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Discover Neo4j, Elasticsearch, and MongoDB, the recent revolution in databases. ETL techniques such as topical crawling are discussed in this book, which can be applied to high-frequency algorithmic trading and goal-oriented dialog systems. In addition, you'll see examples of semisupervised learning, deep learning, and natural language processing. In addition to time series and principal component analysis, Advanced Data Analytics Using Python covers traditional data analysis techniques. This book will give you a thorough understanding of analytics projects. The Python code will help you learn the concepts and give you examples to use. You'll: Classification, clustering, regression, forecasting, and regression analysis ETL techniques, Neo4j, Elasticsearch, MongoDB, and MySQL databases are all handled Learn about Hadoop and Spark big data frameworks Become familiar with semi-supervised learning, deep learning, and natural language processing Discover the latest advances in databases such as Neo4j, Elasticsearch, and MongoDB while gaining a comprehensive understanding of advanced data analytics concepts. ETL techniques are discussed in this book, including topical crawling, which is applied to a range of applications, including high-frequency algorithmic trading and goal-oriented dialog systems. You will also be introduced to concepts such as semi-supervised learning, deep learning, and natural language processing (NLP). Furthermore, time series analysis and principal component analysis are also covered in Advanced Data Analytics Using Python. You will be able to understand all technical aspects of an analytics project after reading this book. Using Python code, you will be able to learn the concepts and apply them to your own work. Here is what you will learn: Analyze data using classification, clustering, regression, and forecasting techniques ETL techniques, Neo4j, Elasticsearch, MongoDB, and MySQL databases are all