Название: Python Data Science: The Complete Step-by-Step Python Programming Guide. Learn How to Master Big Data Analysis and Machine Learning (2022 Edition For Beginners)
Автор: Ivor Osborne
Издательство: Amazon.com Services LLC
Год: 2021
Язык: английский
Формат: pdf, azw3, epub
Размер: 10.2 MB

Data Science is currently one of the most popular buzzwords in the business world. Many businesses understand the value of information collection, but because they can collect so much data in such a short period, the real question is: "What is the next step?"

Data Science encompasses all of the steps you take with data, such as collecting and cleaning it if it comes from multiple sources, analyzing it, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good Data Visualizations.

At some point throughout your journey of dealing with all of that data and attempting to understand it, you will determine that it is time to truly view and visualize the data. Sure, we could put all of that information into a table or write it out in long, monotonous paragraphs, but it will make it difficult to focus and understand the comparisons and other information that comes with it. One of the greatest ways to do this is to take that information and, after it has been thoroughly examined, apply data visualization to make it work for our purposes. The good news is that the Matplotlib library, which is a NumPy and SciPy extension, can assist us in creating all of them. The Matplotlib library can help you visualize your data as a line graph, a pie chart, a histogram, or in another way.

Convolutional Neural Networks (CNNs) are a type of deep neural network that has proven to be particularly effective in a variety of computer science applications such as object recognition, object categorization, and computer vision. ConvNets have been used for many years to differentiate between faces, recognize objects, and power vision in self-driving cars and robotics. A ConvNet can recognize and provide captions for a large number of visual scenes. ConvNets may also recognize everyday items, animals, and humans. Convolutional Neural Networks have recently been employed successfully in natural language processing tasks such as sentence classification. Convolutional Neural Networks are thus one of the most essential tools for machine learning and deep learning problems. LeNet was the first Convolutional Neural Network to be introduced, and it played a vital role in propelling the broader area of deep learning.

This is what you will learn in Python Data Science.

You will learn about the main steps required to correctly implement Data Science techniques, as well as the algorithms that will help you sort through the data and see some amazing results. Some of the topics we will cover inside include
What is data science all about, and why do so many businesses use it to gain a competitive advantage.
Why Python and how to use it to implement Data Science
What is the connection between Machine Learning and Data Science, and how can they be combined?
The most important Data Structures and Object-Oriented Python, with practical codes and exercises to use Python Functions and Modules in Python The 7 most important algorithms and models in Data Science Data Aggregation and Group Operations
9 Crucial Data Mining Techniques in Data Science
Interaction with databases and data in the cloud
And Much More!....

Whereas most books only focus on collecting and cleaning data, this book goes a step further, guiding how to perform a proper analysis to extract valuable information critical for a business. Don't pass up this chance to learn more about these subjects.

Even if you've never used Data Science techniques before, they're much easier to learn than they appear. You simply require the proper direction. Python Data Science teaches you everything you need to know straightforwardly and practically. You will learn how to manipulate and process datasets, the principles of Python programming, and the most important real-world applications, regardless of your prior experience.