Data Analytics VCE Units 3&4 PDF 5th Edition

Data Analytics VCE Units 3&4 © 2019 Cengage Learning Australia Pty Limited 5th Edition Gary Bass Copyright Notice Natalie Heath...

Data Analytics VCE Units 3&4 © 2019 Cengage Learning Australia Pty Limited 5th Edition Gary Bass Copyright Notice Natalie Heath This Work is copyright. No part of this Work may be reproduced, stored in a Therese Keane retrieval system, or transmitted in any form or by any means without prior Anthony Sullivan written permission of the Publisher. Except as permitted under the Mark Kelly Copyright Act 1968, for example any fair dealing for the purposes of private study, research, criticism or review, subject to certain limitations. These limitations include: Restricting the copying to a maximum of one chapter or 10% of this Senior publisher: Eleanor Gregory book, whichever is greater; providing an appropriate notice and warning with the Editor: Scott Vandervalk copies of the Work disseminated; taking all reasonable steps to limit access to Proofreader: Nadine Anderson these copies to people authorised to receive these copies; ensuring you hold the Indexer: Bruce Gillespie appropriate Licences issued by the Copyright Agency Limited (“CAL”), supply a Visual designer: James Steer remuneration notice to CAL and pay any required fees. For details of CAL licences Cover design: Chris Starr, MakeWork and remuneration notices please contact CAL at Level 11, 66 Goulburn Street, Text design: Leigh Ashforth, Watershed Art & Design Sydney NSW 2000, Tel: (02) 9394 7600, Fax: (02) 9394 7601 Permissions researcher: Lyahna Spencer Email: [email protected] Production controller: Karen Young Website: www.copyright.com.au Typeset by: DiacriTech For product information and technology assistance, Any URLs contained in this publication were checked for currency during the in Australia call 1300 790 853; production process. Note, however, that the publisher cannot vouch for the in New Zealand call 0800 449 725 ongoing currency of URLs. For permission to use material from this text or product, please email [email protected] Acknowledgements Extracts from the VCE Applied Computing Study Design (2020–2023), are ISBN 978 0 17 044087 5 reproduced by permission, © VCAA. VCE is a registered trademark of the VCAA. The VCAA does not endorse or make any warranties regarding this study Cengage Learning Australia resource. Current VCE Study Designs, past VCE exams and related content can be Level 7, 80 Dorcas Street accessed directly at www.vcaa.vic.edu.au South Melbourne, Victoria Australia 3205 Cengage Learning New Zealand Unit 4B Rosedale Office Park 331 Rosedale Road, Albany, North Shore 0632, NZ For learning solutions, visit cengage.com.au Printed in Singapore by 1010 Printing International Limited. 1 2 3 4 5 6 7 23 22 21 20 19  Contents Preface v About the authors vi How to use this book vii Outcomes ix Problem-solving methodology xiii Key concepts xvi 3 Unit Introduction 1 Chapter 3 Project management and data analysis 113 Chapter 1 Data and presentation 2 What is data? 114 Project management 114 What is data? 3 Why must we begin with a research question? 124 Referencing data sources 14 Primary and secondary data 128 Data types 16 Quantitative and qualitative data 129 Data structures 19 Data types and data structures 136 Data integrity 20 Referencing data sources 137 Data validation 24 Data integrity 139 Data visualisation 25 Data security 152 Designing solutions 37 Next steps 158 Design principles 37 Formats and conventions 39 Design tools for data visualisation 46 Chapter 4 Drawing conclusions 165 Continuing Unit 3, Outcome 2 166 Chapter 2 Data manipulation and Solution specifications 166 presentation 57 Design principles 171 Databases 58 Generating design ideas 176 Creating a database structure 59 Design tools 187 Spreadsheet tools 84 Types of infographics and data visualisations 190 Data visualisation 89 Preparing for Unit 3, Outcome 2 204 Testing 96 Review the Outcome’s steps 106 Preparing for Unit 3, Outcome 1 112 iii 9780170440875  4 Unit Introduction 207 Chapter 6 Information management 262 Networks 263 Chapter 5 Development and evaluation 208 Threats to data and information 272 Data visualisations 209 Physical and software security controls 275 Procedures and techniques for managing files 214 Managing data on a network 284 Functional capabilities of software 220 Network attached storage and cloud computing 289 An effective infographic or data visualisation 221 Manipulating data 234 Chapter 7 Cyber security measures 297 Formats and conventions 237 Manipulating data with software 238 The importance of data and information to organisations 298 Verification and validation 243 Information management strategies 301 Testing 244 Data security 302 Evaluating your solution 248 The importance of diminished data integrity in information systems 303 Documenting the progress of projects 250 Key legislation relating to data and information 304 Assessing your project plan 254 Ethics and security practices 314 Next steps 255 Resolving legal and ethical tensions 317 Preparing for Unit 4, Outcome 1 261 Reasons to prepare for disaster 318 Consequences of security failure 328 Evaluating information management strategies 331 Preparing for Unit 4, Outcome 2 338 Index 339 iv CONTENTS 9780170440875  Preface This fifth edition of Data Analytics VCE Units 3 & 4 incorporates the changes to the VCAA VCE Applied Computing Study Design that took effect from 2020. This textbook looks at how individuals and organisations use, and can be affected by, information systems in their daily lives. We believe that teachers and students require a text that focuses on the Areas of Study specified in the Study Design and which presents information in a sequence that allows easy transition from theory into practical assessment tasks. We have, therefore, written this textbook so that a class can begin at Chapter 1 and work their way systematically through to the end. Students will encounter material relating to the key knowledge dot points for each Outcome before they reach the special section that describes the Outcome. The Study Design outlines key skills that indicate how the knowledge can be applied to produce a solution to an information problem. These Outcome preparation sections occur regularly throughout the textbook and flag an appropriate point in the student’s development for each Outcome to be completed. The authors have covered all key knowledge for the Outcomes from the Data Analytics VCE Units 3 & 4 course. Our approach has been to focus on the key knowledge required for each school-assessed Outcome, and to ensure that students are well prepared for these; however, there is considerable duplication in the Study Design relating to the knowledge required for many of the Outcomes. We have found that, with an Outcomes approach, we are covering material sometimes several times. For example, knowledge of a problem-solving methodology is listed as key knowledge for many different Outcomes. In these cases, we have tried to provide a general coverage in the first instance, and specifically apply the concept to a situation relevant to the related Outcome on subsequent encounters. The authors assume that teachers will develop the required key skills with their students within the context of the key knowledge addressed in this textbook and the resources available to them. We have incorporated a margin column in the text to provide additional information and reinforce key concepts. This margin column also includes activities that relate to the topics covered in the text and considers issues relevant to information systems usage. Outcome features appear at several points in the book, indicating the nature of the tasks that students undertake in the completion of the school-assessed Outcome. We have listed the steps required to complete the Outcome, together with advice and suggestions for approaching the task. We have also described the output and support material needed for submission. You will also find sample tasks and further advice relating to the Outcomes are available at https://nelsonnet.com.au. The chapters are organised to present the optimum amount of information in the most effective manner. The book is presented in concise, clearly identified sections to guide students through the text. Each chapter is organised into the sections described on pages vii–viii. v 9780170440875  About the authors Gary Bass teaches VCE Applied Computing at Year 11 and Year 12 in an online course environment at Virtual School Victoria. Previously, he has taught VCE Physics, as well as developing and delivering middle school ICT courses. Gary has presented at DLTV DigiCON and the annual IT teachers conference on many topics including Pop-up Makerspace; Big Data requires huge analysis – data visualisation; AR + VR = Mixed reality; and Marshall McLuhan-Medium is the message. Gary was selected as an Apple Distinguished Educator (ADE) in 2002 and 2011. In 2016, he was presented with DLTV’s IT Leader of the Year award. Natalie Heath is the eLearning/ICT Leading Teacher at Eltham High School. She has been an IT specialist teacher for nearly 20 years, teaching at all secondary levels including VCE Informatics, IT Applications, Information Processing and Management, and Software Development. She has extensive experience around VCE, having assessed examinations in various subjects for more than two decades. Natalie has also developed many resources for VCE Computing subjects over the years, including trial examinations. She has presented at teacher professional learning conferences as an expert in Unit 3 and 4 subjects and in 2018 was presented with the DLTV’s Maggie Iaquinto VCE Computing Educator of the Year award for her services to the VCE Computing teaching community and resource development. Associate Professor Therese Keane is Deputy Chair of the Department of Education at Swinburne University and has worked in a variety of school settings where she has taught IT in K–12 education as the Director of ICT. Her passion and achievements in ICT in the education and robotics space have been acknowledged by her peers in her receiving numerous national and state awards. Therese has presented seminars and workshops for teachers involved in the teaching of IT. She has written several textbooks in all units of VCE Information Technology. Therese’s research interests include the use of technology in education, gender inequalities in STEM-based subjects, robotics in education and computers in schools for teaching and learning purposes. Therese is involved with the FIRST LEGO League as the Championship Tournament Director for Victoria and she is a lead mentor for the RoboCats – a female school student only robotics team that participates in the FIRST Robotic Competition. Anthony Sullivan is a Curriculum and Learning Specialist at Monash College where he is responsible for creating assessment and learning materials for accounting and computing subjects as part of the Monash University Foundation Year program. Anthony has more than 25 years experience teaching business and computing subjects. He has taught in both government and non-government settings in Australia and taught computing and information technology courses in schools in Asia and the United Kingdom. Anthony has also been a VCE Examination Assessor, a member of the committee that reviewed and wrote the previous Study Design for VCE Computing, and has written a range of commercial resources related to VCE Computing. He has presented at conferences and professional development events and student examination preparation sessions. vi 9780170440875  How to use this book KEY KNOWLEDGE The key knowledge from the VCE Applied Computing Study Design that you will cover in each chapter is listed on the first page of each chapter. The list includes key knowledge specified in the Outcome related to the chapter. FOR THE STUDENT Each chapter’s opening page includes an overview of that chapter’s contents so that you are aware of the material you will encounter. FOR THE TEACHER This section is for your teacher and outlines how the chapter fits into the overall study of Data Analytics, and outlines how the material relates to the completion of Outcomes. CHAPTERS The major learning material that you will encounter in the chapter is presented as text, photographs and illustrations. The text describes in detail the theory associated with the stated Outcomes of the VCE Applied Computing Study Design in easy-to-understand language. The photographs show hardware, software and other objects that have been described in the text. Illustrations are used to demonstrate concepts that are more easily explained in this manner. Throughout the chapter, glossary terms are highlighted in bold and you can find their definitions at the end of each chapter, in Essential terms. The School-assessed Task Tracker at the bottom of every odd-numbered page provides you with a visual reminder to help you track your progress in the School-assessed Task (SAT), which is derived from Unit 3, Outcome 2 and Unit 4, Outcome 1, so that you can complete 3.1 all required stages on time. THINK ABOUT DATA ANALYTICS MARGIN COLUMN Project management tools are useful to find The margin column contains further explanations that support the main text, weblink icons, the perfect number additional material outside the Study Design and cross-references to material covered of people needed on a task so it is finished elsewhere in the textbook. Issues relevant to Data Analytics that you can discuss with your as quickly as possible classmates are also included in the form of ‘Think about Data Analytics’ boxes. without anyone being idle. Use software to CHAPTER SUMMARY develop a Gantt chart to plan the baking of a The chapter summary at the end of each chapter is divided into two main parts to help you cake. Assume you can use as many cooks as review each chapter. you want. Essential terms are the glossary terms that have been highlighted throughout the chapter. The Important facts section is a list of summaries, ideas, processes and statements relevant to the chapter, in the order in which they occur in the chapter. vii 9780170440875  TEST YOUR KNOWLEDGE Short-answer questions will help you to review the chapter material. The questions are grouped and identified with a section of the text to allow your teacher to direct appropriate questions based on the material covered in class. Teachers will be able to access answers to these questions at https://nelsonnet.com.au. APPLY YOUR KNOWLEDGE Each chapter concludes with a set of questions requiring you to demonstrate that you can apply the theory from the chapter to more complex questions. The style of questions reflects what you can expect in the end-of-year examination. Teachers will be able to access suggested responses to these questions at https://nelsonnet.com.au. PREPARING FOR THE OUTCOMES This section appears at points in the course where it is appropriate for you to complete an Outcome task. The information provided describes what you need to do in the Outcome, the suggested steps to be followed in the completion of the task and the material that needs to be submitted for assessment. NELSONNET The NelsonNet student website contains: multiple-choice quizzes for each chapter, mirroring the VCAA Unit 3 & 4 exam additional material such as spreadsheets and infographics. An open-access weblink page is also provided for all weblinks that appear in the margins throughout the textbook. This is accessible at https://nelsonnet.com.au. The NelsonNet teacher website is accessible only to teachers and it contains: answers for the Test your knowledge and Apply your knowledge questions in the book sample School-assessed Coursework (SAC) chapter tests practice exam. Please note that complimentary access to NelsonNet and the NelsonNetBook is only available to teachers who use the accompanying student textbook as a core educational resource in their classroom. Contact your sales representative for information about access codes and conditions. viii HOW TO USE THIS BOOK 9780170440875  Outcomes OUTCOME KEY KNOWLEDGE LOCATION Unit 3 Data analytics Area of Study 1 On completion of this unit the student should be able to respond to teacher-provided solution Outcome 1 requirements and designs to extract data from large repositories, manipulate and cleanse data and apply a range of functions to develop software solutions to present findings. Data and techniques for efficient and effective data collection, including methods to collect census, pp. 6–10 information Geographic Information System (GIS) data, sensor, social media and weather factors influencing the integrity of data, including accuracy, authenticity, correctness, pp. 20–23 reasonableness, relevance and timeliness sources of, and methods and techniques for, acquiring authentic data stored in large pp. 10–13 repositories methods for referencing primary and secondary sources, including American Psychological pp. 14–16 Association (APA) referencing system characteristics of data types pp. 16–18 Approaches to methods for documenting a problem, need or opportunity p. 37 problem solving methods for determining solution requirements, constraints and scope p. 37 naming conventions to support efficient use of databases, spreadsheets and data visualisations p. 67 a methodology for creating a database structure: identifying entities, defining tables and fields to represent entities; defining relationships by identifying primary key fields and foreign key pp. 58–66 fields; defining data types and field sizes, normalisation to third normal form design tools for representing databases, spreadsheets and data visualisations, including data pp. 46–9, 84 dictionaries, tables, charts, input forms, queries and reports design principles that influence the functionality and appearance of databases, spreadsheets pp. 37–9, 84–93 and data visualisations functions and techniques to retrieve required information through querying data sets, pp. 74–5 including searching, sorting and filtering to identify relationships and patterns software functions, techniques and procedures to efficiently and effectively validate, pp. 80–2, 88 manipulate and cleanse data including files, and applying formats and conventions types and purposes of data visualisations pp. 25–31 formats and conventions applied to data visualisations to improve their effectiveness for pp. 39–45 intended users, including clarity of message methods and techniques for testing databases, spreadsheets and data visualisations pp. 96–106 Interactions and reasons why organisations acquire data p. 4 impact Key skills interpret solution requirements and designs to develop data visualisations pp. 37, 167–71 identify, select and extract relevant data from large repositories pp. 4, 9–13 use a standard referencing system to acknowledge intellectual property p. 14 organise, manipulate and cleanse data using database and spreadsheet software pp. 74–5, 80–8 select, justify and apply functions, formats and conventions to create effective data visualisations pp. 39–45 develop and apply suitable validation and testing techniques to software tools used pp. 24–5 ix 9780170440875 OUTCOME KEY KNOWLEDGE LOCATION Unit 3 Data analytics: Analysis and design Area of Study 2 On completion of this unit the student should be able to propose a research question, formulate Outcome 2 a project plan, collect and analyse data, generate alternative design ideas and represent the preferred design for creating infographics or dynamic data visualisations. Digital systems roles, functions and characteristics of digital system components p. 148–52 physical and software security controls used by organisations for protecting stored and p. 152–8 communicated data Data and primary and secondary data sources and methods of collecting data, including interviews, p. 128 information observation, querying of data stored in large repositories and surveys techniques for searching, browsing and downloading data sets p. 126 suitability of quantitative and qualitative data for manipulation p. 129–30 characteristics of data types and data structures relevant to selected software tools p. 136–7 methods for referencing secondary sources, including the APA referencing system p. 137–9 criteria to check the integrity of data, including accuracy, authenticity, correctness, p. 139–47 reasonableness, relevance and timeliness techniques for coding qualitative data to support manipulation p. 130–6 Approaches to features of a research question, including a statement identifying the research question as an p. 124–6 problem solving information problem functional and non-functional requirements, including data to support the research question, p. 167–71 constraints and scope types and purposes of infographics and dynamic data visualisations p. 190–8 design principles that influence the appearance of infographics and the functionality and p. 171–6 appearance of dynamic data visualisations design tools for representing the appearance and functionality of infographics and dynamic p. 187–9 data visualisations, including data manipulation and validation, where appropriate techniques for generating alternative design ideas p. 176–85 criteria for evaluating alternative design ideas and the efficiency and effectiveness of p. 185–7 infographics or dynamic data visualisations features of project management using Gantt charts, including the identification and p. 114–23 sequencing of tasks, time allocation, dependencies, milestones and the critical path Interactions and key legal requirements for the storage and communication of data and information, including p. 307–14 impact human rights requirements, intellectual property and privacy Key skills frame a research question p. 124–6 analyse and document requirements, constraints and scope of infographics or dynamic data pp. 167–171 visualisations apply techniques for searching, downloading, browsing and referencing data sets p. 126 select and apply design tools to represent the functionality and appearance of infographics or p. 187–9 dynamic data visualisations generate alternative design ideas p. 176–85 x OUTCOMES 9780170440875  OUTCOME KEY KNOWLEDGE LOCATION develop evaluation criteria to select and justify preferred designs p. 185–7 produce detailed designs using appropriate design methods and techniques p. 187–98 propose and apply appropriate methods to secure stored data p. 152–8 create, monitor and modify project plans using software p. 114–23 Unit 4 Data analytics: Development and evaluation Area of Study 1 On completion of this unit the student should be able to develop and evaluate infographics or Outcome 1 dynamic data visualisations that present findings in response to a research question, and assess the effectiveness of the project plan in monitoring progress. Digital systems procedures and techniques for handling and managing files, including archiving, backing up, pp. 214–20 disposing of files and security the functional capabilities of software to create infographics and dynamic data visualisations p. 220 Approaches to characteristics of information for educating targeted audiences, including age appropriateness, p. 221–9 problem solving commonality of language, culture inclusiveness and gender characteristics of efficient and effective infographics and dynamic data visualisations p. 221–34 functions, techniques and procedures for efficiently and effectively manipulating data using pp. 234, 238 software tools techniques for creating infographics and dynamic data visualisations pp. 238–42 techniques for validating and verifying data p. 243 techniques for testing that solutions perform as intended pp. 244–8 techniques for recording the progress of projects, including adjustments to tasks and pp. 250–3 timeframes, annotations and logs strategies for evaluating the effectiveness of infographics and dynamic data visualisations pp. 248–50 solutions and assessing project plans Key skills monitor, modify and annotate the project plan as necessary p. 251 propose and implement procedures for managing files pp. 214–20 select and apply software functions, conventions, formats, methods and techniques to develop pp. 237–8 infographics or dynamic data visualisations select and apply data validation and testing techniques, making any necessary modifications pp. 243–8 apply evaluation criteria to evaluate the efficiency and effectiveness of infographics or dynamic pp. 248–50 data visualisations solutions assess the effectiveness of the project plan in managing the project pp. 254–5 OUTCOMES xi 9780170440875  OUTCOME KEY KNOWLEDGE LOCATION Unit 4 Cybersecurity: Data and information security Area of Study 2 On completion of this unit the student should be able to respond to a teacher-provided case Outcome 2 study to investigate the current data and information security strategies of an organisation, examine the threats to the security of data and information, and recommend strategies to improve current practices. Digital systems characteristics of wired, wireless and mobile networks pp. 263–71 types and causes of accidental, deliberate and events-based threats to the integrity and pp. 272–5 security of data and information used by organisations physical and software security controls for preventing unauthorised access to data and pp. 275–84 information and for minimising the loss of data accessed by authorised and unauthorised users the role of hardware, software and technical protocols in managing, controlling and securing pp. 284–9 data in information systems the advantages and disadvantages of using network attached storage and cloud computing for pp. 289–90 storing, communicating and disposing of data and information Data and characteristics of data that has integrity, including accuracy, authenticity, correctness, pp. 20–3 information reasonableness, relevance and timeliness Interactions and the importance of data and information to organisations impacts p. 298 the importance of data and information security strategies to organisations p. 302 the impact of diminished data integrity in information systems pp. 303–4 key legislation that affects how organisations control the collection, storage, communication and disposal of their data and information: the Health Records Act 2001, the Privacy Act 1988 pp. 304–13 and the Privacy and Data Protection Act 2014 ethical issues arising from data and information security practices pp. 314–17 strategies for resolving legal and ethical issues between stakeholders arising from information pp. 317–18 security practices reasons to prepare for disaster and the scope of disaster recovery plans, including backing up, pp. 318–27 evacuation, restoration and test plans possible consequences for organisations that fail or violate security measures p. 328 criteria for evaluating the effectiveness of data and information security strategies pp. 331–3 Key skills analyse and discuss the current data and information security strategies used by an pp. 298–302 organisation propose and apply criteria to evaluate the effectiveness of current data and information p. 302 security strategies identify and evaluate threats to the security of data and information pp. 272–5, 302 identify and discuss possible legal and ethical consequences of ineffective data and information p. 328 security strategies recommend and justify strategies to improve current data and information security practices p. 328 Reproduced from the VCE Applied Computing Study Design (2020–2023) © VCAA; used with permission. xii OUTCOMES 9780170440875  Problem-solving methodology When an information problem exists, a structured problem-solving methodology is followed to ensure that the most appropriate solution is found and implemented. For the purpose of this course, the problem-solving methodology has four key stages: analysis, design, development and evaluation. Each of these stages can be further broken down into a common set of activities. Each unit may require you to examine a different set of problem-solving stages. It is critical for you to understand the problem-solving methodology because it underpins the entire VCE Applied Computing course. Problem-solving methodology.noissimrep htiw desu ;AACV © )3202–0202( ngiseD ydutS gnitupmoC deilppA ECV eht morf decudorpeR Analysis Design Development Evaluation Activities Activities Activities Activities Solution Solution Solution Manipulation requirements design evaluation Solution Evaluation Evaluation Validation constraints criteria strategy Solution Testing scope FIGURE 1 The four stages of the problem- Documentation solving methodology and their key activities Analyse the problem The purpose of analysis is to establish the root cause of the problem, the specific information needs of the organisation involved, limitations on the problem and exactly what a possible solution would be expected to do (the scope). The three key activities are: 1 identifying solution requirements – attributes and functionality that the solution needs to include, information it must produce and data needed to produce this information 2 establishing solution constraints – the limitations on solution development that need to be considered; constraints are classified as economic, technical, social, legal and related to usability 3 defining the scope of the solution – what the solution will and will not be able to do. xiii 9780170440875  Design the solution During the design stage, several alternative design ideas based on both appearance and function are planned and the most appropriate of these is chosen. Criteria are also created to select the most appropriate ideas and to evaluate the solution’s success once it has been implemented. The two key design activities include the following. 1 Creating the solution design – it must clearly show a developer what the solution should look like, the specific data required, and how its data elements should be structured, validated and manipulated. Tools typically used to represent data elements could include data dictionaries, data structure diagrams, input–process–output (IPO) charts, flowcharts, pseudocode and object descriptions. The following tools are also used to show the relationship between various components of the solution: storyboards, site maps, data flow diagrams, structure charts, hierarchy charts and context diagrams. Furthermore, the appearance of the solution, including elements like a user interface, reports, graphic representations or data visualisations, needs to be planned so that overall layout, fonts and their colours can be represented. Layout diagrams and annotated diagrams (or mock-ups) usually fulfil this requirement. A combination of tools from each of these categories will be selected to represent the overall solution design. Regardless of the visual or functional aspects of a solution design at this stage, a design for the tests to ultimately ensure the solution is functioning correctly must also be created. 2 Specifying evaluation criteria – during the evaluation stage, the solution is assessed to establish how well it has met its intended objectives. The criteria for evaluation must be created during the design stage so that all personnel involved in the task are aware of the level of performance that will ultimately determine the success or otherwise of the solution. The criteria are based on the solution requirements identified at the analysis stage and are measured in terms of efficiency and effectiveness. Develop the solution The solution is created by the developers during this stage from the designs supplied to them. The ‘coding’ takes place, but also checking of input data (validation), testing that the solution works and the creation of user documentation. The four activities involved with development include the following. 1 Manipulating or coding the solution – the designs are used to build the electronic solution. The coding will occur here and internal documentation will be included where necessary. 2 Checking the accuracy of input data by way of validation – manual and electronic methods are used; for example, proofreading is a manual validation technique. Electronic validation involves using the solution itself to ensure that data is reasonable by checking for existence, data type and that it fits within the required range. Electronic validation, along with any other formulas, always needs to be tested to ensure that the solution works properly. xiv PROBLEM-SOLVING METHODOLOGY 9780170440875 3 Ensuring that a solution works through testing – each formula and function, not to mention validation and even the layout of elements on the screen, needs to be tested. Standard testing procedures involve stating what tests will be conducted, identifying test data, stating the expected result, running the tests, reporting the actual result and correcting any errors. 4 Documentation allowing users to interact with (or use) the solution – while it can be printed, in many cases it is now designed to be viewed on screen. User documentation normally outlines procedures for operating the solution, as well as generating output (like reports) and basic troubleshooting. Evaluate the solution Sometimes after a solution has been in use by the end-user or client, it needs to be assessed or evaluated to ensure that it has been successful and does actually meet the user’s requirements. The two activities involved in evaluating a solution include the following. 1 Evaluating the solution – providing feedback to the user about how well the solution meets their requirements or needs or opportunities in terms of efficiency and effectiveness. This is based on the findings of the data gathered at the beginning of the evaluation stage when compared with the evaluation criteria created during the design stage. 2 Working out an evaluation strategy – creating a timeline for when various elements of the evaluation will occur and how and what data will be collected (because it must relate to the criteria created at the design stage). PROBLEM-SOLVING METHODOLOGY xv 9780170440875  Key concepts Within each VCE Applied Computing subject are four key concepts the purpose of which is to organise course content into themes. These themes are intended to make it easier to teach and make connections between related concepts and to think about information problems. Key knowledge for each Area of Study is categorised into these key concepts, but not all concepts are covered by each Area of Study. The four key concepts are: 1 digital systems 2 data and information 3 approaches to problem solving 4 interactions and impact. Digital systems focus on how hardware and software operate in a technical sense. This also includes networks, applications, the internet and communication protocols. Information systems have digital systems as one of their parts. The other components of an information system are people, data and processes. Data and information focuses on the acquisition, structure, representation and interpretation of data and information in order to elicit meaning or make deductions. This process needs to be completed in order to create solutions. Approaches to problem solving focuses on thinking about problems, needs or opportunities and ways of creating solutions. Computational, design, and systems thinking are the three key problem-solving approaches. Interactions and impact focuses on relationships that exist between different information systems and how these relationships affect the achievement of organisational goals and objectives. Three types of relationships are considered: 1 people interacting with other people when collaborating or communicating with digital systems 2 how people interact with digital systems 3 how information systems interact with other information systems. This theme also looks at the impact of these relationships on data and information needs, privacy, and personal safety. xvi KEY CONCEPTS 9780170440875  3 Unit INTRODUCTION In Unit 3 of Data Analytics, you will use data that you have effectively and efficiently identified and extracted. You will consider the integrity and source of the data and make sure that you have correctly referenced the data using the American Psychological Association (APA) referencing system. You will manipulate this data to create data visualisations and infographics. To do this, you will use databases, spreadsheets and data manipulation software. You will propose a research question and then collect data to answer this research question. You will use a range of methods to analyse this data. You will use all the stages of the problem-solving methodology (PSM) to prepare a project plan. This will complete the first half of the School-assessed Task (SAT) (Unit 3, Outcome 2). The second half of the SAT will be completed in Unit 4 (Unit 4, Outcome 1). Area of Study 1 – Data analytics OUTCOME 1 In this Outcome, you will respond to teacher-provided solution requirements and designs. You will extract data from large data repositories. You will manipulate and cleanse the data and use database, spreadsheet and data manipulation software to present your findings in the form of a data visualisation. Area of Study 2 – Data analytics: Analysis and design OUTCOME 2 In this Outcome, you will propose a research question and then collect data to answer this research question. You will use a range of methods to analyse this data. You will use all the stages of the problem-solving methodology to prepare a project plan. This will complete the first half of the School-assessed Task (SAT) (Unit 3, Outcome 2). The second half of the SAT will be completed in Unit 4 (Unit 4, Outcome 1). nilmerg/moc.kcotSi 1  1 CHAPTER Data and presentation KEY KNOWLEDGE FOR THE STUDENT If you can imagine the sheer amount of data that is generated every day, After completing this chapter, you will you might also be able to imagine that there is someone, somewhere, be able to demonstrate knowledge of: who is looking through a mountain of data searching for meaning. Data visualisation is the process by which we take large amounts of data and Data and information process it into effective graphical representations that will meet the techniques for efficient and effective data needs of users or clients. These representations can take the form of collection, including methods to collect charts, graphs, spatial relationships and network diagrams. census, Geographic Information System In some cases, the data visualisation might involve interactivity and the (GIS) data, sensor, social media and weather inclusion of dynamic data that allows the user to deduce further meaning factors influencing the integrity of from the visualisation. This chapter will cover the definitions of data and data, including accuracy, authenticity, information, the various ways in which data can be acquired and referenced correctness, reasonableness, relevance and how to check that data is reliable enough to be used to generate and timeliness useful information. This chapter will then look at the many types of data sources of, and methods and techniques visualisations and the design tools that could be used to help plan their use. for, acquiring authentic data stored in large repositories methods for referencing primary and FOR THE TEACHER secondary sources, including American This chapter introduces students to the knowledge and skills needed to Psychological Association (APA) referencing system use software tools to access authentic data from repositories and present characteristics of data types the information in a visual form. It covers data types, data integrity and citing references before covering a range of data visualisation tools and Approaches to problem solving their purposes. methods for documenting a problem, The key knowledge and skills are based on Unit 3, Area of Study 1. If a need or opportunity data visualisation is effective, it reduces the effort needed by readers to methods for determining solution interpret information. This chapter takes students through the different requirements, constraints and scope types of visualisations. This chapter, combined with Chapter 2, will form design tools for representing databases, a foundation for the Unit 3, Outcome 1 School-assessed Coursework spreadsheets and data visualisations, (SAC). Much of this will be applied when students work on their School- including data dictionaries, tables, assessed Task (SAT). charts, input forms, queries and reports design principles that influence the functionality and appearance of databases, spreadsheets and data visualisations formats and conventions applied to data visualisations to improve their effectiveness for intended users, including clarity of message Interactions and impact reasons why organisations acquire data. jtisnom/moc.kcotSi Reproduced from the VCE Applied Computing Study Design (2020–2023) © VCAA; used with permission. 2 9780170440875  CHAPTER 1 » DATA AND PRESENTATION 3 What is data? Datum is the singular form of data, which is Data is made up of facts and statistics. Raw facts have no context to them, so you cannot technically plural. Today, nearly everyone uses data, make much sense of them, or give them any meaning. To understand and make meaning of the plural form, for both data, you need to process or manipulate it, converting it into something useful: information. singular and plural. Data consists of raw, unorganised facts, figures and symbols fed into a computer during the input process. Data can also refer to ideas or concepts before they have been refined. In addition A cookie is a small file to text and numbers, data also includes sounds and images (that are both still and moving). that a web server stores on a user’s computer. Organisations are collecting this data in vast quantities every day. It allows them to plan day-to- Cookies typically contain day operations, make better business decisions as well as to better understand their customers. data about the user, such as their email address and There are several ways in which this data is gathered by organisations. Popular methods browsing preferences. include analysing comments on social media, tracking activity on product websites, placing The cookie is sent to the cookies on customer computers and tracking IP addresses. computer when a website is browsed and stored on Organisations can then use data for specific purposes such as targeting customers with the computer’s hard disk. advertising tailored to their interests, developing new products and improving existing ones, The next time the website is visited, the browser and even protecting data (for instance, when banks analyse your credit card usage patterns retrieves the cookie from to identify potential fraudulent transactions). There is no doubt that understanding the the hard disk and sends the data in the cookie to customer remains a key need of any organisation. the website. Cookies are The potential value of data cannot be fully unlocked without processing it into not viruses because they information. Marked ballot papers after an election hold a great deal of data, and thus cannot be executed or run, and they cannot replicate considerable potential value, but in their raw form, they hold little value. They need to be themselves; however, they processed through counting and grouping the ballot papers into their electorates before they can be misused as spyware. Cookies can be used to become useful information – that is, election results. track people, which leads to privacy issues. a b c TSEW MAILLIW/PFA/segamI ytteG enaK luaP/segamI ytteG FIGURE 1.1 a Raw data in the form of ballot papers are b grouped and counted to produce c election results. In this chapter, you will learn more about data, data collection and how information is The IP addressing formed and visualised through manipulation. As part of Unit 3, Outcome 1, you will need standard – four to collect data and manipulate it using both a relational database and spreadsheet software numbers between 0 and 255 separated by to create meaningful data visualisations. These manipulations will be covered in detail in full stops – defines Chapter 2. a mechanism to provide We will also discuss the data you need to collect, in terms of how it should be treated and a unique address for each computer on a network. manipulated, and how limiting factors involving constraints and scope can affect the data. Data should be gathered from reputable sources, so we will cover how you can acquire data through existing sources, and then measure suitability and integrity by questioning how the data was acquired, such as through surveys, interviews or observation. We will also discuss how to reference those sources properly. SCHOOL-ASSESSED TASK TRACKER ☐ Project plan ☐ Collect complex ☐ Analysis ☐ Folio of alternative ☐ Infographic or ☐ Evaluation and ☐ Finalise report or data sets designs dynamic data assessment visual plan visualisations 9780170440875  DATA ANALY TICS VCE UNITS 3&4 4 Once you have gathered all of this data, you need to store it, protect it and understand what type of data it is. We will discuss data integrity and how to maintain it through measures such as timeliness, accuracy, authenticity and relevance. This is important because you need to maintain the integrity of the data collected for your Outcome so that your final product can be considered reliable. Why organisations acquire data Organisations depend on data in order to function. They use it to keep track of stock levels, client details, employee details, rosters, finances and records of their work. They also use data analytics to make predictions about all aspects of their business. If an organisation were to lose all of its data, it would suffer greatly. The organisation would not be able to keep track of any finances, it would lose track of its client-base and, if the general public became aware of the data loss, it would suffer a loss of reputation. This would potentially result in fewer clients and the possibility of legal action and closure of the business. Information needs When clients or users require particular information, and no system currently exists that provides the information, then an information need has been identified. This could be due to an existing information problem (an organisation worried about declining sales), an identified need (park rangers needing a method to communicate weather conditions on total fire ban days), or an opportunity (currently no list of driving instructors in Victoria exists). When an information need has been identified, one process used to create a solution that will meet the needs of the clients or users is the problem-solving methodology (PSM). Data acquisition Trusting that data has been Acquisition is when raw data is gathered from the world outside the information system. collected and stored in a First-hand, or primary data, may be acquired manually, via surveys, interviews or observation secure manner is discussed or it may be acquired electronically. Electronic acquisition can be completed in many ways: on page 20 under the ‘Data integrity’ heading. through cameras, people inputting data manually, sensors detecting something such as movement, or it may be acquired through other electronic means (for example, if using a keylogger or scanner). Data can also be acquired by locating repositories of data that already exist, often online, that has been compiled by someone else or another organisation. If it has not been collected by the organisation directly, or if it has been manipulated or summarised in any way, then it is considered secondary data. This form of data can save a lot of time, but you must ensure that the data has come from a trustworthy source. It is important to know how this data was collected, by whom and if there are any reasons to doubt its reliability. Primary and secondary data Data that has not been filtered by interpretation or evaluation is called primary data. Often, these are facts that you, the researcher, have collected directly to answer a specific question, but it may also be old data that has never been given proper scrutiny before. The lack of previous interpretation is what categorises something as primary data. 9780170440875  CHAPTER 1 » DATA AND PRESENTATION 5 Suits your research question exactly No mysteries about the source No mysteries about how it has been statistically altered or edited Strengths You know if follow-up data can be sought No original data has been lost so you can analyse in detail Primary data Use it to gather specific data to support or refute a hypothesis based on secondary data Time and labour intensive Weaknesses Expensive to collect Data is scarce compared with research backed by universities or companies FIGURE 1.2 Strengths and weaknesses of primary data Journal articles, results of questionnaires, diaries and letters, emails, internet postings, speeches, audio and video recordings (if unedited), official documents and photos may all be primary data. Therefore, when you, as the researcher, need to interpret or evaluate unedited data that you are using in your Outcome, it is primary data. Researchers also collect (new) primary data using interviews, focus groups, surveys, experiments, observations and measurements. Secondary data differs from primary data because it has been collected and interpreted by someone other than the researcher. The collectors of the data could be other researchers, or government departments, or any of a variety of sources: encyclopaedias and other books, biographers, conductors of polls and surveys, journalists, newspapers and magazines, the Australian Bureau of Statistics, internet posts, databases and so on. When using secondary data, it is especially important to consider whether both the data and its interpretation are from a reputable source. Therefore, when you collect data for your Outcome that has been interpreted by someone else already, you should consider this to be secondary data. SCHOOL-ASSESSED TASK TRACKER ☐ Project plan ☐ Collect complex ☐ Analysis ☐ Folio of alternative ☐ Infographic or ☐ Evaluation and ☐ Finalise report or data sets designs dynamic data assessment visual plan visualisations 9780170440875  DATA ANALY TICS VCE UNITS 3&4 6 Cheap and quick to collect: someone else has paid for the experiments and performed all the tedious tasks Huge amount of data available – more than you could ever source by yourself Only way to collect historical data Can be gathered from many locations Strengths over a large area and over a long span of time Can support a researcher's own findings Can provide a baseline to which primary data can be compared Helpful for gathering data to Secondary data formulate a hypothesis May be partly irrelevant to the research question Sources and context may be unknown and unknowable Potentially inaccurate, biased, Weaknesses unrepresentative or even false Could have been edited too much and distorted as a result Gaining access to the original untouched data may be impossible FIGURE 1.3 Strengths and weaknesses of secondary data Techniques to collect data Techniques to collect data Ideally, data should be collected both efficiently (not wasting time, cost or effort) and will also be covered later effectively (in a way that ensures data is complete, usable, accurate and current). How when you begin working the data is collected can affect the quality or integrity of the data. Data can be collected by on your SAT for Unit 3, Outcome 2. using surveys, interviewing participants or observation. Each method has its own benefits and drawbacks; you need to know how the data you are using has been collected in order to understand the impact it will have upon the quality of your first SAC. 9780170440875  CHAPTER 1 » DATA AND PRESENTATION 7 Survey Surveys are a fast and relatively cheap way to gather large amounts of data and feedback. They can be administered in many different ways – online (having users enter their own data makes things much easier and quicker), on paper (circling numbers, ticking boxes, writing short responses) or verbally in person or over the telephone. The questions in a survey remain identical for each person completing it, so that if any further clarification is required, this cannot be done, especially if the survey is anonymous. If it becomes apparent that extra questions need to be added, it is probably too late to do so – once produced, a survey is fixed. For example, if a respondent misunderstood a question, or if the response given contradicts an earlier response, then the quality of data might be affected. There is no way of gauging whether or not people are being honest in their responses as with a face-to-face interview, which might allow for tone and body language to make points of view clearer. Question types are limited to: checkbox for Boolean data: yes/no or true/false questions only scaled responses: Likert scales ask respondents to select how they feel about a particular statement, asking for a number from 1–5 (or any scale) closed questions: asking for a response from a set number of options open questions: respondents give worded responses with no limitations; this can give more detail, but it is more difficult to use this data when collecting from a large number of people. vopoP_yerdnA/moc.kcotsrettuhS FIGURE 1.4 Conducting surveys online saves time and effort because they avoid added manual data entry. Interview Interviews take place with two or more people in real time. They can be conducted face-to- face, via video or telephone, and with individuals or small groups. Like surveys, the questions are usually written in advance so that responses can be compared to get the big picture from the data collected. Unlike surveys, respondents can request clarification if they do not understand a question, or the interviewer can probe for more detail if they think that it is appropriate. For example, an interviewer might ask the respondent to provide an example or ask why they said something. Interviews are more costly, in that they cannot be deployed to as many people or as quickly as surveys, but the quality of data is usually better. SCHOOL-ASSESSED TASK TRACKER ☐ Project plan ☐ Collect complex ☐ Analysis ☐ Folio of alternative ☐ Infographic or ☐ Evaluation and ☐ Finalise report or data sets designs dynamic data assessment visual plan visualisations 9780170440875  DATA ANALY TICS VCE UNITS 3&4 8 baLiduaG/moc.kcotsrettuhS FIGURE 1.5 Interviewing allows for follow-up and clarification of questions. Observation Observations are the most costly data-collection technique when collecting data about real- time events or existing processes. They involve watching and taking notes in real time as an impartial observer. This is far slower than the previous two techniques, but results in far richer and more genuine data. This kind of data collection is better suited to studies that do not require large amounts of data. Observations are generally a good idea to combine with surveys and interviews to allow more information to be collected. For example, if a respondent’s body language in an interview indicated that they were unsure of their answer, the interviewer could request more detail around the response to gather more usable data. Sensor Sensors or data loggers are devices used to detect characteristics of the environment around them. This may include temperature, humidity, light levels, motion, touch, and the amount of gases in the air. Sensors are always connected to other electronics that can interpret the electrical signals they generate. Sensors can be connected together to provide an overall snapshot of a particular environment (as with a weather station) and even set-up to transmit tihcuhP/moc.kcotSi FIGURE 1.6 Sensors can be used to measure air quality. 9780170440875  CHAPTER 1 » DATA AND PRESENTATION 9 data from remote locations (including other planets). It is important that data collected from sensors is validated to reduce anomalies and stored securely to reduce the possibility of data loss. Sensors collect data electronically. Once installed, they can monitor and transfer data to storage without human intervention. They can operate 24 hours a day, 7 days a week, and they can gather data on weather, movement, traffic speeds on roads, levels of light, noise or pollution, or numbers of people or cars entering or leaving facilities, or almost anything else. Methods applied to specific data collections The following data repositories have collected vast amounts of data in various ways. The organisations and their data will be discussed later in this chapter. Considering the sheer volume of data being collected by organisations, it would be impossible for a human to record it either efficiently or effectively. Therefore, there are several automated processes commonly used to gather data. Common types of data to be gathered include census, Geographic Information System (GIS) data, sensor, social media and weather data. Census Every five years, the Australian Bureau of Statistics (ABS) conducts the Census of Population and Housing. This is one of the largest data collecting activities in Australia. It is designed to provide a demographic snapshot of Australian society. In previous years, households were asked to complete a paper survey booklet containing questions about various characteristics of the people living in a house on a particular night (Census Night). The 2016 Census was the first time census data could be entered via an online portal. Households were provided with login details for authentication. Responses to questions were validated electronically and deidentified. While the collection of census data was easier in 2016 when compared with the paper-based forms, the data entry process was more vulnerable to interference, as was demonstrated when hackers caused the portal to be shut down temporarily. Those who were unable to complete the census online were given extra days to complete the task. It is a legal requirement for each household to complete the Census, and the honest, accurate completion of nearly every question is required. 42sirhc/otohP kcotS ymalA FIGURE 1.7 The ABS census provides a snapshot of Australians and Australian households every five years. SCHOOL-ASSESSED TASK TRACKER ☐ Project plan ☐ Collect complex ☐ Analysis ☐ Folio of alternative ☐ Infographic or ☐ Evaluation and ☐ Finalise report or data sets designs dynamic data assessment visual plan visualisations 9780170440875  DATA ANALY TICS VCE UNITS 3&4 10 Weather The Bureau of Meteorology (BOM) uses sensors to collect data on all aspects of weather and climate. Data is automatically gathered and stored and is made available on the BOM website. The sensors are not subject to personal bias or human error, and unless they malfunction their accuracy and reliability is very high. The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) in the United States also use sensors to gather climate data, which is available from their websites. Social media Many social media sites generate data from their users. This data, sometimes only available through subscription, summarises how far the account has reached, and how many views and interactions have happened. It is not always easy to locate the raw data. Social media sites also take part in data mining – storing data about its users. This data is gathered in order to learn about users for, among other things, targeted advertising. Interaction with popular social media platforms such as Facebook, Twitter and LinkedIn provides a range of measurable data elements. This data comes from interactions such as clicks, comments, likes, shares and conversations. Social media users can also be broken down further by location and language preferences. This data can reveal the success of a marketing campaign or evaluate customer impressions of a product. Geographic Information System (GIS) Geographic Information Systems use sensors to record data about Earth. They capture the data, store it, carry out manipulation to create useful information and then present it in easily understood ways. A GIS stores data on geographical features as well as their characteristics. Features are usually classified as points, lines or areas. Data might also be stored as images. City data on a map might be stored as points, road data as lines, and boundaries as areas, while aerial photographs or scanned maps could be stored as raster images. The ABS presents a lot of their data as GIS data. Data sources Finding a relevant data source takes thought, judgement and care. A great deal of data is available online. The trick is to find the data that is worth using and to use it correctly. While Chapter 3 deals with the forming of a research question, this chapter is about the nature of data itself. It is important that you investigate both primary and secondary data sources. This will be discussed in much more detail as you prepare for the next Outcome. The source of data is very important. Treat data without an identified source with care and take necessary steps to resolve matters of authenticity. When the source of the data is known, it is more reliable. Without this, your trust would be blind because you would not be able to contact the data’s creator – the data could even be a work of fiction. Putting complete trust in anonymous data is especially unwise. For example, if you find survey results on social media, it is important to check to see if it is supported by substantiated facts and that the data has not been made up by somebody. However, if you know how data has been collected, statistically manipulated, edited and/or abridged, you can interpret the information more wisely. A reputable data source 9780170440875  CHAPTER 1 » DATA AND PRESENTATION 11 with authority is more likely to provide high-quality data. Be wary of data given by people who are experts, but not necessarily experts in the relevant field. The opinion of a champion footballer on politics, or an actor on climate change, are no better than any other person’s opinion. There are hundreds of data sites in Victoria and Australia on many topics. Choose a question that interests you. Search online fo