This course is designed for beginners and aspiring professionals who want to explore how Data Science and Generative AI are used in Finance and Accounting. It introduces foundational concepts, hands-on tools, and real-world applications with a practical learning approach. This course is available in English, Tamil, and Hindi to make learning accessible and inclusive for a diverse group of learners.
Includes an exclusive in-person campus immersion at IIT Kanpur for hands-on learning and networking.
Pre-Requisites:
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Basic understanding of numbers and spreadsheets (e.g., Excel or Google Sheets).
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Curiosity about how finance works in the real world.
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No prior coding experience is required — No-code tools are included.
Target Audience:
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Students and fresh graduates looking to enter finance, accounting, or data roles.
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Professionals in finance, commerce, or administration who want to learn modern tools.
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Anyone interested in learning how AI is changing finance and accounting without needing technical expertise.
Key Learning Outcomes:
By the end of this course, participants will:
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Understand how data science is applied in finance and accounting.
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Learn to use tools like Excel, Google Sheets, Python, and Power BI.
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Gain knowledge of financial data types and how to clean and analyze them.
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Build simple models to detect fraud, predict credit scores, and visualize data.
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Explore advanced topics like AI-based risk management and portfolio optimization.
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Complete a capstone project focused on fairness and transparency in AI systems.
Delivery Mode & Duration:
- Delivery Mode:
- Online Live
- Hybrid Mode: Live online sessions + in-person campus immersion
- Duration: 40 Hours (8 Modules × 5 Hours Each)
Curriculum
Module 1: Introduction to Data Science for Finance & Accounting (5 hours)
Topics Covered:
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Understanding data, types of data, data science tools, and its importance in finance and accounting.
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Key Concepts in Finance and Accounting: Introduction to financial statements, key metrics (ROI, NPV, etc.), and how they relate to data science.
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Data Science Tools: Python, Excel, Jupyter Notebooks.
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Data Visualization: Using Matplotlib, Seaborn, Plotly for visualizing financial data.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 2: Data Collection and Cleaning (5 hours)
Topics Covered:
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Data Sources in Finance: Stock market data, economic indicators, company financials, and alternative data (e.g., social media, sentiment analysis).
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Data Preprocessing: Handling missing data, data normalization/standardization, dealing with outliers.
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ETL Process: Extracting, Transforming, and Loading data for analysis.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 3: Exploratory Data Analysis (5 hours)
Topics Covered:
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Basic Statistics for Finance: Descriptive statistics, distributions, hypothesis testing, correlation analysis.
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Financial Ratios: Liquidity, profitability, and solvency ratios.
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Visualizing Financial Data: Line charts for time series data, histograms, heatmaps, scatter plots.
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Data Analysis with Pandas: Grouping, pivot tables, aggregation, and filtering.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 4: Machine Learning for Finance (5 hours)
Topics Covered:
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Supervised Learning: Regression (Linear, Logistic), Decision Trees, Random Forest, Support Vector Machines.
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Unsupervised Learning: Clustering (K-means, Hierarchical), Dimensionality Reduction (PCA).
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Feature Engineering: Feature selection, creating financial indicators, and handling categorical data.
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Model Evaluation: Cross-validation, precision, recall, F1 score, ROC Curve.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 5: Predictive Modeling for Financial Forecasting (5 hours)
Topics Covered:
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Credit Scoring Models: Logistic regression, SVM, and Random Forest for credit risk prediction.
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Stock Price Prediction: Using machine learning to predict stock prices based on historical data, sentiment analysis, or macroeconomic factors.
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Portfolio Optimization: Markowitz Efficient Frontier, Modern Portfolio Theory (MPT), CAPM, and multi-factor models.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 6: Financial Analytics and Risk Management (5 hours)
Topics Covered:
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Risk Management Models: Value at Risk (VaR), Conditional VaR, Monte Carlo simulations.
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Credit Risk Analysis: Default probability estimation, stress testing.
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Market Risk: Analyzing market exposure and potential loss using data-driven methods.
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Operational Risk: Predicting fraud, cyber risk analysis.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 7: Accounting Analytics (5 hours)
Topics Covered:
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Financial Statement Analysis: Predictive models for detecting irregularities in financial statements (fraud detection).
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Auditing Analytics: Using data science to improve auditing processes (e.g., anomaly detection in transactions).
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Cost and Budget Analysis: Forecasting and optimizing costs and budgets using data models.
Case Study:
No-Code Tools:
Hands-on Activity:
Module 8: Advanced Topics in Financial Data Science (5 hours)
Topics Covered:
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Deep Learning for Finance: Neural Networks for financial forecasting, sentiment analysis from financial news, and stock prediction.
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Classification & Clustering Processing: Analyzing financial news, reports, and social media for sentiment analysis.
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Blockchain and Cryptocurrency: Data science applications in blockchain analysis, cryptocurrency price forecasting.
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Algorithmic Trading: Creating automated trading systems using machine learning models and financial data.
Case Study:
No-Code Tools:
Hands-on Activity:
Final Project: DSGenAI for Finance & Accounting
Project Theme:
Ensuring Transparency, Fairness & Accountability in AI-Driven Business Management
Problem Statement:
Businesses and organizations increasingly rely on AI and data-driven decision-making for hiring, credit scoring, marketing, customer interactions, and risk assessment. However, these AI systems often face ethical challenges such as bias, discrimination, privacy concerns, lack of explainability, and misinformation risks.
This project aims to identify, assess, and mitigate ethical risks in AI-based business management using data science, ML, LLMs, and Generative AI while maintaining fairness, transparency, and regulatory compliance.
Project Guidelines
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Project Selection & Scope Definition
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Choose one of the 10 business challenges related to ethical AI.
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Define a research question and ethical objectives for the project.
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Identify the key ethical risks and AI challenges to be addressed.
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Data Collection & Preprocessing
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Collect relevant datasets from open sources.
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Perform data cleaning, handle missing values, and remove bias.
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Conduct EDA to identify bias, fairness issues, and ethical risks.
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AI & ML Implementation
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Choose an appropriate model (Classification, Clustering, DS, Generative AI, or Fair AI).
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Train and evaluate using no-code tools (Google AutoML, DataRobot, Azure ML).
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Evaluate using accuracy, precision, recall, fairness metrics (AUC, F1, SHAP, etc.).
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Generative AI for Responsible AI Practices
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Analyze biases in LLMs, marketing content, and chatbots.
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Use GenAI for document summarization, compliance audits.
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Ensure ethical standards in all generated content.
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Ethical Risk Mitigation & AI Governance
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Use explainability frameworks (SHAP, LIME).
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Propose mitigation strategies and regulatory recommendations.
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Visualization & Final Report
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Use Power BI, Tableau, or Google Sheets to present results.
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Submit a structured report with insights, risk analysis, and audits.
Assessment Criteria (Total: 100 Marks)
Category |
Criteria |
Marks |
Problem Definition & Scope |
Clear AI ethics problem and goals |
10 |
Data Collection & Preprocessing |
Proper dataset usage and bias handling |
15 |
Exploratory Data Analysis (EDA) |
Bias detection, visualizations |
10 |
AI Model Implementation |
Ethical techniques, fairness focus |
20 |
Generative AI Use |
Responsible content generation |
10 |
Report & Presentation |
Final insights and strategy |
15 |
Total |
|
100 |