Python for Data Analysis: A Basic Guide for Beginners to Learn the Language of Python Programming Codes Applied to Data Analysis with Libraries Software Pandas, Numpy, and IPythonКНИГИ » ПРОГРАММИНГ
Название: Python for Data Analysis: A Basic Guide for Beginners to Learn the Language of Python Programming Codes Applied to Data Analysis with Libraries Software Pandas, Numpy, and IPython Автор: Oliver R. Simpson Издательство: Amazon Digital Services LLC Год: 2019 Страниц: 137 Язык: английский Формат: epub, rtf, pdf (conv) Размер: 10.1 MB
Are you a new business owner? Or an entrepreneur looking to catch up to the big companies in your industrial sector? If you want to be a data analysis expert, and looking to develop a sound understanding of all the nitty-gritty of this field, then this book is here to rescue you by simplifying and providing a working definition of “Big Data” and “Big Data Analytics.”
In addition to that, this book will also provide you a concise overview of the fundamentals of Machine Learning (ML), the underlying challenges and limitations of engineering machines to ‘think’ using open source data analysis libraries built on Python such as “Scikit-Learn” and “Pandas”, with example from open source data sets that you can easily access and get your hands dirty.
Thanks to the smart and savvy customer of today, the competition to gain new customers while retaining existing customers is fierce. As a result, companies are increasingly relying upon cutting edge technologies such as big data analytics, data mining technology, machine learning, and artificial intelligence technology to gain an edge over the competition.
Few of the many reasons why you should buy this book include:
• Learn how our increasing online presence has led to the development of large volumes of data called "Big Data" and its significance in our modern lives. • Learn all about the historical development of the current explosion in this field of Big Data Analytics and how it differs from data visualization techniques. • Dig deep into the data mining process, the benefits of using data mining technology, the challenges facing the data mining technology, and learn about some data mining tools that you can leverage for your business. • Get familiar with the “Python” programming language with a detailed overview of a variety of Data Analysis libraries, including “Django,” “Scikit-Learn,” “NumPy,” “Pandas,” and “IPython” among others. • Deep dive into the concept of personalized marketing, predictive analytics, customer analytics, and exploratory data analysis presented with details on how you can make sense out of all your customer behavioral data. • Get a step-by-step walkthrough of how the "Scikit-Learn" platform can be used to create your own predictive data analysis model by processing Big Data to produce high-quality training and test data sets. • Learn how big data and big data analytics are being leveraged by businesses across the industrial spectrum, with a focus on the eCommerce, healthcare, and entertainment industry.
This book is filled with real-life examples to help you understand the nitty-gritty of all the concepts as well as names and descriptions of multiple tools that you can further explore and selectively implement in your business to reap the benefits of these cutting-edge technologies. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices.
Introduction Chapter 1: Introduction to Big Data and Big Data Analysis Chapter 2: Fundamentals of Python and Data Analysis Libraries Chapter 3: Predictive Modeling, Data Visualization and Creation of Training Data Set Chapter 4: Applications of Big Data Analysis Conclusion
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Python for Data Analysis, 2nd Edition Название: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition Автор: Wes McKinney Издательство: O'Reilly Media Год:...
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Python for Data Analysis: A Basic Guide for Beginners to Learn the Language of Python Programming Codes Applied to Data Analysis with Libraries Software Pandas, Numpy, and IPython 
