1st Edition

Data Science and Analytics with Python

By Jesus Rogel-Salazar Copyright 2017
    412 Pages 25 B/W Illustrations
    by Chapman & Hall

    412 Pages 25 B/W Illustrations
    by Chapman & Hall

    400 Pages 25 B/W Illustrations
    by Chapman & Hall

    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.

     

    About the Author

    Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and 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 in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

    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 Different
    Why 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 Naïve 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

    Biography

    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.

    For advanced students and professionals in data science and data analytics, this work provides an excellent introduction to the main concepts of data analytics using tools developed in Python. The popularity and open source nature of Python makes it an excellent choice for developing analytic models using add-on tools such as SciKit-learn, Numpy, and others. The book does not assume a working knowledge of Python and provides a through introductory chapter. The other chapters can be read independently of one another, making the text a valuable resource for readers interested in a specific area of data analytics. The book's design is user-friendly as well; wide margins allow for taking notes while reading. This space also contains summary notes of the material, making it easy to scan for specific concepts. The material covered includes machine learning and pattern recognition, various regression techniques, classification algorithms, decision tree and hierarchical clustering, and dimensionality reduction. Though this text is not recommended for those just getting started with computer programming, it would make an excellent tool for readers who wish to add Python to their programming language repertoire while developing models or analyzing data.
    D. B. Mason, Albright College, CHOICE, June 2018