Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, based on Columbia University’s Introduction to Data Science class, interdisciplinary field that’s so clouded in hype? This insightful book, tells you what you need to know.
Doing Data Science: Straight Talk from the Frontline - In many of these chapter-long lectures, data scientists from companies such as Google, and eBay share new algorithms, Microsoft, methods, and models by presenting case studies and the code they use.
Practical Statistics for Data Scientists: 50 Essential ConceptsO'Reilly Media - This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. Courses and books on basic statistics rarely cover the topic from a data science perspective.
If you’re familiar with the r programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn:why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data.
Practical Statistics for Data Scientists: 50 Essential Concepts - Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training.
Reproducible Research with R and R Studio, Second Edition Chapman & Hall/CRC The R SeriesRoutledge - It saves you time searching for information so that you can spend more time actually addressing your research questions. New to the second edition the rmarkdown package that allows you to create reproducible research documents in pdf, enabling you to take full advantage of relative file paths so that your documents are more easily reproducible across computers and systems The dplyr, and Microsoft Word formats using the simple and intuitive Markdown syntax Improvements to RStudio’s interface and capabilities, HTML, magrittr, such as its new tools for handling R Markdown documents Expanded knitr R code chunk capabilities The kable function in the knitr package and the texreg package for dynamically creating tables to present your data and statistical results An improved discussion of file organization, and tidyr packages for fast data manipulation Numerous modifications to R syntax in user-created packages Changes to GitHub’s and Dropbox’s interfaces Create Dynamic and Highly Reproducible Research This updated book provides all the tools to combine your research with the presentation of your findings.
This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. All the tools for gathering and analyzing data and Presenting Results Reproducible Research with R and RStudio, Second Edition brings together the skills and tools needed for doing and presenting computational research.
Reproducible Research with R and R Studio, Second Edition Chapman & Hall/CRC The R Series - Supplementary files used for the examples and a reproducible research project are available on the author’s website. Using straightforward examples, the book takes you through an entire reproducible research workflow.
Data Science from Scratch: First Principles with PythonO'Reilly Media - Data science libraries, modules, and toolkits are great for doing data science, frameworks, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask.
Data Science from Scratch: First Principles with Python - This book provides you with the know-how to dig those answers out. Get a crash course in pythonlearn the basics of linear algebra, neural networks, and clusteringexplore recommender systems, and probability—and understand how and when they're used in data scienceCollect, MapReduce, natural language processing, network analysis, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, explore, clean, Naive Bayes, linear and logistic regression, munge, decision trees, statistics, and databases Oreilly Associates Inc.
The Statistical Sleuth: A Course in Methods of Data AnalysisCengage Learning - Used book in Good Condition. With interesting examples, and data problems, and a variety of exercise types conceptual, real data, computational, the authors get readers excited about statistics. The material is independent of any specific software package, and prominently treats modeling and interpretation in a way that goes beyond routine patterns.
The statistical sleuth: a course in methods of data analysis, third edition offers an appealing treatment of general statistical methods that takes full advantage of the computer, both as a computational and an analytical tool. Oreilly Associates Inc. The book focuses on a serious analysis of real case studies, strategies and tools of modern statistical data analysis, the interplay of statistics and scientific learning, and the communication of results.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPythonO'Reilly Media - Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3. 6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. Python for data analysis.
Python for data analysis wes mckinney. You’ll learn the latest versions of pandas, IPython, NumPy, and Jupyter in the process. Written by wes mckinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Data files and related material are available on GitHub.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython - Use the ipython shell and jupyter notebook for exploratory computinglearn basic and advanced features in NumPy Numerical PythonGet started with data analysis tools in the pandas libraryUse flexible tools to load, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, transform, dice, merge, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, clean, detailed examples Oreilly Associates Inc.
Used book in Good Condition. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic ThinkingO'Reilly Media - Python for data analysis wes mckinney. Written by renowned data science experts foster provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
. O'reilly Media. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates Oreilly Associates Inc.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking - This guide also helps you understand the many data-mining techniques in use today. Based on an mba course provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects.
Python for data analysis. Used book in Good Condition.
R for Data Science: Import, Tidy, Transform, Visualize, and Model DataO'Reilly Media - Used book in Good Condition. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. O reilly Media. Python for data analysis wes mckinney. This book introduces you to r, rstudio, a collection of R packages designed to work together to make data science fast, fluent, and the tidyverse, and fun.
Python for data analysis. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors hadley wickham and garrett Grolemund guide you through the steps of importing, exploring, wrangling, and modeling your data and communicating the results.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - O'reilly Media. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to:wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, generate hypotheses, and results Oreilly Associates Inc.
Learn how to use R to turn raw data into insight, knowledge, and understanding.
Naked Statistics: Stripping the Dread from the DataW. W. Norton & Company - The best math teacher you never had. San francisco chronicle once considered tedious, chief economist at Google, the field of statistics is rapidly evolving into a discipline Hal Varian, has actually called “sexy. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds.
. Brilliant, funny. Used book in Good Condition. Python for data analysis wes mckinney. Oreilly Associates Inc. With the wit, and sheer fun that turned Naked Economics into a bestseller, accessibility, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life. W w norton Company.
Naked Statistics: Stripping the Dread from the Data - Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. O reilly Media. O'reilly Media. How can we catch schools that cheat on standardized tests? how does netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
He clarifies key concepts such as inference, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, correlation, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
Python Data Science Handbook: Essential Tools for Working with DataO'Reilly Media - W w norton Company. For many researchers, python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. O reilly Media. Used book in Good Condition. O'reilly Media. Python for data analysis. O\'reilly Media. Python for data analysis wes mckinney. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you’ll learn how to use:ipython and jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms Oreilly Associates Inc.
Python Data Science Handbook: Essential Tools for Working with Data - Several resources exist for individual pieces of this data science stack, NumPy, Matplotlib, but only with the Python Data Science Handbook do you get them all—IPython, Pandas, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.
Advanced and Multivariate Statistical MethodsRoutledge - Students also learn how to compute each technique using SPSS software. O'reilly Media. New to the sixth edition Instructor ancillaries are now available with the sixth edition. All spss directions and screenshots have been updated to Version 23 of the software. Oreilly Associates Inc. Python for data analysis wes mckinney.
Student learning objectives have been added as a means for students to target their learning and for instructors to focus their instruction. O reilly Media. Python for data analysis. W w norton Company. Ideal for non-math majors, advanced and Multivariate Statistical Methods teaches students to interpret, present, and write up results for each statistical technique without overemphasizing advanced math.
Advanced and Multivariate Statistical Methods - O\'reilly Media. This highly applied approach covers the why, what, when and how of advanced and multivariate statistics in a way that is neither too technical nor too mathematical. Key words are reviewed and reinforced in the end of chapter material to ensure that students understand the vocabulary of advanced and multivariate statistics.
Used book in Good Condition.