Certificate in Financial Data Analytics & Artificial Intelligence

This course in Financial Data Analytics provides a comprehensive foundation in sourcing, processing, and analyzing diverse financial datasets using cutting- edge tools like Python and R. It combines fundamental concepts with advanced techniques in valuation, portfolio optimization, technical trading, and predictive modeling to equip learners with practical skills for data-driven decision-making in finance.

Program Details

Ideal for working professionals as well as students
Seven months (330 hours)
INR 1,85,500 (All inclusive) for participants in India
USD 2,530 for those registering from outside India
Super Early-bird discounts, Early-bird discounts, group discounts and special prices for students are available. Please contact us for more information
Fee payable in 3 installments
Classes are held on weekends, i.e., Saturdays and Sundays in the afternoon 3–8 pm IST (9:30 am–2:30 pm GMT).
Lectures will be live i.e. led by the instructor in real-time
Recording of each class will be available to all students

Eligibility

Who should attend?

Finance professionals seeking to upgrade analytical skills and integrate data-driven decision-making into their roles
Data analysts from other industries are looking to transition into financial services and gain domain expertise.
Graduate students in finance, economics, business, mathematics, computer science, or quantitative fields are pursuing specialized skills.
Banking and investment professionals wanting to enhance their quantitative modeling and risk assessment capabilities.
Business analysts and consultants working in financial services who need advanced analytics competencies.
Mid-career professionals from consulting, audit, or corporate finance looking to specialize in data-driven finance.
Risk management professionals requiring advanced modeling skills for credit, market, and operational risk.
Investment managers and portfolio analysts seeking quantitative modeling expertise.

Curriculum: A bird’s eye view

1. Foundation in Maths

(Asynchronous) Mathematics for Quant Finance

Matrices, Ordinary and partial differential equations, Sequences and series, Taylor series.

Probability and Statistics

Basics of probability, Distributions, Expectation, Functions of a random variable, Moment generating function, Central Limit Theorem.

2. Explore the World of Finance

Foundations of Finance and Bond Mathematics

Forward rates, Yield-to-maturity, Duration, Convexity, Hedging with bonds, Floating rate notes, Interest Rate Swaps.

Options

Arbitrage Relationships for Option Prices, Put-Call parity, Trading strategies involving options, Two-step and multi-step Binomial Tree for option pricing.

3. Essential Tools

Python Programming for Finance

This course builds Python programming skills with emphasis on data types, functions, modules, OOP, and file handling. It also introduces NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization.

Time Series Modelling and Forecasting

Covers AR(p) and MA(q) models (stationarity, invertibility, GE log returns examples), ARIMA framework (differencing, model selection via AIC/BIC), and forecasting with ARCH/GARCH for volatility clustering in financial data.

4. Dive Deep into Financial Data

Financial Markets & Statement Analytics

This module integrates financial data acquisition and analysis, covering market data sources, financial statement analysis, and fundamental valuation techniques, including DCF and multiples-based models. Students learn to implement sophisticated valuation models in Python while mastering technical analysis indicators, including moving averages, MACD, RSI, and volatility measures.

Portfolio Analytics, Optimization & Risk Analytics

This comprehensive risk analytics module integrates advanced portfolio optimization (Markowitz, CAPM, VaR), fraud detection using Benford’s Law, and sophisticated credit risk modeling, including PD/LGD/EAD estimation under regulatory frameworks such as Basel/IFRS 9. Students develop end-to-end quantitative risk management capabilities, from portfolio insurance strategies to credit scorecard development, leveraging Python programming.

5. Get Your Hands Dirty with AI!!

Machine Learning: Algorithms, Models, and Performance Analysis

This course introduces core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It covers regression, classification, decision trees, probabilistic models, and evaluation metrics for practical applications.

Advanced Concepts in Machine Learning and Deep Learning

This course focuses on ensemble methods, clustering algorithms, reinforcement learning basics, and neural network foundations. Students gain insights into backpropagation, activation functions, and introductory convolutional neural networks.

Advanced Deep Learning and Reinforcement Learning

This course explores advanced deep learning architectures (CNNs, GANs, LSTMs) and reinforcement learning methods. Learners practice with NLP, model optimization, and tools like TensorFlow, Keras, and TensorBoard.

6. AI Applications to Finance

Transforming Finance with AI: Trading, Sentiment Analysis, and Risk Modelling

Explores AI and ML application in Finance, including algo trading, feature engineering , time series application and regulatory standards.

Advanced AI in Finance: Volatility Prediction, Stress Testing, and Risk Modeling

Covers AI-driven financial analysis, including sentiment analysis, fraud detection, volatility modeling & model interpretability.

7. Fintech and Financial Data

FinTech Applications & Advanced Topics

This module covers blockchain and digital assets, real-time web scraping for market sentiment, and NLP-driven news analytics. It also teaches explainable AI (SHAP, LIME), bias mitigation, and business reporting with Power BI and SQL for transparent, compliant financial models.

8. Apply What You Learned to the Real World!!

Capstone Projects & Case Studies

Students work on comprehensive capstone projects that demonstrate mastery of the complete financial analytics workflow, from data acquisition and pipeline engineering through advanced modeling and validation to executive-level business reporting and presentation. These real-world case studies integrate all course components—valuation models, portfolio optimization, risk analytics, and regulatory frameworks—requiring students to synthesize technical skills with business acumen to deliver actionable insights for financial decision-making.

What distinguishes Certificate in Financial Data Analytics & Artificial Intelligence of SQF?

World-class faculty
Curriculum at par with the world’s best
Thorough coverage of risk analytics
Unmatched rigor in theoretical underpinnings
Hands-on implementation of the important models
Specialized support for interview preparation to secure quant roles at leading financial firms.

Career Opportunities

Graduates can pursue various roles in the quantitative finance sector:

Financial Data Analyst - Analyzing financial datasets, building reports, and providing insights for strategic decisions.
Quantitative Analyst - Developing mathematical models for trading, risk management, and investment strategies.
Credit Risk Analyst - Building credit scoring models, assessing default probability, and managing loan portfolios.
Portfolio Analyst - Optimizing investment portfolios, performance measurement, and asset allocation.
Financial Risk Manager - Implementing risk models, stress testing, and regulatory compliance.

Still have questions?

Email: info@sqf.org.in

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Teach at SQF

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