"A journey of a thousand miles begins with a single step." | ||
| --Laozi (604 BC - 531 BC) | ||
Data science is a relatively new knowledge domain that requires the successful integration of linear algebra, statistical modelling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.
The Python programming language, having conquered the scientific community during the last decade, is now an indispensable tool for the data science practitioner and a must-have tool for every aspiring data scientist. Python will offer you a fast, reliable, cross-platform, mature environment for data analysis, machine learning, and algorithmic problem solving. Whatever stopped you before from mastering Python for data science applications will be easily overcome by our easy step-by-step and example-oriented approach that will help you apply the most straightforward and effective Python tools to both demonstrative and real-world datasets.
Leveraging your existing knowledge of Python syntax and constructs (but don't worry, we have some Python tutorials if you need to acquire more knowledge on the language), this book will start by introducing you to the process of setting up your essential data science toolbox. Then, it will guide you through all the data munging and preprocessing phases. A necessary amount of time will be spent in explaining the core activities related to transforming, fixing, exploring, and processing data. Then, we will demonstrate advanced data science operations in order to enhance critical information, set up an experimental pipeline for variable and hypothesis selection, optimize hyper-parameters, and use cross-validation and testing in an effective way.
Finally, we will complete the overview by presenting you with the main machine learning algorithms, graph analysis technicalities, and all the visualization instruments that can make your life easier when it comes to presenting your results.
In this walkthrough, which is structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets. It will also give you hints dictated by experience to help you immediately operate on your current projects. Are you ready to start? We are sure that you are ready to take the first step towards a long and incredibly rewarding journey.
Chapter 1, First Steps, introduces you to all the basic tools (command shell for interactive computing, libraries, and datasets) necessary to immediately start on data science using Python.
Chapter 2, Data Munging, explains how to upload the data to be analyzed by applying alternative techniques when the data is too big for the computer to handle. It introduces all the key data manipulation and transformation techniques.
Chapter 3, The Data Science Pipeline, offers advanced explorative and manipulative techniques, enabling sophisticated data operations to create and reduce predictive features, spot anomalous cases and apply validation techniques.
Chapter 4, Machine Learning, guides you through the most important learning algorithms that are available in the Scikit-learn library, which demonstrates the practical applications and points out the key values to be checked and the parameters to be tuned in order to get the best out of each machine learning technique.
Chapter 5, Social Network Analysis, elaborates the practical and effective skills that are required to handle data that represents social relations or interactions.
Chapter 6, Visualization, completes the data science overview with basic and intermediate graphical representations. They are indispensable if you want to visually represent complex data structures and machine learning processes and results.
Chapter 7, Strengthen Your Python Foundations, covers a few Python examples and tutorials focused on the key features of the language that it is indispensable to know in order to work on data science projects.
This chapter is not part of the book, but it has to be downloaded from Packt Publishing website at https://www.packtpub.com/sites/default/files/downloads/0429OS_Chapter-07.pdf.
Python and all the data science tools mentioned in the book, from IPython to Scikit-learn, are free of charge and can be freely downloaded from the Internet. To run the code that accompanies the book, you need a computer that uses Windows, Linux, or Mac OS operating systems. The book will introduce you step-by-step to the process of installing the Python interpreter and all the tools and data that you need to run the examples.
This book builds on the core skills that you already have, enabling you to become an efficient data science practitioner. Therefore, it assumes that you know the basics of programming and statistics.
The code examples provided in the book won't require you to have a mastery of Python, but we will assume that you know at least the basics of Python scripting, lists and dictionary data structures, and how class objects work. Before starting, you can quickly acquire such skills by spending a few hours on the online courses that we are going to suggest in the first chapter. You can also use the tutorial provided on the Packt Publishing website.
No advanced data science concepts are necessary though, as we will provide you with the information that is essential to understand all the core concepts that are used by the examples in the book.
Summarizing, this book is for the following:
Novice and aspiring data scientists with limited Python experience and a working knowledge of data analysis, but no specific expertise of data science algorithms
Data analysts who are proficient in statistic modeling using R or MATLAB tools and who would like to exploit Python to perform data science operations
Developers and programmers who intend to expand their knowledge and learn about data manipulation and machine learning
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "When inspecting the linear model, first check the coef_ attribute."
A block of code is set as follows:
from sklearn import datasets iris = datasets.load_iris()
Since we will be using IPython Notebooks along most of the examples, expect to have always an input (marked as In:) and often an output (marked Out:) from the cell containing the block of code. On your computer you have just to input the code after the In: and check if results correspond to the Out: content:
In: clf.fit(X, y) Out: SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
When a command should be given in the terminal command line, you'll find the command with the prefix $>, otherwise, if it's for the Python REPL, it will be preceded by >>>:
$>python >>> import sys >>> print sys.version_info
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