SKU/Artículo: AMZ-B0GJ2QKM2Q
Python for Probability and Statistics: A Hands-On Guide to Statistical Modeling, Data Analysis, and Data Science Applications
Format:
Kindle
Detalles del producto
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Fuera de stock
Fuera de stock
Peso con empaque:
0.76 kg
0.76 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Unlock the Power of Python for Data Mastery: Your Essential Guide to Probability, Statistics, and Beyond Dive into the world of data with Python for Probability and Statistics: A Hands-On Guide to Statistical Modeling, Data Analysis, and Data Science Applications the ultimate resource for transforming theoretical concepts into practical, real-world solutions. Whether you're a student grappling with probability distributions, a data analyst seeking efficient workflows, or an aspiring machine learning engineer ready to tackle advanced modeling, this book equips you with the tools and knowledge to excel in today's data-driven landscape. Why This Book Stands Out: Hands-On, Code-Driven Excellence Forget dry theory – this guide is packed with over 445 runnable Python code blocks, all fully reproducible in environments like Jupyter Notebooks. Experiment with simulations, tweak parameters, and witness results in real-time, building unbreakable confidence in statistical modeling and data analysis. From bootstrapping confidence intervals to running Monte Carlo simulations for probability estimates, you'll learn by doing, making abstract ideas tangible and immediately applicable. Master the Python Ecosystem for Seamless Data Science Harness the full potential of essential libraries like NumPy, Pandas, SciPy, Matplotlib, Seaborn, Statsmodels, Scikit-learn, SymPy, Lifelines, TensorFlow, and Keras. Discover how to integrate them for end-to-end workflows: Clean messy datasets with Pandas, visualize correlations via heatmaps in Seaborn, build predictive models with Scikit-learn, and even dive into deep learning for image processing. This interconnected approach slashes your learning curve, enabling you to prototype statistical models like linear regression, GLMs, or survival analysis for applications in healthcare, finance, or marketing – all with efficiency and scalability. Bridge Theory and Practice with Depth and Clarity Gain profound insights by connecting mathematical foundations – including random variables, convergence theorems, hypothesis testing, and maximum likelihood estimation – directly to Python implementations. New additions like the Fisher Exact Test, Mann-Whitney-Wilcoxon Test, and expanded coverage of Generalized Linear Models ensure you're equipped for modern challenges. Over 158 Python-generated visualizations, from probability density plots to ROC curves, make complex topics intuitive, enhancing your ability to communicate data insights effectively. Advanced Topics for Cutting-Edge Applications Stay ahead with expanded sections on survival analysis (using Lifelines for time-to-event modeling in customer churn or medical studies), deep learning via gradient descent, and handling imbalanced datasets. Whether simulating A/B tests, modeling financial risks, or deploying ML for predictive maintenance, this book delivers real-world applicability, complete with programming tips for optimizing code, debugging, and ensuring numerical stability in scientific computing. Perfect for Every Learner and Professional Tailored for those with basic Python knowledge and undergraduate-level exposure to probability or statistics, this self-contained guide is accessible yet deep. Beginners build foundational skills progressively, while experts find advanced techniques and best practices to refine their craft. It's an ideal companion for self-learners, educators, researchers, and career-switchers entering data science – all without needing multiple resources or expensive tools, thanks to its focus on free, open-source Python. Elevate your skills, solve real problems, and accelerate your career in data science. Grab your copy of **Python for Probability and Statistics today and turn data into decisions!
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