Introduction 1
Python For Data Science – The Ultimate Beginners’ Guide to Learning Python Data Science Step by Step
According to a report published by LinkedIn, data science is one of the fastest growing tech fields within the past 7 years. The need for companies to have a better understanding of data generated via their business has motivated a lot of interest in the field. However, there is a gap to be breached as the supply of competent data scientists is way lower than the demand. This makes data science a very in-demand skill, with generous compensation for the few that possess the relevant portfolio.
Remember, data science leverages the exceptional processing and data manipulation capacity of computers? To do this, the data scientist must communicate the task in a clear and logical manner to the computer.
The outline of this book is detailed below, and it is a guide for maximizing your use of this book depending on your level in programming. On this note, I wish you Godspeed as you journey through this book to becoming a data scientist with Python.
This book is a comprehensive guide for beginners to learn Python Programming, especially its application for Data Science.
Introduction 2
Python For Data Science – Comprehensive Guide of Tips and Tricks using Python Data Science Theories
Python for Data Science is a comprehensive guide about how to perform data science with Python. This book is for students, researchers, and developers who are technically-minded, and have a wide background in writing code as well as using numerical and computational tools. However, many of you may don’t wish to learn Python, but instead wish to learn the language in hopes of utilizing it as a means for computational and data-intensive science.
The aim of this book is not meant to serve as a kind of introduction to Python or even programming in general; we presume readers will get their hands on this book already possess ample amount of knowledge in the Python language, which includes assigning variables, defining functions, controlling a program’s flow, calling methods of objects, and other basic operations. Rather, the book was put together to assist users of Python to understand how to use the data science stack of Python – with libraries including NumPy, pandas, Matplotlib and other such tools – with the aim of effectively manipulating, storing, and getting data insight.
In this book, we’ll cover a variety of topics, including several libraries, such as NumPy that offers the ndarray for efficient manipulation and storage of dense data arrays in Python. Then you’ll be able to learn how to manipulate data using Pandas, a library that offers the DataFrame object for efficient manipulation and storage of columnar/label data in Python.
We are confident that you will make fine a data scientists going forward!
Introduction 3
Python For Data Science – Advanced and Effective Strategies of Using Python Data Science Theories
Years ago, when the concept of data science was first introduced, it only meant gathering statistical data and cleaning data sets. It was simply just the science of collecting and presenting data. Together with technology evolution, however, paired with the increasing number of information we now have and continuously acquire, data science means so much more.
Business intelligence is the process of collecting, integrating, and analyzing data for managers and executive officers with the primary goal of using this data for business decisions. If you look at the definition, it is very similar to what data science does as well. It acquires information, processes it, analyzes it, and also presents the data to relevant people to make smart decisions.
What this guide intends to do is go in-depth on some of the more popular advanced data science theories using Python, as well as an overview of machine learning, algorithms, and an in-depth tutorial on using SciPy to optimize your data so, let’s dive in.
