Cambridge Advanced National (AAQ) in IT: Data Analytics Digital Student Book Mandatory Units (1 Year Site Licence) (via email)
This digital student book offers comprehensive coverage of the mandatory units for the Cambridge Advanced National in IT: Data Analytics (Certificate and Extended Certificate). Matched to OCR's specification, the book is designed to support students' learning of key concepts and information, while providing teachers with guidance on the depth of content they're required to teach. With features such as practice questions and contextual examples highlighting how concepts can be applied in the real-world, the book will engage students and help them build the skills and confidence needed to succeed in the qualification and beyond. Working towards endorsement by OCR.
This digital student book offers comprehensive coverage of the mandatory units for the Cambridge Advanced National in IT: Data Analytics (Certificate and Extended Certificate). Mapped to the OCR specification, the book has been designed to help students and teachers understand and succeed in the new qualification. It’s an ideal learning and reference tool, structured into clear units that follow the order of the specification and unpack its content in an easy-to-follow, digestible way. It will help students learn key concepts and information, while also providing teachers with guidance on the depth of content they're required to teach. Written by experienced teachers and practitioners, the book will help students understand how the concepts they are learning relate to the real world through the inclusion of modern and engaging “Learning in Context” examples. It will also support independent learning and help students prepare for their assessments. Worked examples guide the development of essential skills, step-by-step. Frequent quick-check and practice questions help students assess their progress and understanding of each topic. The flexible digital format means content can be accessed anywhere, at any time. We are working towards endorsement by OCR.
Features
- The book provides comprehensive coverage of the OCR specification and unpacks it in an easy-to-follow, digestible way. This supports students’ learning of key concepts and essential information, while providing teachers with comprehensive guidance on the depth of content they need to teach. We are working towards endorsement by OCR.
- Written for students to motivate them to learn. Uses accessible and appropriate language, short and focused paragraphs, as well as modern and useful contextual examples.
- Engaging “Learning in context” features highlight how concepts can be applied within the industry or role, helping students to make connections between what they are studying and the real world.
- Written by expert authors, who are experienced teachers and practitioners.
- Worked examples guide students' development of essential skills, step-by-step.
- Frequent quick check questions empower students to assess their progress and understanding of each topic. Answers to these can be found within our online platform, Cambridge Go.
- Bolded key words and a glossary help students to grasp and master new vocabulary.
- Great for use in class, as well as for setting homework tasks or independent learning.
- Designed to better prepare students for and build confidence ahead of assessments. Practice questions are found at the end of each examined topic, with answers available within our online platform, Cambridge Go.
- Digital format means content can be accessed anytime, anywhere – on a phone, tablet or computer.
Table of Contents
- Acknowledgements
- Introduction
- About the authors
- How to use this book
- Unit F200: Fundamentals of data analytics
- TA1: Understanding data
- TA2: Managing data
- TA3: How data can be accessed and managed across platforms
- TA4: Legal considerations
- TA5: Job roles, skills and attributes in data analytics
- Unit F201: Big data and machine learning
- TA1: The scope of managing big data
- TA2: The infrastructure challenges of big data
- TA3: Big data, machine learning and artificial intelligence
- TA4: Legal and ethical issues in data management
- TA5: Environment and society
- Unit F202: Spreadsheet data modelling
- TA1: Principles of spreadsheet modelling
- TA2: Planning the design of a spreadsheet model
- TA3: Creating the spreadsheet model
- TA4: Delivering the outcomes
- TA5: Evaluation
- Glossary
- Index
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