The Blavida Resource Library provides links to PDF documents of relevant research and study materials on different categories of career and discipline. It simplifies research and studying as it brings all useful learning materials to a single platform.
Resource Categories
Data Science and Analysis (40)
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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).
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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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Beginning Data Science in R_ Data Analysis, Visualization, and Modelling for the Data Scientist
Data Science and Analysis
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The Art of Data Science_ A Guide for Anyone Who Works with Data
Data Science and Analysis
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Applied Biostatistical Principles and Concepts _ Clinicians' Guide to Data Analysis and Interpretation
Data Science and Analysis
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How to Use SPSS®_ A Step-By-Step Guide to Analysis and Interpretation
Data Science and Analysis
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Making sense of data I _ a practical guide to exploratory data analysis and data mining
Data Science and Analysis
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Python for Data Analysis
Data Science and Analysis
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Python Data Analytics_ Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language
Data Science and Analysis
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Data Analysis and Visualization Using Python Analyze Data to Create Visualizations for BI Systems
Data Science and Analysis
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Microsoft Excel 2010_ Data Analysis and Business Modeling eBook
Data Science and Analysis
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Derivatives analytics with Python _ data analysis, models, simulation, calibration and hedging
Data Science and Analysis
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Excel Data Analysis_ Modeling and Simulation
Data Science and Analysis
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Microsoft Excel Data Analysis For Dummies (3rd Ed.)
Data Science and Analysis
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Introduction to Python for Econometrics, Statistics and Data Analysis
Data Science and Analysis
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Data Analysis From Scratch With Python_ Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib
Data Science and Analysis
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Business Analytics_ Data Analysis & Decision Making
Data Science and Analysis
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Data analysis and graphics with R
Data Science and Analysis
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Data Science and Big Data Analytics
Data Science and Analysis
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Data Structures and Algorithm Analysis in C
Data Science and Analysis
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Scala Data Analysis Cookbook_ Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes
Data Science and Analysis
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Qualitative Data Analysis_ A Methods Source book
Data Science and Analysis
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Applied Text Analysis with Python_ Enabling Language-Aware Data Products with Machine Learning
Data Science and Analysis
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Natural Language Processing Recipes_ Unlocking Text Data with Machine Learning and Deep Learning using Python
Data Science and Analysis
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Introduction to Statistics and Data Analysis _ With Exercises, Solutions and Applications in R
Data Science and Analysis
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Machine learning with R _ discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
Data Science and Analysis
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Data Analysis and Graphics Using R – an Example-Based Approach
Data Science and Analysis
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Data Analysis Using SQL and Excel
Data Science and Analysis
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Data Analysis with Microsoft Excel
Data Science and Analysis
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Excel Data Analysis
Data Science and Analysis
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DATA ANALYSIS FOR NETWORK CYBER-SECURITY
Data Science and Analysis
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Excel_data_analysis_tutorial
Data Science and Analysis
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