This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

Annotation This broad, deep, but not-too-technical guide introduces you to 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. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful." Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you’re a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Teach your students how to think like scientists. This book shows you practical ways to incorporate science thinking in your classroom using simple "Thinking Tasks" that you can insert into any lesson. What is science thinking and how can you possibly teach and assess it? How is science thinking incorporated into the Next Generation Science Standards (NGSS) and how can it be weaved into your curriculum? This book answers these questions. This practical book provides a clear, research-verified framework for helping students develop scientific thinking as required by the NGSS. Your students will not be memorizing content but will become engaged in the real work scientists do, using critical thinking patterns such as: Recognizing patterns, Inventing new hypotheses based on observations, Separating causes from correlations, Determining relevant variables and isolating them, Testing hypotheses, and Thinking about their own thinking and the relative value of evidence. The book includes a variety of sample classroom activities and rubrics, as well as frameworks for creating your own tools. Designed for the busy teacher, this book also shows you quick and simple ways to add deep science thinking to existing lessons.

This book will provide you with an in-depth understanding of machine learning, data science, predictive analytics, big data, data analytics, predictive modeling, and the applications of machine learning.If you are looking to get a head start on your dream career or improve your knowledge as a data scientist, credit risk analyst, credit risk modeler, or data analyst, this is the book for you. It is easy enough for a beginner to understand yet will provide you with a comprehensive understanding of the subject when read cover to cover. Every single step of building a predictive model is included from selecting the right variables and determining the appropriate type of model to testing and updating the model. This book contains numerous illustrative case studies and real-life application examples, graphing examples and techniques, and sophisticated thinking skills. Every chapter explains a different facet of machine learning and all of the various tools that one can use as a data scientist.

Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away

Solve business problems with data-driven techniques and easy-to-follow Python examples Ê KEY FEATURESÊÊ _ Essential coverage on statistics and data science techniques. _ Exposure to Jupyter, PyCharm, and use of GitHub. _ Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTIONÊÊ This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you willÊ clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN _ Rapid understanding of Python concepts for data science applications. _ Understand and practice how to run data analysis with data science techniques and algorithms. _ Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. _ Become self-sufficient to perform data science tasks with the best tools and techniques. Ê WHO THIS BOOK IS FORÊÊ This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Ê TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All ChaptersÕ Learning

The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures.

Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.