18. Data Science for Business and Decision- Making

Sections

Navigate through the learning materials and practice exercises.

1. 18

   Data Science For Business And Decision-Making

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   This section discusses the significant role of data science in enhancing...

2. 18.1

   The Role Of Data Science In Business

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   Data science is crucial for making informed business decisions, enhancing...

3. 18.1.1

   What Is Business Decision-Making?

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   Business decision-making involves selecting the best course of action from...

4. 18.1.2

   How Data Science Enhances Decision-Making

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   Data science transforms raw data into actionable insights, enhancing...

5. 18.2

   Key Areas Of Application

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   This section highlights the various domains where data science applications...

6. 18.2.1

   Marketing Analytics

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   Marketing Analytics employs data science techniques to optimize marketing...

7. 18.2.2

   Sales Forecasting

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   Sales forecasting utilizes data-driven techniques to predict future sales...

8. 18.2.3

   Operations And Supply Chain

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   This section discusses how data science enhances operations and supply chain...

9. 18.2.4

   Human Resources

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   This section discusses the role of data science in enhancing human resource...

10. 18.2.5

    Finance

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    This section discusses the role of data science in enhancing financial...

11. 18.3

    Data-Driven Decision-Making Framework

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    The Data-Driven Decision-Making Framework outlines a systematic approach to...

12. 18.3.1

    Step 1: Define The Business Problem

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    Defining the business problem is crucial for effective data-driven decision-making.

13. 18.3.2

    Step 2: Data Collection

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    Data collection is a critical step in data-driven decision-making, involving...

14. 18.3.3

    Step 3: Data Preprocessing

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    Data preprocessing is a critical step in the data-driven decision-making...

15. 18.3.4

    Step 4: Model Building

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    Model building is the process of developing predictive models using data to...

16. 18.3.5

    Step 5: Evaluation And Interpretation

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    This section emphasizes the evaluation and interpretation of models to...

17. 18.3.6

    Step 6: Deployment

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    Deployment is critical for integrating data-driven insights into business processes.

18. 18.3.7

    Step 7: Monitoring And Feedback Loop

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    Step 7 emphasizes the importance of tracking model performance and...

19. 18.4

    Tools And Technologies In Business Analytics

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    This section details the various tools and technologies essential for...

20. 18.5

    Case Studies

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    This section presents three illuminating case studies demonstrating the...

21. 18.5.1

    Case Study 1: Predicting Customer Churn In Telecom

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    This section discusses a telecom case study focusing on predicting customer...

22. 18.5.2

    Case Study 2: Retail Inventory Optimization

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    This case study highlights how effective inventory optimization can save...

23. 18.5.3

    Case Study 3: Credit Card Fraud Detection

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    This section examines a case study focused on utilizing anomaly detection...

24. 18.6

    Ethical And Strategic Considerations

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    This section addresses the ethical implications of data science in...

25. 18.6.1

    Data Ethics In Decision-Making

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    This section addresses the ethical considerations in using data for...

26. 18.6.2

    Strategic Alignment

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    Strategic alignment ensures that data analytics efforts are in line with...

27. 18.7

    Metrics For Evaluating Business Decisions

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    This section discusses various metrics used to evaluate business decisions,...