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Vicky Lucas,
Jon Blower,
Richard Lamb,
Thomas Eldridge,
Rebecca Emerton,
Sally Stevens,
and
Tom August
Topics Covered
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A reference for executives and policymakers who want to understand the implications of big data for the future of business and governance.
Introduces data science and its applications in business, covering topics such as data mining, data analysis, and machine learning. It provides a solid foundation for understanding the concepts and techniques involved in Big Data analysis.
Provides an in-depth introduction to machine learning, covering the fundamental concepts and algorithms used in Big Data analysis. It is written by Andrew Ng, a leading expert in machine learning, and is highly recommended for those who want to gain a deeper understanding of Big Data.
Covers the practical aspects of Big Data analytics, providing guidance on how to plan, implement, and integrate Big Data solutions in an enterprise environment. It includes discussions on NoSQL and graph databases, which are essential technologies for handling Big Data.
Provides a comprehensive guide to Hadoop, the open-source framework for Big Data processing. It covers the core concepts and components of Hadoop, as well as advanced topics such as data warehousing and machine learning.
Provides a comprehensive guide to Spark, the popular open-source framework for Big Data processing. It covers the core concepts and components of Spark, as well as advanced topics such as streaming data and machine learning.
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Provides a technical overview of Big Data principles and best practices. It covers topics such as data ingestion, data storage, and data processing. It good option for those who want to gain a deeper understanding of the technical aspects of Big Data.
Provides a comprehensive guide to text processing with MapReduce, a framework for processing large datasets. It covers topics such as tokenization, stemming, and lemmatization, as well as more advanced topics such as sentiment analysis and text classification.
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Provides a comprehensive overview of deep learning, a subfield of machine learning that has revolutionized the field of artificial intelligence. It covers the fundamental concepts and algorithms of deep learning, as well as applications in various domains.
Provides a comprehensive introduction to reinforcement learning, a type of machine learning that involves making decisions in order to maximize reward. It covers the fundamental concepts and algorithms of reinforcement learning, as well as applications in various domains.
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Provides a practical introduction to data visualization, covering the principles and techniques involved in creating effective visualizations. It good option for those who want to learn how to visualize Big Data in order to communicate insights and make informed decisions.
Provides a comprehensive guide to Big Data analytics with Java, covering topics such as data ingestion, data storage, and data processing. It good option for those who want to gain a practical understanding of how to use Java to analyze Big Data.
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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.
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Time
9 hours
Via
FutureLearn
Institutions
University of Reading
Institute of Environmental Analytics
Institute for Environmental Analytics
Instructors
Vicky Lucas
Jon Blower
Richard Lamb
Thomas Eldridge
Rebecca Emerton
Sally Stevens
Tom August
Featured in The Course Notes
Read
one article that features
this course,
Big Data and the Environment
from our blog, The Course Notes:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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