Learn how to apply powerful data analysis techniques with popular open source Python modulesAbout This Book
- Learn how to find, manipulate, and analyze data using Python
- Perform advanced, high performance linear algebra and mathematical calculations with clean and efficient Python code
- An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects
Who This Book Is For
This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
What You Will Learn
- Install open source Python modules on various platforms
- Get to know about the fundamentals of NumPy including arrays
- Manipulate data with pandas
- Retrieve, process, store, and visualize data
- Understand signal processing and time-series data analysis
- Work with relational and NoSQL databases
- Discover more about data modeling and machine learning
- Get to grips with interoperability and cloud computing
In Detail
Python is a multi-paradigm programming language well suited for both object-oriented application development as well as functional design patterns. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. It will give you velocity and promote high productivity.
This book will teach novices about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time.
Ivan Idris
Ivan Idris has an MSc degree in Experimental Physics. His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, he worked for several companies as Java developer, data warehouse developer, and QA analyst. His main professional interests are Business Intelligence, Big Data, and Cloud Computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginner's Guide - Second Edition, NumPy Cookbook, and Learning NumPy Array, all by Packt Publishing. You can find more information and a blog with a few NumPy examples at ivanidris.net.
