Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.
Table of Contents (11 chapters)

Exploratory Data Analysis of Boston Housing Data with NumPy Statistics

Exploratory data analysis (EDA) is a crucial component of a data science project (as shown in Figure Data Science Process). Even though it is a very important step before applying any statistical model or machine learning algorithm to your data, it is often skipped or underestimated by many practitioners:

Data Science Process (https://en.wikipedia.org/wiki/Data_analysis)

John Wilder Tukey promoted exploratory data analysis in 1977 with his book Exploratory Data Analysis. In his book, he guides statisticians to analyze their datasets statistically by using several different visuals, which will help them to formulate their hypotheses. In addition, EDA is also used to prepare your analysis for advance modeling after you identify the key data characteristics and learn which questions you should ask about your...