Data Science for Business (CS2988)

Introduction to Data Science for Business

Dr. Rourab Paul

Computer Science Department, SNU University, Chennai

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Data Science For Business

What DSB?

Data Science for Business refers to the application of data science methods to solve business problems and support decision-making. It is not about tools or coding alone; it is about using data to create business value.

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Data Science for Business =

Using data, statistical thinking, and analytical models to make better business decisions.

It focuses on how data science fits into business strategy, rather than only technical details.

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What DSB?

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Core Components of DSB?

1. Business Problem Understanding

Before any modeling:

• What decision needs to be made?
• What action will be taken based on the result?
• What is the cost of wrong decisions?

Example: Should we offer a discount to this customer?

2. Data Collection & Preparation

• Customer data
• Transaction data
• Logs, sensors, text, images
• Cleaning, integration, feature creation

3. Data Mining & Machine Learning

Using models such as:

• Classification (e.g., churn prediction)
• Regression (e.g., sales forecasting)
• Clustering (e.g., customer segmentation)
• Anomaly detection (e.g., fraud detection)

4. Evaluation Using Business Metrics

Not just accuracy:

• Profit
• Cost savings
• Risk reduction
• Return on Investment (ROI)

Example: A model with 85% accuracy may be worse than one with 75% accuracy if it costs more money to deploy.

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5. Decision-Making & Deployment

• Integrating models into business processes
• Automating or supporting decisions
• Monitoring and improving models over time�

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Room, timetable, teacher

• Teacher : Dr. Rourab Paul
• Computer Science Department
• Web: https://rourab.com/leclist.php?sub=DSB&year=2025-2026&sec=Economics-DS
• Email: rourabpaul@snuchennai.edu.in
• Skype: rourabpaul
• Room and Timetable
• Theory: 9:00 – 09:50 Monday, 203 AB2
• Theory: 8:10 – 09:00 Wednesday, 203 AB2
• Theory: 09:50 – 10:40 Thursday, 203 AB2
• Theory: 11:00 – 11:50 Friday, 203 AB2
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Evaluation Procedure

• Theory: 60 Marks:
• End Sem (Written): 100 marks (50%)
• Mid Sem (Written): 50 Marks (30%)
• CIA : 20 Marks (20%)

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Total Credit : 4

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Data Science Business Process

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Analytical Methods of Data Science

1. Predictive Modeling & Machine Learning

Goal: Predict future outcomes using historical data.

Key Techniques

• Linear & Logistic Regression
• Decision Trees, Random Forest
• Support Vector Machines (SVM)
• Neural Networks

Business Examples

• Customer churn prediction
• Loan default prediction
• Sales forecasting

Historical Data → Model → Prediction → Business Decision

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Analytical Methods of Data Science

2. Operations Research (OR) & Optimization

Goal: Find the best solution under constraints.

Key Techniques

• Linear Programming (LP)
• Integer Programming
• Dynamic Programming
• Simulation

Business Examples

• Supply chain optimization
• Production scheduling
• Inventory control

Objective + Constraints → Optimization Model → Optimal Solution

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Analytical Methods of Data Science

3. Agent-Based Modeling (ABM)

Goal: Study complex systems through individual agent behavior.

Key Concepts

• Autonomous agents
• Local rules
• Emergent behavior�

Business Examples

• Market behavior simulation
• Traffic flow analysis
• Epidemic spread modeling

Agents → Interactions → Emergent System Behavior

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Analytical Methods of Data Science

5. Text Analysis (Text Mining / NLP)

Goal: Extract insights from unstructured text.

Key Techniques

• Tokenization
• TF-IDF
• Sentiment Analysis
• Topic Modeling�

Business Examples

• Customer review analysis
• Social media monitoring
• Chatbots

Text Data → NLP Processing → Structured Insights

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Business Use Cases

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Simple Example

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Thank You

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