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Probability and Statistics for Data Science

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These days, statistics and probability are essential skills in a world where data is everywhere and influences decision-making in a wide range of businesses. It is critical to recognize the inherent unpredictability in today’s data-driven environment and make well-informed decisions. These mathematical ideas serve as the foundation for risk management techniques and are essential to scientific research because they facilitate the design of experiments, the interpretation of data, and the development of strong conclusions. Furthermore, statistical techniques play a crucial role in training models and generating predictions based on extensive datasets in the fields of artificial intelligence and machine learning, where their application is extensive. The book “Probability and Statistics for Data Science” stands out as a crucial tool, written by professionals who give the educational process real-world application. The book not only provides theoretical knowledge but also instills the skills required to apply these concepts in the ever-changing field of data analysis through the use of practical examples and hands-on exercises. With a strong basis in probability and statistics, the emphasis on contemporary tools and technology guarantees that readers are prepared to take on the complexity of today’s data science challenges and make substantial contributions to their particular fields.

We cover these topics

  1. Bayesian Networks
  2. Advanced Statistical Hypothesis Testing
  3. Regression Analysis (Advanced Techniques)
  4. Time Series Analysis
  5. Analysis of Variance (ANOVA)
  6. Machine Learning and Statistics Integration
  7. Bootstrap Methods
  8. Statistical Learning Theory
  9. Dimensionality Reduction
  10. Causal Inference
  11. Statistical Ethics and Bias
  12. Statistical Quality Control
  13. Spatial Statistics
  14. Survival Analysis
  15. Monte Carlo Methods in Statistics

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