In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. Found inside – Page iiThis book describes process mining use cases and business impact along the value chain, from corporate to local applications, representing the state of the art in domain know-how. Found insideData Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. Found inside – Page 171Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Found insideWhether you are starting out in your career, looking to advance, navigating a mid-career shift, or anywhere in between, this is the book you need to thrive in the New World of Work. Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. In this book you find out succinctly how leading companies are getting real value from Big Data – highly recommended read!" —Arthur Lee, Vice President of Qlik Analytics at Qlik Data Science Secrets is the #1 strategy guide to break into the field of data and get hired as a Data Scientist, Data Analyst, or Data Engineer. Presents case studies and instructions on how to solve data analysis problems using Python. Found inside – Page iMany of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Found inside – Page 23There are several excellent Data Science introductory courses offered by top universities through ... 23 2.4 Learning Data Science 2.4.2 Picking a Career Path. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Found inside – Page iThis book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at ... Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This ... Presents an introduction to the new programming language for the Java Platform. "This book is about the fundamentals of R programming. Found inside – Page iiiThis book covers both classical and modern models in deep learning. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. "This book describes the process of analyzing data. Found insideThe need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Found inside – Page 230As one example, IBM has partnered with Coursera to provide learning paths for ... direction in areas with job growth such as data science and cybersecurity. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found inside – Page 149With the flexible nature of learning, MOOCs have attracted learners who have a ... purposes influenced their SRL in a MOOC about data science by Coursera. Found insideThe Python ecosystem with scikit-learn and pandas is required for operational machine learning. Based on author Emmett Cox's thirty years of retail experience at Walmart, Kmart, and GE Money Global, this comprehensive reference guide provides fact-based, field-tested analytic strategies that you can replicate in your own company, ... Found insideThis book constitutes revised tutorial lectures of the 7th European Business Intelligence and Big Data Summer School, eBISS 2017, held in Bruxelles, Belgium, in July 2017. Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. Found insideStyle and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. Getting Started with Data Science takes its approach from worldwide best-sellers like Freakonomics and the books of Malcolm Gladwell: it teaches through a powerful narrative packed with unforgettable stories. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... This strategy guide will help you decide whether online learning or the self-teaching is best for your goals, and you'll be set up for success. Don't sign up for a course or hit the books until you read this guide! 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 work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. This guide to the top platforms breaks down the costs and benefits of each one, so you'll be able to make the best decision and not worry about wasting money on the wrong platform. Found insidePython Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Found inside – Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Endorsed by top AI authors, academics and industry leaders, The Hundred-Page Machine Learning Book is the number one bestseller on Amazon and the most recommended book for starters and experienced professionals alike. "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- Found insideThis book includes: Data Science interview questions and answers; Help preparing for Machine Learning Interviews; Top 25 Interview Questions for Data Analyst/Scientist roles; An in-depth overview of Data Science Interview Process; How to ... "Designed to teach people to program even if they have no prior experience. Found inside – Page iiiThis is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. Found inside – Page 1In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Found inside – Page iA basic grasp of data science is recommended in order to fully benefit from this book. This book seeks to promote the exploitation of data science in healthcare systems. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found insideAbout This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with ... A catalog of solutions to commonly occurring design problems, presenting 23 patterns that allow designers to create flexible and reusable designs for object-oriented software. Found insideThis book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised ... Systematic and methodological development of visual analytics was detected, you’ll learn how many of most! 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