Jesus Rogel-Salazar

Data Science and Analytics with Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

The book 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 analytics using tools developed in Python, such as SciKit Learn, Pandas, Numpy, etc.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 400
• Erscheinungstermin: 5. Februar 2018
• Englisch
• ISBN-13: 9781498742115
• Artikelnr.: 56837502

Autorenporträt

Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant and data scientist in the financial industry since 2006. He is the author of the book "Essential Matlab and Octave", also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism and finance. Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant and data scientist in the financial industry since 2006. He is the author of the book "Essential Matlab and Octave", also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism and finance.

Inhaltsangabe

The Trials and Tribulations of a Data Scientist. Python: For Something
Completely Different. The Machine that Goes "Ping": Machine Learning and
Pattern Recognition. The Relationship Conundrum: Regression. Jackalopes and
Hares: Clustering. Decisions, Decisions: Hierarchical Clustering, Decision
Trees and Ensable Techniques. Less is More: Dimensionality Reduction.
Kernel Tricks under the Sleeve: Support Vector Machines. Pipelines in
Scikit-learn.