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  • Book Overview & Buying Python Data Science Essentials
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Python Data Science Essentials

Python Data Science Essentials - Second Edition

By : Alberto Boschetti, Luca Massaron
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Python Data Science Essentials

Python Data Science Essentials

4 (3)
By: Alberto Boschetti, Luca Massaron
Buy this Book

Overview of this book

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. First Steps
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1. First Steps
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Introducing data science and Python
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Installing Python
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Introducing Jupyter
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Datasets and code used in the book
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Summary
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2. Data Munging
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2. Data Munging
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The data science process
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Data loading and preprocessing with pandas
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Working with categorical and text data
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Data processing with NumPy
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Creating NumPy arrays
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NumPy's fast operations and computations
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Summary
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3. The Data Pipeline
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3. The Data Pipeline
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Introducing EDA
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Building new features
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Dimensionality reduction
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The detection and treatment of outliers
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Validation metrics
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Testing and validating
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Cross-validation
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Hyperparameter optimization
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Feature selection
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Wrapping everything in a pipeline
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Summary
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4. Machine Learning
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4. Machine Learning
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Preparing tools and datasets
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Linear and logistic regression
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Naive Bayes
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K-Nearest Neighbors
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Nonlinear algorithms
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Ensemble strategies
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Dealing with big data
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Approaching deep learning
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A peek at Natural Language Processing (NLP)
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An overview of unsupervised learning
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Summary
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5. Social Network Analysis
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5. Social Network Analysis
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Introduction to graph theory
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Graph algorithms
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Graph loading, dumping, and sampling
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Summary
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6. Visualization, Insights, and Results
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6. Visualization, Insights, and Results
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Introducing the basics of matplotlib
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Wrapping up matplotlib's commands
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Interactive visualizations with Bokeh
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Advanced data-learning representations
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Summary
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1. Strengthen Your Python Foundations
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1. Strengthen Your Python Foundations
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Your learning list
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Learn by watching, reading, and doing

Introducing data science and Python

Data science is a relatively new knowledge domain, though its core components have been studied and researched for many years by the computer science community. Its components include linear algebra, statistical modeling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.

Data science is a new domain and you have to take into consideration that currently its frontiers are still somewhat blurred and dynamic. Since data science is made of various constituent sets of disciplines, please also keep in mind that there are different profiles of data scientists depending on their competencies and areas of expertise.

In such a situation, what can be the best tool of the trade that you can learn and effectively use in your career as a data scientist? We believe that the best tool is Python, and we intend to provide you with all the essential information that you will need for a quick start.

In addition, other tools such as R and MATLAB provide data scientists with specialized tools to solve specific problems in statistical analysis and matrix manipulation in data science. However, Python really completes your data scientist skill set. This multipurpose language is suitable for both development and production alike; it can handle small- to large-scale data problems and it is easy to learn and grasp no matter what your background or experience is.

Created in 1991 as a general-purpose, interpreted, and object-oriented language, Python has slowly and steadily conquered the scientific community and grown into a mature ecosystem of specialized packages for data processing and analysis. It allows you to have uncountable and fast experimentations, easy theory development, and prompt deployment of scientific applications.

At present, the core Python characteristics that render it an indispensable data science tool are as follows:

  • It offers a large, mature system of packages for data analysis and machine learning. It guarantees that you will get all that you may need in the course of a data analysis, and sometimes even more.
  • Python can easily integrate different tools and offers a truly unifying ground for different languages, data strategies, and learning algorithms that can be fitted together easily and which can concretely help data scientists forge powerful solutions. There are packages that allow you to call code in other languages (in Java, C, Fortran, R, or Julia), outsourcing some of the computations to them and improving your script performance.
  • It is very versatile. No matter what your programming background or style is (object-oriented, procedural, or even functional), you will enjoy programming with Python.
  • It is cross-platform; your solutions will work perfectly and smoothly on Windows, Linux (even on small-sized distributions, suitable for IoT on tiny-PCs like Raspberry Pi, Arduino and so on), and Mac OS systems. You won't have to worry all that much about portability.
  • Although interpreted, it is undoubtedly fast compared to other mainstream data analysis languages such as R and MATLAB (though it is not comparable to C, Java, and the newly emerged Julia language). Moreover, there are also static compilers such as Cython or just-in-time compilers such as PyPy that can transform Python code into C for higher performance.
  • It can work with large in-memory data because of its minimal memory footprint and excellent memory management. The memory garbage collector will often save the day when you load, transform, dice, slice, save, or discard data using various iterations and reiterations of data wrangling.
  • It is very simple to learn and use. After you grasp the basics, there's no better way to learn more than by immediately starting with the coding.
  • Moreover, the number of data scientists using Python is continuously growing: new packages and improvements have been released by the community every day, making the Python ecosystem an increasingly prolific and rich language for data science.
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