Data science and analytics with Python

• Main Author: Rogel-Salazar, Jesus, (Author)
• Format: eBook
• Language: English
• Published: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
• Series: Chapman & Hall/CRC data mining and knowledge discovery series.
• Subjects: Data mining.; Python (Computer program language); Databases.; elektronické knihy; electronic books
• ISBN: 9781315151670; 1315151677; 9781498742115; 1498742114; 9781523120000; 1523120002; 9781498742092; 1498742092; 9781138043176; 1138043176
• Physical Description: 1 online resource (xxxv, 376 pages)
• Access: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.--