Highlighted Resources
- O'Reilly for Higher Education This link opens in a new windowIncludes books, interactive tutorials, and videos in computer science, information systems, user experience, business, communication, professional development, and management topics.
Data Analytics Made Easy by
No code. No complex math. Just fun with data, with results that can transform your business! Key Features Learn the art of telling stories with your data Get ahead in your career by thinking like a data scientist Build models to improve profitability, create customer segmentation, automate and improve data reporting, and more Book Description Data Analytics Made Easy is an accessible guide for anyone new to working with data. It focuses on how to generate insights from your data at a click of a button using popular tools, without having to write a line of code! The book helps you start analyzing data and quickly apply these skills to your work. Data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you. The book introduces the concepts of data analytics and shows you how to get your data ready and apply algorithms. Implement analytics solutions to predict future trends and assess their levels of accuracy. Create impressive visualizations and learn the greatest secret in successful analytics - how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process. By the end of this book, you will have learned how to get your data talking and sell the results to your customers without writing a line of code. What you will learn Understand the potential of data and its impact on any business Influence business decisions with effective data storytelling when delivering insights Import, clean, transform, combine data feeds, and automate your processes Learn the basics of machine learning to add value to your organization Create professional-looking and business-centric visuals and dashboards Who this book is for If you are new to working with data and you want to progress in your career, you'll find this book an effective way to add analytics to your skill stack. No previous math, statistics, or computer science knowledge is required.
ISBN: 9781801074155Publication Date: 2021-08-30
Designing Data-Intensive Applications by
Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures
ISBN: 9781449373320Publication Date: 2017-04-18
Intro to Pyton by
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.
ISBN: 9780135404799Publication Date: 2019Learning Data Science by As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.
ISBN: 9781098112998Publication Date: 2023
System Lifecycle Management by
Years of experience in the area of Product Lifecycle Management (PLM) in industry, research and education form the basis for this overview. The author covers the development from PDM via PLM to SysLM (System Lifecycle Management) in the form commonly used today, which are necessary prerequisites for the sustainable development and implementation of IoT/IoS, Industry 4.0 and Engineering 4.0 concepts. The building blocks and properties of future-proof systems for the successful implementation of the concepts of Engineering 4.0 are thereby dedicated to holistic considerations, which also inform in detail. SysLM functions and processes in mechatronic development and design as well as across the entire product lifecycle - from requirements management to the Digital Twin - are covered as examples. SysLM trends such as low code development, cloud, disruptive business models, and bimodality provide an outlook on future developments. The author dedicates the treatment of the agile SysLM introduction to the implementation in the enterprise. The basics are deepened with examples of a concrete SysLM system.
ISBN: 9783658338732Publication Date: 2021-08-11
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