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Module 1 Data Science Essentials | IABAC

Module 1 introduces the fundamentals of data scienceu2014its purpose, evolution, and links with AI, machine learning, and big data. It covers key terms, core concepts, and real-world applications, building a strong foundation for understanding data-driven decision-making and predictive analysis.

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Module 1 Data Science Essentials | IABAC

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  1. Module 1 Data Science Essentials iabac.org

  2. Introduction – Why This Module Matters Data Science starts with a solid foundation — understanding the essentials. This module explains what data science is, how it evolved, and how it connects with AI, analytics, and big data. Builds the groundwork for deeper learning in algorithms, analytics, and machine learning. Helps you think like a data scientist before applying tools. iabac.org

  3. What Is Data Science? Data science uses data, math, statistics, and programming to guide better decisions. Answers key questions: Why did something happen? What will happen next? What can we do about it? Used across industries — retail, healthcare, finance, and entertainment. iabac.org

  4. Data Science and Related Fields Big Data vs. Data Science Big Data: Managing large volumes of data. Data Science: Analyzing data to extract meaning. (Analogy: Big Data is a library; Data Science is the reader.) Data Science, AI & ML Connection AI: Goal — simulate human intelligence. ML: Method — enables machines to learn from data. Data Science: Process — combines both to create insights. iabac.org

  5. Core Concepts & Key Terms Big Data vs. Data Science Dataset: Collection of data points. Variable: Individual data element. Feature: Input used for prediction. Label: Desired outcome. Algorithm: Set of rules to solve problems. Model: Trained system that makes predictions. Data Science vs. Analytics Analytics: Explains past trends. Data Science: Predicts and shapes the future. iabac.org

  6. Real-World Application & Takeaway Online Retail Store Data Collection – user activity captured. Data Preparation – clean and organize data. Analysis – find patterns and trends. Prediction – forecast customer behavior. Action – personalized recommendations. Key Takeaways: Data science combines reasoning with data-driven insight. Understanding fundamentals improves decisions and communication. Prepares you for Module 2: Data Science Demo – Theory Meets Practice iabac.org

  7. Thank you Visit: www.iabac.org

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