Thinking Data Science: A Data Science Practitioner's Guide

ISBN-13: 9783031023620 / Angielski / Twarda / 2023 / 266 str.

Poornachandra Sarang

Spis treści

1. Data Science Process

2. Dimensionality Reduction - Creating Manageable Training Datasets

3. Classical Algorithms - Overview

4. Regression Analysis

5. Decision Tree

6. Ensemble - Bagging and Boosting

7. K-Nearest Neighbors

8. Naive Bayes

9. Support Vector Machines: A supervised learning algorithm for Classification and Regression

10. Clustering Overview

11. Centroid-based Clustering

12. Connectivity-based Clustering

13. Gaussian Mixture Model

14. Density-based

15. BIRCH

16. CLARANS

17. Affinity Propagation Clustering

18. STING

19. CLIQUE

20. Artificial Neural Networks

21. ANN-based Applications

22. Automated Tools

23. Data Scientist’s Ultimate Workflow

O autorze

Poornachandra Sarang, in his IT career spanning four decades, has been consulting large IT organizations on the design and architecture of systems using state-of-the-art technologies. He has authored several books covering a wide range of emerging technologies. Dr. Sarang is a Ph.D. advisor for Computer Science and Engineering and is on the thesis advisory committee for aspiring doctoral candidates. He has designed and delivered courses/curricula for universities at the postgraduate level, including courses and workshops on emerging technologies for industry. He is a known face at technical and research conferences delivering both keynote and technical talks.

Blurb

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.

The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.