Data Science

Create Teams That Ask the Right Questions and Deliver Real Value

Author: Doug Rose

Publisher: Apress

ISBN:

Category: Business & Economics

Page: 236

View: 402

Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: • Shows project managers and team leaders how to create data science teams from existing talent in their organizations to cost-efficiently extract maximum business value from their organizations’ data• Introduces managers to key data science terms and concepts •Gives practical guidance on how to create and integrate an effective data science team with key roles and the responsibilities for each team member • Presents the data science life cycle (DSLC) to model essential processes and practices for delivering value• Details the use of sprints and storytelling to help the team stay on track and adapt to new knowledge Who This Book Is ForThe primary readership for this book is data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.

Data Science

Create Teams That Ask the Right Questions and Deliver Real Value

Author: Doug Rose

Publisher: Apress

ISBN:

Category: Computers

Page: 251

View: 567

Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s data Understand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.

Enterprise Agility For Dummies

Author: Doug Rose

Publisher: John Wiley & Sons

ISBN:

Category: Business & Economics

Page: 384

View: 860

Manage and improve your organization's agile transformation Adopting an enterprise agile framework is a radical organizational change, and this book will help you get there without ever breaking a sweat. In Enterprise Agile For Dummies, you'll discover how to successfully choose and implement the right framework based on your organization's own unique culture. Organizational culture is one of the most overlooked challenges when trying to make a change to enterprise agile, and there are lots of resources out there that claim to have the perfect, one-size-fits-all solution. Luckily, this book takes a neutral stance and covers popular organizational change management techniques that you can implement to suit to your unique needs. Packed with step-by-step instruction and complemented with real-world case studies, this book offers everything you need to know in order to embrace a more agile mindset. Understand the benefits of an agile approach Pick the best enterprise agile framework for your organization Create a successful enterprise change management plan Let Enterprise Agile For Dummies help you optimize your business processes, and watch your productivity soar.

Reinventing the Social Scientist and Humanist in the Era of Big Data

A Perspective from South African Scholars

Author: Susan Brokensha

Publisher: UJ Press

ISBN:

Category: Social Science

Page: 205

View: 686

This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.

Adoption of Data Analytics in Higher Education Learning and Teaching

Author: Dirk Ifenthaler

Publisher: Springer Nature

ISBN:

Category: Education

Page: 434

View: 332

The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.

Artificial Intelligence for Business

Author: Doug Rose

Publisher: Financial Times/Prentice Hall

ISBN:

Category: Business & Economics

Page: 220

View: 99

Millions of non-technical professionals and leaders want to understand Artificial Intelligence (AI) and Machine Learning (ML) -- whether to improve their businesses, be more effective citizens, consumers or policymakers, or just out of sheer curiosity. Until now, most books on the subject have either been too complicated and mathematical, or have simply avoided the big picture by focusing on the use of specific software libraries. In Artificial Intelligence for Business , Doug Rose bridges the gap, offering today's most accessible and useful introduction to AI and ML technologies -- and what they can and can't do. Rose begins by tracing AI's evolution from the early 1950s to the present, illuminating core ideas that still drive its development. Next, he explores recent innovations that have reinvigorated the field by providing the "big data" that makes machine learning so powerful - innovations such as GPS, social media and electronic transactions. Finally, he explains how today's machines learn by combining powerful processing, advanced algorithms, and artificial neural networks that mimic the human brain. Throughout, he illustrates key concepts with practical examples that help you connect AI, ML, and neural networks to specific problems and solutions. Step by step, he systematically demystifies these powerful technologies, removing the fear, bewilderment, and advanced math -- so you can understand the new possibilities they create, and start using them.

Managing Data Science

Effective strategies to manage data science projects and build a sustainable team

Author: Kirill Dubovikov

Publisher: Packt Publishing Ltd

ISBN:

Category: Computers

Page: 290

View: 943

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Data Science Strategy For Dummies

Author: Ulrika Jägare

Publisher: John Wiley & Sons

ISBN:

Category: Computers

Page: 320

View: 489

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

Agile Data Science 2.0

Building Full-Stack Data Analytics Applications with Spark

Author: Russell Jurney

Publisher: "O'Reilly Media, Inc."

ISBN:

Category: Computers

Page: 352

View: 797

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Strategies for Team Science Success

Handbook of Evidence-Based Principles for Cross-Disciplinary Science and Practical Lessons Learned from Health Researchers

Author: Kara L. Hall

Publisher: Springer Nature

ISBN:

Category: Psychology

Page: 633

View: 670

Collaborations that integrate diverse perspectives are critical to addressing many of our complex scientific and societal problems. Yet those engaged in cross-disciplinary team science often face institutional barriers and collaborative challenges. Strategies for Team Science Success offers readers a comprehensive set of actionable strategies for reducing barriers and overcoming challenges and includes practical guidance for how to implement effective team science practices. More than 100 experts--including scientists, administrators, and funders from a wide range of disciplines and professions-- explain evidence-based principles, highlight state-of the-art strategies, tools, and resources, and share first-person accounts of how they’ve applied them in their own successful team science initiatives. While many examples draw from cross-disciplinary team science initiatives in the health domain, the handbook is designed to be useful across all areas of science. Strategies for Team Science Success will inspire and enable readers to embrace cross-disciplinary team science, by articulating its value for accelerating scientific progress, and by providing practical strategies for success. Scientists, administrators, funders, and others engaged in team science will also leave equipped to develop new policies and practices needed to keep pace in our rapidly changing scientific landscape. Scholars across the Science of Team Science (SciTS), management, organizational, behavioral and social sciences, public health, philosophy, and information technology, among other areas of scholarship, will find inspiration for new research directions to continue advancing cross-disciplinary team science.