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Andy Cobley,
Mark Whitehorn,
Patrick Blackwill,
Stuart Anderson,
Alex Taylor,
Iain Kay,
Gary Bowerbank,
and
James Frost
Topics Covered
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Topics Covered
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Topics Covered
- The Data Exhaust
- Tabular vs Big Data
- Disappearances in the CAP Triangle
- NoSQL
- Cassandra
- MongoDb
- Graphs and Graph Databases
- Dark Data’s Hiding Place
- Big Data and Distributed Systems
- Hadoop, HDFS, MapReduce and Other Technologies
- Real-time Systems
- Lambda
- Introduction to Statistics
- Consumer Testing
- Introduction to R and Python
- Bayesian Statistics
- Machine learning and data mining
- The Future of Data Science
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An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.
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A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.
A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.
An authoritative text on statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
This online book provides a comprehensive overview of machine learning concepts and techniques. Written by a leading expert in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
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A comprehensive introduction to data analysis using R, this book covers a wide range of topics, including data manipulation, visualization, and statistical modeling. Written by leading experts in the field, it valuable resource for students and practitioners.
Provides a comprehensive overview of statistical methods for data analysis, covering topics such as probability distributions, hypothesis testing, and regression analysis. Written by a leading expert in the field, it valuable resource for students and practitioners in various fields.
This comprehensive handbook provides a wide range of topics in data science, including data mining, machine learning, and data visualization. Written by experts in the field, it valuable resource for students and practitioners who want to gain a broad understanding of data science.
A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
Provides a practical introduction to data science for business professionals. It covers topics such as data mining, data visualization, and statistical modeling. Written by experts in the field, it valuable resource for anyone who wants to gain a better understanding of data science.
Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
Provides a comprehensive and practical guide to deep learning, including hands-on exercises and real-world examples.
Classic text on machine learning and statistical pattern recognition, with a focus on Bayesian approaches. The author has won the prestigious Turing Award.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
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Provides a comprehensive treatment of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian inference to deep learning.
Practical guide to machine learning for programmers, with a focus on using Python to build and deploy machine learning models.
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