Luiz Felipe Martins – författare

Become an expert at using Python for advanced statistical analysis of data using real-world examplesKey Features[*] Clean, format, and explore data using graphical and numerical summaries[*] Leverage the IPython environment to efficiently analyze data with Python[*] Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataBook DescriptionPython, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.What you will learn[*] Read, sort, and map various data into Python and Pandas[*] Recognise patterns so you can understand and explore data[*] Use statistical models to discover patterns in data[*] Review classical statistical inference using Python, Pandas, and SciPy[*] Detect similarities and differences in data with clustering[*] Clean your data to make it useful[*] Work in Jupyter Notebook to produce publication ready figures to be included in reportsWho this book is forIf you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.

Become an expert at using Python for advanced statistical analysis of data using real-world examples

About This Book

Clean, format, and explore data using graphical and numerical summariesLeverage the IPython environment to efficiently analyze data with PythonPacked with easy-to-follow examples to develop advanced computational skills for the analysis of complex data

Who This Book Is For

If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.

What You Will Learn

Read, sort, and map various data into Python and PandasRecognise patterns so you can understand and explore dataUse statistical models to discover patterns in dataReview classical statistical inference using Python, Pandas, and SciPyDetect similarities and differences in data with clusteringClean your data to make it usefulWork in Jupyter Notebook to produce publication ready figures to be included in reports

In Detail

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!

Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You''ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.

You''ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you''ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we''ll focus on performing regression using computational tools and you''ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you''ll discover how to use Python''s tools for supervised machine learning.

Style and approach

This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.

In data science, it is difficult to present interesting visual or technical content, as it involves scientific notations that are not easy to type in a normal document format. IPython provides a web-based UI called Notebook, which creates a working environment for interactive computing that combines code execution with computational documents. IPython Notebook makes the task simpler as it was developed for scientific programming to solve larger problems through a series of smaller programs. IPython Notebook is used to learn Python in a fun and interactive way and to do some serious parallel / technical computing.

The book begins with an introduction to the efficient use of IPython Notebook for interactive computation. The book then focuses on the integration of technologies such as matplotlib, pandas, and SciPy. The book is aimed at empowering you to work with IPython Notebook for interactive computing, configuring it, creating your own notebooks / research documents. You will learn how IPython lets you perform efficient computations through examples with NumPy, data analysis with pandas, and visualization with matplotlib.