Applied Advanced Analytics
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Applied Advanced Analytics
This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.
Applied Advanced Analytics
This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.
600 Expert Interview Questions for Data Scientists: Apply Advanced Analytics to Solve Business Problems
Author: CloudRoar Consulting Services
language: en
Publisher: CloudRoar Consulting Services
Release Date: 2025-08-15
Are you preparing for a Data Scientist interview or looking to sharpen your expertise in machine learning, statistics, and AI-driven analytics? This book provides 600 carefully designed interview questions and answers, built to help you succeed in job interviews and stand out in the fast-growing field of data science. Data science has become the backbone of business intelligence, predictive analytics, and artificial intelligence. Organizations across finance, healthcare, retail, and technology depend on skilled data scientists to extract insights, build predictive models, and drive decision-making. This guide serves as both an interview prep manual and a knowledge reference, giving you a strong advantage in competitive hiring processes. Key topics covered include: Data Science Fundamentals: Statistics, probability, data preprocessing, and feature engineering. Machine Learning & AI: Supervised and unsupervised learning, deep learning, reinforcement learning. Programming & Tools: Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, Spark MLlib. Data Visualization & BI: Matplotlib, Seaborn, Tableau, Power BI. Big Data & Cloud Platforms: Hadoop, Spark, AWS SageMaker, Azure ML Studio, Google AI Platform. Model Deployment & MLOps: CI/CD pipelines, model monitoring, containerization with Docker/Kubernetes. Ethics & Compliance: Responsible AI, data privacy, GDPR, and bias mitigation. Case Studies & Problem Solving: Real-world interview scenarios and hands-on analytical challenges. This book is ideal for: Job seekers preparing for data scientist, ML engineer, or AI research roles. Professionals pursuing certifications such as AWS Certified Machine Learning – Specialty, Microsoft Azure Data Scientist Associate, Google Professional Data Scientist, or DASCA Senior Data Scientist (SDS™). Teams and hiring managers looking for structured Q&A resources to evaluate technical expertise. Students & professionals transitioning into the field of data science and AI. With 600 in-depth Q&As, you’ll gain the ability to explain concepts clearly, solve analytical challenges, and demonstrate expertise in data-driven problem solving. Whether your career path is in machine learning, applied AI, or enterprise analytics, this book equips you with the skills employers demand. Published by CloudRoar Consulting Services, this guide is your ultimate resource for data science interview preparation.