Mastering Numerical Computing with NumPy

Master scientific computing and perform complex operations with ease

Umit Mert Cakmak , Mert Cuhadaroglu 지음

Packt(GCO Science) 2018년 06월 28일 출간

작품소개

책소개

-

목차

▶TABLE of CONTENTS
1: WORKING WITH NUMPY ARRAYS
2: LINEAR ALGEBRA WITH NUMPY
3: EXPLORATORY DATA ANALYSIS OF BOSTON HOUSING DATA WITH NUMPY STATISTICS
4: PREDICTING HOUSING PRICES USING LINEAR REGRESSION
5: CLUSTERING CLIENTS OF A WHOLESALE DISTRIBUTOR USING NUMPY
6: NUMPY, SCIPY, PANDAS, AND SCIKIT-LEARN
7: ADVANCED NUMPY
8: OVERVIEW OF HIGH-PERFORMANCE NUMERICAL COMPUTING LIBRARIES
9: PERFORMANCE BENCHMARKS

책 속으로

▶What this book covers
？ Chapter 1, Working with Numpy Arrays, explains the basics of numerical computing with NumPy, which is a Python library for working with multi-dimensional arrays and matrices used by scientific computing applications.

？ Chapter 2, Linear Algebra with Numpy, covers the basics of linear algebra and provides practical NumPy examples.

？ Chapter 3, Exploratory Data Analysis of Boston Housing Data with NumPy Statistics, explains exploratory data analysis and provides examples using Boston Housing Dataset.

？ Chapter 4, Predicting Housing Prices Using Linear Regression, covers supervised learning and provides a practical example for predicting housing prices using linear regression.

？ Chapter 5, Clustering Clients of a Wholesale Distributor Using NumPy, explains unsupervised learning and provides a practical example of a clustering algorithm to model a wholesale distributor sales dataset, which contains information on annual spending in monetary units for diverse product categories.

？ Chapter 6, NumPy, SciPy, Pandas, and Scikit-Learn, shows the relationship between NumPy and other libraries and provides examples of how they are used together.

？ Chapter 7, Advanced Numpy, explains the advanced considerations of NumPy library usage.

？ Chapter 8, Overview of High-Performance Numerical Computing Libraries, introduces several low-level, high-performance numerical computing libraries and their relationship with NumPy.

？ Chapter 9, Performance Benchmarks, takes a deep dive into the performance of NumPy algorithms depending on the underlying high-performance numerical computing libraries.

출판사 서평

▶ Preface
If you are trying to hone your skills in the field of data science, there are many books and courses out there with varying levels of difficulty. What usually happens is that you start to study introductory resources and then continue with more in-depth, technical ones to get a taste of a new field or technology. If you were following this kind of learning path for sometime, you must have realized that it becomes very time consuming journey. We, as lifelong learners, need books with more compact representation of knowledge and experience which requires the right balance between theory and practice. This book aims to bring beginner, intermediate, and advanced concepts together and it is our humble effort to build up your knowledge from scratch.

This book assumes no previous background of scientific computing and will introduce various subjects using practical examples. It may sometimes feel like separate topics pulled together randomly and the book's flow doesn't stick to one consistent path. This was a deliberate decision we made to give you a little taste of several different topics and applications.

We hope that you will read this book to have a broader overview of scientific computing as well as to master the nitty-gritty of NumPy and other supporting scientific libraries of Python such as SciPy and Scikit-Learn.

인물정보

저자(글) Umit Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

저자(글) Mert Cuhadaroglu

Mert Cuhadaroglu is a BI Developer in EPAM, developing E2E analytics solutions for complex business problems in various industries, mostly investment banking, FMCG, media, communication, and pharma. He consistently uses advanced statistical models and ML algorithms to provide actionable insights. Throughout his career, he has worked in several other industries, such as banking and asset management. He continues his academic research in AI for trading algorithms.