Название: Data Science from Scratch With Python: A crash course for beginners to learn Data Analysis, Programming and Machine Learning with Python Автор: Steve Geddis
Издательство: Amazon.com Services LLC
Год: 2020
Страниц: 167
Язык: английский
Формат: pdf, epub
Размер: 14.8 MB
Data are no longer just information but a resource that is growing exponentially. There are many powerful ways to store and manipulate data and there are many helpful data science tools that you can use to begin conducting your own analyses. If you are willing to understand more, this book is a crash course on data science together with the basics of Python.
Data comes in different forms, but at an advanced level, it exists in three major categories. That is structured, semi-structured, and unstructured. Data scientists are experts responsible for gathering, analyzing, and interpreting large amounts of data to help businesses and organizations. Throughout all the chapters in this book, you are going to learn what the best data scientists know about Data analytics, Machine learning, big data, data mining, and statistics. Since Data Science is a multidisciplinary field, this book covers every critical concept that you must know to become a Professional Data Scientist.
Machine Learning uses the basics of Python programming language. Python is easy to learn and supports compelling typing. Python programs are very 'natural' in that it is easy to understand and read them (thanks to the exclusion of braces and semicolons). Python programming language can run on any computer platform, ranging from Linux to Windows to Solaris, and Macintosh etcetera. The simplicity nature of Python is what makes it accessible and a perfect choice for computer programmers.
This book will guide you exploring, among others:
The Python programming environment, including fundamental Python programming techniques;
Basics of Data Analysis in Python;
What is a Data Scientist?;
Functionality and Features used for data science;
Data manipulation using the Python Pandas;
models such as neural networks, plotting and clustering;
fundamentals of big data, deep learning, artificial intelligence and machine learning;
And much much more.
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