The Data Model Toolkit...Data Architecture Fundamentals

Brief

Book

This book is a key resource that explains step by step, the essential skills required to rigorously construct and quality assure Logical Data Models. It also defines the processes required for organizations to successfully adopt them, and so align their systems, operations and strategic direction. In the last decade, Data Models have made the transition from being ad-hoc and limited in scope, to becoming a central pillar of the Enterprise Data Architectural landscape. Arguably they are now more important than ever. It is only with a full and agreed understanding of the ‘What?’, ‘When?’ and ‘How?’ of an organization’s data structures and flows, that we can, for example; contemplate plugging COTS products together, implement in the Cloud, ingest data into our Data Lakes, or report across the Enterprise system landscape.

SQL for Data Science...Cleaning, Wrangling and Analytics with Relational Databases

Brief

Book

The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it.

Python Data Science...Tools for Working with Data

Brief

Book

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, 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. Quite simply, this is the must-have reference for scientific computing in Python.

Intro to Python for Computer Science and Data Science...

Brief

Book

Offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like.

Practical Statistics for Data Scientists...Using R and Python

Book

Brief

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

Data Science for Marketing:...Improve your strategies...using Python and R

Brief

Book

Regardless of company size, the adoption of data science and machine learning for marketing is witnessing an exponential rise in the industry. With this book, you'll learn to implement data science techniques to identify the factors behind the successes and failures of marketing campaigns. With the help of this guide, you'll also be able to understand and predict customer behavior, and create more effectively targeted and personalized marketing strategies. You'll first get to grips with performing simple through to advanced tasks, such as extracting hidden insights from data and using them to make smart business decisions. The book will further guide you through understanding what drives sales and increases customer engagement for your products. As you explore further chapters, you'll not only learn how to gain insights into consumer behavior using exploratory analysis, but also discover the concept of A/B testing and implement it using Python and R.

Machine Learning Under a Modern Optimization Lens

Brief

Book

The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments. Philosophical principles of the book: Interpretability is materially important in the real world.  Practical tractability not polynomial solvability leads to real world impact. NP-hardness is an opportunity not an obstacle. ML is inherently linked to optimization not probability theory. Data represent an objective reality; models only exist in our imagination. Optimization has a significant edge over randomization . The ultimate objective in the real world is prescription, not prediction.

Artificial Intelligence & Data Science..2 in 1

Book

Brief

Discover why learning as much as you can about AI, data science, and machine learning is worth the time and effort with this easy-to-understand guide designed for beginners but is chockful of information even more experienced people can use. This two-book bundle offers a glimpse into these fascinating topics that will carry you through the 21st century and help you to analyze or scale your business or practices. Leave the guessing to someone else and finally get a full grasp of the information you need to move up in the business world, understand your business records and/or data better, and gain insight into the world of robotics. This two-book bundle has all the answers you need to get your foot in the door and to prepare you for what lies ahead in the world of technology.

Data Science and Machine Learning...Mathematical and Statistical Methods

Brief

Book

The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.  Key Features: Focuses on mathematical understanding.  Presentation is self-contained, accessible, and comprehensive.  Extensive list of exercises and worked-out examples.  Many concrete algorithms with Python code.  Full color throughout.

Generative Deep Learning...Teaching Machines to Paint

Book

Brief

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Advances in Financial Machine Learning...

Book

Brief

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to back test your discoveries while avoiding false positives. The book addresses real life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Analytics, Data Science, & Artificial Intelligence...

Brief

Book

The most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus – analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Big Data and Social Science... A Practical Guide to Methods and Tools

Book

Brief

Shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation.  The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations.

Advanced Data Science and Analytics with Python

Brief

Book

Enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python; SciKit-learn,  Pandas, Numpy,  Beautiful Soup, NLTK,  NetworkX and others.  The model development is supported by the use of frameworks.  Note, this book targets readers with a background in programming, who are interested in the tools used in data analytics and data science.