Data Science and Analytics with Python

Contents: Preface. 1. Introduction to Data Science. 2. Environment Set-up and Basics of Python. 3. NumPy and pandas. 4. Data Visualization. 5. Python scikit-learn. 6. Environment Set-up: TensorFlow and Keras. 7. Probability. 8. Machine Learning and Data Pre-processing. 9. Statistical Analysis: Descriptive Statistics. 10. Statistical Analysis: Inferential Statistics. 11. Classification. 12. Prescriptive Analytics: Data Stream Mining. 13. Language Data Processing in Python. 14. Clustering. 15. Association Rule Mining. 16. Time Series Analysis Using Python. 17. Deep Neural Network and Convolutional Neural Network. 18. Case Studies. Index. Data Science and Machine Learning are the leading buzzwords of today. This book covers all aspects of these subjects, from data definition and categorization, classification techniques, clustering and ML algorithms to data stream and association rule mining, language data processing and neural networks. It explains descriptive and inferential statistical analysis, probability distribution and density functions as well as time series. It also describes the fundamentals of Python programming, the Python environment and libraries such as scikit-learn, NumPy and pandas, and takes a deep dive into data visualization modules and tools. Mastery of these areas will enable students to become proficient and effective data scientists. Salient features Ideal for undergraduate courses on Data Science and Analytics Provides step-by-step instructions for setting up the Python environment and executing various libraries and packages All chapters include relevant case studies, their Python code and output; the last chapter is dedicated to case studies Over 300 exercise questions comprising MCQs, programming exercises and concept-based questions, with answers provided for quick reference Bibliography at the end of every chapter for further reading Android app with chapter-wise PowerPoint slides and job interview questions.…