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Big Data LDN
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Metadata-Driven Design
Data Analytics
Business Agility
User Interface Design
Business Rules
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Teaches the application of metadata to all aspects of data analytics
Features an instructor with 25 years of experience in data-centric UK charities
Examines the potential of metadata-driven approaches to enhance business agility
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Full Stack Metadata Driven Thinking to Drive Business Agility.
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Career center
Learners who complete Full Stack Metadata Driven Thinking to Drive Business Agility will develop knowledge and skills
that may be useful to these careers:
Chief Data Officer
Chief Data Officer
In order to make effective use of metadata, an extensive understanding of both data and business needs is required. Practically every course in data science and analytics can benefit a Chief Data Officer, but many of these courses focus on one side or the other. This course offers an uncommon combination of data science and business administration that is highly valuable to a CDO.
Data Scientist
Data Scientist
A course in metadata-driven thinking can help a Data Scientist understand how data can be used not only for information and insight, but to drive change in the organization and how it operates. This course specifically looks at the use of metadata to create an agile business.
Business Analyst
Business Analyst
A business analyst is responsible for understanding how a business works and for identifying ways to improve its processes and performance. Though this course is focused on the use of metadata to achieve these ends, it can nonetheless help a Business Analyst learn about the fundamentals of data-driven decision-making.
Data Architect
Data Architect
A data architect may design and develop systems that can easily use metadata and metadata-driven techniques. This course will help Data Architects build a foundation in the use of metadata and will be particularly helpful for understanding how metadata can be used to support data-driven decision-making.
Software Engineer
Software Engineer
This course may be helpful to a Software Engineer in a data-focused role. Understanding how metadata can be used can help drive change in modern data-driven organizations.
Data Engineer
Data Engineer
Data engineers who work in an agile environment will find this course particularly useful. It discusses how to collect metadata from a wider range of sources and how to use metadata to support business agility.
Project Manager
Project Manager
Project managers in business intelligence will need to be able to work in a metadata-driven environment. This course will help Project Managers understand the fundamentals of working with metadata and the reasons why it is increasingly important in modern business.
Business Intelligence Analyst
Business Intelligence Analyst
Today's Business Intelligence Analysts must be able to take a data-driven approach to developing insights and creating data-driven solutions. This course helps practicing BIAs lay a strong foundation for working in a data-driven organization.
Database Administrator
Database Administrator
Database administrators must understand metadata in order to increase the usability and efficiency of database systems. This course can help DBAs understand and use metadata effectively.
Data Governance Professional
Data Governance Professional
Data governance professionals who work in an agile environment will find this course particularly useful. It discusses how to collect and use metadata to support business agility.
Product Manager
Product Manager
Product Managers in organizations that take an agile approach to digital products will find this course particularly useful. It provides an understanding of how metadata can be used to collect and respond to feedback from customers.
Business Process Analyst
Business Process Analyst
Business process analysts must understand how to use data to make improvements to business processes. This course can help Business Process Analysts understand how metadata can be used to support data-driven decision-making and business agility.
Quality Assurance Manager
Quality Assurance Manager
Quality Assurance Managers can benefit from this course by learning how metadata can be used to improve the quality and effectiveness of data-driven decision-making. This can be particularly valuable for organizations that are aiming for better business agility and responsiveness.
Data Visualization Specialist
Data Visualization Specialist
This course may be helpful to a Data Visualization Specialist who is working with metadata to design and create data visualizations that support business agility. The course can help Specialists understand the importance of metadata and how it can be used to create visualizations that are both accurate and informative.
Data Analyst
Data Analyst
This course may be helpful to a Data Analyst who is working with metadata to collect and analyze data for the purposes of business agility. The course can help Analysts understand the importance of metadata and how it can be used to support data-driven decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/yth3w4/full
Reading list
We haven't picked any books for this reading list yet.
Provides a practical guide to big data analytics. It covers the challenges of big data, as well as the techniques and tools that can be used to analyze big data. It valuable resource for anyone who wants to learn more about big data analytics.
Provides a comprehensive overview of data mining. It covers the basics of data mining, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data mining.
Provides a practical introduction to statistical methods for data analytics. It covers the basics of statistics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using statistics to analyze data.
Provides a guided tour of predictive analytics. It covers the basics of predictive analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using predictive analytics to make better decisions.
Provides a friendly introduction to data analytics for people who are new to the field. It covers the basics of data analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data analytics without getting bogged down in technical details.
Provides a comprehensive introduction to data analytics with Python. It covers the basics of Python, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use Python for data analytics.
Provides a broad, introductory overview of data analytics concepts, making it ideal for beginners across various disciplines. It covers key data concepts and includes real-world examples and case studies to solidify understanding. Many universities use this book as a textbook for introductory data analytics courses. It serves as excellent background reading for anyone new to the field.
Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.
Focusing on the crucial aspect of communicating insights, this book teaches the fundamentals of data visualization and how to tell compelling stories with data. It provides practical guidance and real-world examples to help readers create effective visualizations and presentations. is highly recommended for anyone who needs to present data-driven findings clearly and persuasively, regardless of their technical background.
Offers an accessible and engaging introduction to the fundamentals of statistics, a critical component of data analytics. It explains key statistical concepts using real-world examples and relatable anecdotes, making it an excellent resource for those without a strong mathematical background. It helps build a solid foundation in statistical thinking necessary for data analysis.
Written by the creator of the pandas library, this book practical, hands-on guide to data manipulation, cleaning, processing, and analysis using Python. It is an essential resource for anyone looking to use Python for data analytics, covering key libraries like pandas, NumPy, and Jupyter. It includes numerous real-world case studies and is widely used by students and professionals.
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Provides a comprehensive introduction to data science using the R programming language and the tidyverse package collection. It guides readers through the entire data analysis workflow, from importing and cleaning data to visualization and modeling. It's a widely recommended resource for those who prefer to use R for data analytics and is suitable for students and professionals.
Offers a less technical introduction to statistical learning compared to its counterpart, 'The Elements of Statistical Learning.' It covers essential concepts and methods for statistical modeling and prediction, with practical applications in R. It is widely used as a textbook in universities and is suitable for those with a background in statistics or quantitative fields looking to deepen their understanding of the statistical foundations of data analytics.
Considered a classic in the field, this book provides a comprehensive and rigorous treatment of statistical learning methods. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and a variety of algorithms. While mathematically more demanding, it is an invaluable reference for graduate students and researchers seeking a deep understanding of the theoretical underpinnings of many data analytics techniques.
This practical guide focuses on machine learning concepts and techniques using popular Python libraries. It provides a hands-on approach with code examples, making it excellent for those who want to implement machine learning models as part of their data analytics workflow. It is suitable for individuals with some programming experience and valuable resource for deepening technical skills.
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Offers a practical and engaging approach to data science and analytics, focusing on using readily available tools like Excel to perform powerful analysis. It's a great resource for business professionals who want to leverage data without necessarily diving deep into programming. It provides a solid understanding of analytical techniques through relatable examples.
This influential book explores how organizations can gain a competitive advantage by effectively using data and analytics for decision-making. It highlights the importance of building an analytical capability within a company and provides examples of successful analytical competitors. This must-read for business leaders and professionals interested in the strategic implications of data analytics.
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Makes a compelling case for the importance of big data in today's business landscape. It explores the opportunities and challenges presented by large datasets and how organizations can leverage them for insights and innovation. It's a valuable read for business professionals and leaders looking to understand the strategic value of big data analytics.
This comprehensive textbook covering the fundamental concepts and techniques of data mining. It delves into various data mining methodologies, algorithms, and applications. It widely used resource in academic settings for both undergraduate and graduate students seeking a detailed understanding of data mining as a core component of data analytics.
Provides a comprehensive introduction to data analytics with R. It covers the basics of R, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use R for data analytics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yth3w4/full
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