Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. 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. 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 science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data
Published by: O'Reilly Media | Publication date: 05/10/2017Kindle book details: Kindle Edition, 318 pages
Join the technological revolution that’s taking the financial world by storm. Mastering Bitcoin is your guide through the seemingly complex world of bitcoin, providing the knowledge you need to participate in the internet of money. Whether you’re building the next killer app, investing in a startup, or simply curious about the technology, this revised and expanded second edition provides essential detail to get you started.Bitcoin, the first successful decentralized digital currency, is still in its early stages and yet it’s already spawned a multi-billion-dollar global economy open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides the knowledge. You simply supply the passion.The second edition includes:
- A broad introduction of bitcoin and its underlying blockchain—ideal for non-technical users, investors, and business executives
- An explanation of the technical foundations of bitcoin and cryptographic currencies for developers, engineers, and software and systems architects
- Details of the bitcoin decentralized network, peer-to-peer architecture, transaction lifecycle, and security principles
- New developments such as Segregated Witness, Payment Channels, and Lightning Network
- A deep dive into blockchain applications, including how to combine the building blocks offered by this platform into higher-level applications
- User stories, analogies, examples, and code snippets illustrating key technical concepts
Published by: O'Reilly Media | Publication date: 06/12/2017Kindle book details: Kindle Edition, 410 pages
User experience (UX) strategy requires a careful blend of business strategy and UX design, but until now, there hasn’t been an easy-to-apply framework for executing it. This hands-on guide introduces lightweight strategy tools and techniques to help you and your team craft innovative multi-device products that people want to use.Whether you’re an entrepreneur, UX/UI designer, product manager, or part of an intrapreneurial team, this book teaches simple-to-advanced strategies that you can use in your work right away. Along with business cases, historical context, and real-world examples throughout, you’ll also gain different perspectives on the subject through interviews with top strategists.
- Define and validate your target users through provisional personas and customer discovery techniques
- Conduct competitive research and analysis to explore a crowded marketplace or an opportunity to create unique value
- Focus your team on the primary utility and business model of your product by running structured experiments using prototypes
- Devise UX funnels that increase customer engagement by mapping desired user actions to meaningful metrics
Published by: O'Reilly Media | Publication date: 05/20/2015Kindle book details: Kindle Edition, 312 pages
An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing)
This textbook provides anintroduction to the free software Python and its use for statistical dataanalysis. It covers common statistical tests for continuous, discrete andcategorical data, as well as linear regression analysis and topics from survivalanalysis and Bayesian statistics. Working code and data for Python solutionsfor each test, together with easy-to-follow Python examples, can be reproducedby the reader and reinforce their immediate understanding of the topic. Withrecent advances in the Python ecosystem, Python has become a popular languagefor scientific computing, offering a powerful environment for statistical dataanalysis and an interesting alternative to R. The book is intended for masterand PhD students, mainly from the life and medical sciences, with a basicknowledge of statistics. As it also provides some statistics background, thebook can be used by anyone who wants to perform a statistical dataanalysis.
Published by: Springer | Publication date: 07/20/2016Kindle book details: Kindle Edition, 278 pages
This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Published by: Springer | Publication date: 03/16/2016Kindle book details: Kindle Edition, 276 pages
As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist:
- Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity.
- Explore the different types of Data Scientists and the skillset each one has.
- Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people.
- Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how.
- See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background.
- Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path.
- Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice.
Published by: Technics Publications | Publication date: 05/09/2014Kindle book details: Kindle Edition, 280 pages
Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like separated Lego blocks in the closet. Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides beginners through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won't teach you how to beat the market or predict the next U.S. election but provides a concise and simple-to-understand suplement to a standard textbook. The book includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.Full Overview of Book Themes Historical Development of StatisticsData Sampling Central Tendency Measures Measures Of Spread Measures Of Position Designing Hypothesis TestsProbability & Bayes TheoryRegression AnalysisClustering AnalysisAs the launch pad to quantitative research, business optimization or a promising career in data science, it's never been a better time to brush up on statistics or learn these concepts for the very first time.
Publication date: 09/28/2017Kindle book details: Kindle Edition, 160 pages
When author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than twenty interviews, she asks leading data scientists questions about starting in the field and the future of the industry. With their stories, learn about
- the many different positions available for data scientists,
- the criteria recruiters look for when hiring,
- the best options for building your portfolio,
- the recruitment and interviewing process,
- the typical workday for a data scientist,
- the changing industry and its impact on other industries,
- the wide variety of projects that use data science, and
- the skills that can complement and improve your work.
Publication date: 11/21/2017Kindle book details: Kindle Edition, 128 pages
Statistics for Data Science: Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
Get your statistics basics right before diving into the world of data scienceKey Features
Transitioning from Data Developer to Data Scientist Declaring the Objectives A Developer's Approach to Data Cleaning Data Mining and the Database Developer Statistical Analysis for the Database Developer Database Progression to Database Regression Regularization for Database Improvement Database Development and Assessment Databases and Neural Networks Boosting your Database Database Classification using Support Vector Machines Database Structures and Machine Learning
- No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;
- Implement statistics in data science tasks such as data cleaning, mining, and analysis
- Learn all about probability, statistics, numerical computations, and more with the help of R programs
- Analyze the transition from a data developer to a data scientist mindset
- Get acquainted with the R programs and the logic used for statistical computations
- Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more
- Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis
- Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks
- Get comfortable with performing various statistical computations for data science programmatically
Published by: Packt Publishing | Publication date: 11/17/2017Kindle book details: Kindle Edition, 288 pages
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. 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. 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 advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
Published by: O'Reilly Media | Publication date: 07/27/2013Kindle book details: Kindle Edition, 414 pages