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 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 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
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 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.
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