Data science and analytics with Python / Jesus Rogel-Salazar.

By: Rogel-Salazar, Jesus [author.]
Language: English Series: Chapman & Hall/CRC data mining and knowledge discovery seriesPublisher: Boca Raton : Taylor & Francis, CRC Press, [2017]Copyright date: c2017Description: xxxv, 376 pages ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781498742092 (pbk. : alk. paper)Subject(s): Data mining | Python (Computer program language) | DatabasesDDC classification: 006.3/12 LOC classification: QA76.9.D343 | R638 2017
Contents:
The Trials and Tribulations of a Data Scientist Data? Science? Data Science! The Data Scientist: A Modern Jackalope Data Science Tools From Data to Insight: the Data Science Workflow Python: For Something Completely DifferentWhy Python? Why not?! Firsts Slithers with Python Control Flow Computation and Data Manipulation Pandas to the rescue Plotting and visualising: Matplotlib The Machine that Goes "Ping": Machine Learning and Pattern Recognition Recognising Patterns Artificial Intelligence and Machine Learning Data is good, but other things are also needed Learning, Predicting and Classifying Machine Learning and Data Science Feature selection Bias, Variance and Regularisation: A Balancing Act Some Useful Measures: Distance and Similarity Beware the Curse of Dimensionality Scikit-learn is our Friend Training and Testing Cross-validation The Relationship Conundrum: Regression Relationships between variables: Regression Multivariate Linear Regression Ordinary Least Squares Brain and Body: Regression with one variable Logarithmic transformation Making the Task Easier: Standardisation and Scaling Polynomial Regression Variance-Bias Trade-Off Shrinkage: LASSO and Ridge Jackalopes and Hares: Clustering Clustering Clustering with k-means Summary Unicorns and Horses: Classification Classification Classification with KNN Classification with Logistic Regression Classification with Naive Bayes Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensable Techniques Hierarchical Clustering Decision Trees Ensemble Techniques Ensemble Techniques in Action Less is More: Dimensionality Reduction Dimensionality Reduction Principal Component Analysis Singular Value Decomposition Recommendation Systems Kernel Tricks under the Sleeve: Support Vector Machines Support Vector Machines and Kernel Methods Pipelines in Scikit-learn
Summary: 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
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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.

Includes bibliographical references and index.

The Trials and Tribulations of a Data Scientist Data? Science? Data Science! The Data Scientist: A Modern Jackalope Data Science Tools From Data to Insight: the Data Science Workflow Python: For Something Completely DifferentWhy Python? Why not?! Firsts Slithers with Python Control Flow Computation and Data Manipulation Pandas to the rescue Plotting and visualising: Matplotlib The Machine that Goes "Ping": Machine Learning and Pattern Recognition Recognising Patterns Artificial Intelligence and Machine Learning Data is good, but other things are also needed Learning, Predicting and Classifying Machine Learning and Data Science Feature selection Bias, Variance and Regularisation: A Balancing Act Some Useful Measures: Distance and Similarity Beware the Curse of Dimensionality Scikit-learn is our Friend Training and Testing Cross-validation The Relationship Conundrum: Regression Relationships between variables: Regression Multivariate Linear Regression Ordinary Least Squares Brain and Body: Regression with one variable Logarithmic transformation Making the Task Easier: Standardisation and Scaling Polynomial Regression Variance-Bias Trade-Off Shrinkage: LASSO and Ridge Jackalopes and Hares: Clustering Clustering Clustering with k-means Summary Unicorns and Horses: Classification Classification Classification with KNN Classification with Logistic Regression Classification with Naive Bayes Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensable Techniques Hierarchical Clustering Decision Trees Ensemble Techniques Ensemble Techniques in Action Less is More: Dimensionality Reduction Dimensionality Reduction Principal Component Analysis Singular Value Decomposition Recommendation Systems Kernel Tricks under the Sleeve: Support Vector Machines Support Vector Machines and Kernel Methods Pipelines in Scikit-learn

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

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