Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see practical examples of machine learning concepts such as semi-supervised learning, deep learning, computer vision and NLP. Practical Data Analytics with Python also covers important traditional data analysis techniques such as time series, principal component analysis through examples from real industry projects.
What You Will Learn
- Work with data analysis techniques such as classification, clustering, regression, and forecasting
- Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
- Examine the different big data frameworks, including Hadoop, Hive, Pig, Storm, and Spark
- Discover advanced machine learning concepts such as semi-supervised learning, deep learning, computer vision, and NLP
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
- ISBN: 978-1-4842-3449-5
- Editorial: Apress
- Encuadernacion: Rústica
- Páginas: 190
- Fecha Publicación: 21/07/2018
- Nº Volúmenes: 1
- Idioma: Inglés
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