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Data Science and Analysis

Data Science and Analysis (10)

  • Data Structures and Algorithm Analysis

    By Clifford A. Shaffer This book describes many techniques for representing data. With the third edition, there is explicit coverage of some design patterns that are encountered when programming the basic data structures and algorithms covered in the book.

  • Mathematical Problems in Data Science

    By Li M. Chen, Zhixun Su and Bo Jiang This book contains state-of-the-art knowledge for researchers in data science. It also presents various problems in BigData and data science. We first introduce important statistical and computational methods for data analysis. For example, we discuss the principal component analysis for the dimension reduction of massive data sets. Then, we introduce graph theoretical methods such as GraphCut, the Laplacian matrix, and Google PageRank for data search and classification. We also discuss efficient algorithms, the hardness of problems involving various types of BigData, and geometric data structures.

  • Computational Methods for Data Science

    By Vince Melfi Contents include Data, Introduction to R and RStudio, Data Structures in R, Graphics in R, Working with Data, etc

  • Data Science from Scratch

    By Joel Grus In this book, we will be approaching data science from scratch. That means we’ll be building tools and implementing algorithms by hand in order to better understand them. I put a lot of thought into creating implementations and examples that are clear, well-commented, and readable.

  • Introducing Data Science: Big data, machine learning, and more, using Python tools

    By DAVY CIELEN, ARNO D. B. and MEYSMAN MOHAMED AL This book is an introduction to the field of data science. Seasoned data scientists will see that we only scratch the surface of some topics. For our other readers, there are some prerequisites for you to fully enjoy the book. A minimal understanding of SQL, Python, HTML5, and statistics or machine learning is recommended before you dive into the practical examples.

  • Data Science at the Command Line

    By Jeroen Janssens This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

  • Foundations of Data Science

    By John Hopcroft and Ravindran Kannan This book starts with the treatment of high dimensional geometry. The mathematical areas most relevant to dealing with high-dimensional data are matrix algebra and algorithms. We focus on singular value decomposition, a central tool in this area.

  • Data Science in R

    By Deborah Nolan and Duncan Temple Lang Data Science in R A Case Studies Approach to Computational Reasoning and Problem Solving. The books will appeal to programmers and developers of R software, as well as applied statisticians and data analysts in many fields. The books will feature detailed worked examples and R code fully integrated into the text, ensuring their usefulness to researchers, practitioners and students.

    Data Science in R

  • Data Science: A Gentle Introduction

    James G. Scott | This book is about data science. This term has no precise definition. Data science involves some statistics, some probability, some computing—and above all, some knowledge of your data set (the “science” part).

  • Excel Data Analysis: Modeling and Simulation

    Hector Guerrero | This book is targeted at the student or practitioner that is looking for a single introductory Excel-based resource that covers three essential business skills—Data Analysis, Business Modeling, and Simulation.

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