Noel Megane. Python Data Science: The Complete Crash Course to Mastering Python Data Science on your own

Independently published, December 11, 2021. — 60 p. — EAN 1230005302109.The comprehensive, easy-to-understand handbook for anyone interested in learning more about Data Science, what it has to offer, and how to avoid frequent beginner mistakes to master it in the near future.Are you interested in learning more about Data Science?
Are you interested in learning it as a self-taught skill?
Have you ever considered studying it to one day become a brilliant computer programmer?If you answered yes to any of these questions, you've found the appropriate resource to assist you in achieving your objectives!
Python Data Science is intended to be a guide that will mold you into a smart and master computer programmer. It contains the kinds of content that you can trust to guide you through the world of data science and everything you'll need to excel in it. You'll not only come across as intelligent and well-informed, but you'll also be an expert in your field.There are many great coding languages to work with when the time comes to tackle data science, but many agree that the power, libraries, and ease of use and learning of Python make it one of the best choices to deal with this idea . We'll also take some time to look at a few libraries we'll be working with when it comes to Python, including NumPy and its arrays, Seaborn, and Matplotlib, to get all the work done in no time.This is just the beginning of some of the fantastic things we can do when it's time to start learning about data. We can spend our time looking at what machine learning is, the different types of machine learning, and how we can bring it all together when the time comes to sort our data and find the right patterns and insights in the process.You can build complex data structures out of them as they are powerful in storing data; however, they are not good at manipulating the data. They are not optimal in terms of power and processing speed, which are critical when working with complex algorithms. Therefore, we use NumPy and its ndarray object, which stands for "n-dimensional array". Let's look at the properties of the NumPy array:It is optimal and fast in data transfer. When you work with complex data, you want memory to handle it efficiently rather than being a bottleneck.You can vectorize. In other words, you can perform linear algebra calculations and operations on specific elements without having to use a "for" loop. This is a big plus for NumPy because Python "for" loops are resource-intensive, making working with a large number of loops instead of ndarray expensive.You will need to use tools or libraries such as SciPy and Scikit-learn in data analysis operations. They cannot be used without arrays because they are required as input, otherwise the functions will not work as intended.This book includes:
Brief introduction to Data Science
The difference between data science and data analysis
Short introduction and understanding of the NumPy library
Basics of Python
And so much moreChapter 1: What's the Difference Between Data Science and Analytics?
Chapter 2: Introduction to NumPy
Chapter 3: Data Manipulation with Pandas
Chapter 4: Visualization with Matplotlib
Chapter 5: Machine Learning