DATA SCIENCE ANALYTIC THINKING INTRODUCTION

DATA SCIENCE

1. Introduction to Data Science & Analytic Thinking Understanding data-driven decision-making in modern businesses

2. What is Data Science? • Data Science is the study of data to extract meaningful insights. • Combines statistics, computer science, and domain knowledge. • Applied across industries to solve real-world problems.

3. Data-Analytic Thinking • Think in terms of how data can help solve problems. • Data can improve decisions, optimize operations, and predict outcomes. • Enables data-driven business strategies.

4. Example: Hurricane Frances • Used satellite and sensor data to predict impact. • Helped in planning evacuation and reducing losses.

5. Example: Predicting Customer Churn • Analyzing customer behavior to identify churn risk. • Allows proactive retention strategies.

6. Data Science vs Data Engineering • Data Engineering: Data collection, storage, transformation. • Data Science: Data analysis, modeling, insights. • Both enable data-driven decision-making.

7. Data Processing and Big Data • Big Data: Large volume, velocity, variety. • Requires distributed tools like Hadoop and Spark. • Enables real-time processing and analysis.

8. Data as a Strategic Asset • Companies use data for competitive advantage. • Example: Amazon, Netflix, Google. • Drives innovation and efficiency.

9. From Business Problem to Data Task • Identify business objective. • Translate into a data mining task (classification, clustering). • Example: Predict customer churn or segment users.

10. Supervised vs Unsupervised Learning • Supervised: Labeled data, predict known outcomes. • Unsupervised: No labels, find hidden patterns. • Examples: Churn prediction vs customer segmentation.

11. The Data Mining Process (CRISP-DM) 1. Business Understanding 2. Data Understanding 3. Data Preparation 4. Modeling 5. Evaluation 6. Deployment

12. Other Techniques & Technologies • Statistics: Foundations of analysis. • SQL: Data extraction. • Data Warehousing: Central data storage. • ML & Data Mining: Predictive and pattern-finding tools.

13. Conclusion • Data science transforms data into strategic insights. • Data-analytic thinking is key to solving modern business problems. • Learn tools, apply models, and drive value from data.