSTM Model for Stock Price Prediction

Module 10: Model Deployment, Monitoring, and MLOps in a Financial Context

ü  Strategies for Operationalizing Data Science Models (Deployment)

ü  Continuous Monitoring of Model Performance and Drift Detection

ü  Version Control and Reproducibility in Model Development

ü  Principles of MLOps in a Regulated Financial Environment

ü  Practical Session: Defining a Production Monitoring Strategy for a Deployed Model

About Our Trainers

Our trainers are senior Quantitative Analysts (Quants) and Data Scientists with PhDs in computational fields and over 15 years of experience in leading global financial institutions (Investment Banks, Hedge Funds). They have direct expertise in developing algorithmic trading systems, designing Basel-compliant credit risk models, and implementing XAI frameworks, ensuring the course is technically advanced, mathematically sound, and aligned with industry best practices.

Quality Statement

Armstrong Global Institute is committed to delivering a premier Data Science and Predictive Analytics in Finance Training Course. Our curriculum integrates cutting-edge machine learning with rigorous financial theory and regulatory standards. We guarantee an intensive, hands-on, and highly specialized learning experience essential for mastering the future of quantitative finance.

Tailor Made Courses

This Data Science and Predictive Analytics in Finance Training Course can be fully customized to focus on specific financial domains (e.g., insurance risk, asset management), specific programming languages (e.g., R, MATLAB), or specialized regulatory environments (e.g., Dodd-Frank, MiFID II). We offer bespoke in-house training programs designed after a detailed needs assessment. For further inquiries, please contact us on Tel: +254737296202 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com  

Admission Criteria

ü  Participants should be reasonably proficient in English. 

ü  Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

ü  Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

ü  What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

ü  Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

ü  Course Improvement: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

ü  Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com  and we will send you a registration form. We advise you to book early to avoid missing a seat to this training. Or call us on +254737296202 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

ü  Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

ü  Invoice: We can send a bill directly to you or your company.

ü  Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

ü  Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

ü  Participants may cancel attendance 14 days or more prior to the training commencement date.

ü  No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Accommodation and Airport Transfer

For physical training attendees, we can assist with recommendations for accommodation near the training venue. Airport pick-up services can also be arranged upon request to ensure a smooth arrival. Please inform us of your travel details in advance if you require these services. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254737296202 / +254725012095 / +254724452588