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Sebastian B
Topics Covered
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Topics Covered
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Topics Covered
- Introduction to big data and where it comes from
- Overview of the data analytics cycle
- Social media platforms and types of data
- Applications of big data across different industries
- Opportunities and challenges for big data analytics
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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.
Provides a practical guide to big data analytics. It covers the challenges of big data, as well as the techniques and tools that can be used to analyze big data. It valuable resource for anyone who wants to learn more about big data analytics.
Provides a comprehensive overview of data mining. It covers the basics of data mining, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data mining.
Provides a practical introduction to statistical methods for data analytics. It covers the basics of statistics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using statistics to analyze data.
Provides a guided tour of predictive analytics. It covers the basics of predictive analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using predictive analytics to make better decisions.
Provides a friendly introduction to data analytics for people who are new to the field. It covers the basics of data analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data analytics without getting bogged down in technical details.
Provides a comprehensive introduction to data analytics with Python. It covers the basics of Python, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use Python for data analytics.
Provides a comprehensive introduction to data analytics with R. It covers the basics of R, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use R for data analytics.
Covers the use of text analytics to extract insights from social media data. It provides a technical introduction to natural language processing and machine learning techniques.
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