Introduction Our philosophy Important information for all assignments Feedback in DSI AT1: A discussion paper Assessment 1 general guidance Scaffold for the paper (on campus, week 3) Timeline and Benchmarking task instructions How to write and submit AT1 AT2: Quantified Self Report Assessment 2 general guidance Guidance on the criteria Scaffold for AT2 Timeline for AT2 How to write and submit AT2 Introduction This document should be read in conjunction with the Subject Outline, available via the subject Canvas site. Our philosophy Assessments in DSI - and the whole of the MDSI - are authentic, and embedded in our teaching. We won’t ask you to write an essay, and you won’t spend weeks studying for a final exam that only covers ⅓ of the subject content. We design the subject content - both online, and in class - to contribute directly to what you need to do to complete the assignments. And the assignments are all meaningful. You could use them to create a professional portfolio of work, demonstrating the key graduate attributes of a data scientist. Important information for all assignments The assessment for this subject consists of the following components: Deliverable Description Weight Assessment task 1 Analysis of Contemporary trends in data science (individual) 25% Assessment task 2 Quantified Self Project: stories and accounts discovered in data relationships (individual): Part A: 1 page minimum Part B: Substantive draft + peer review Part C: 3000 words final submission (90%) 75% You will find all you need to know about your assessments under Assignments on Canvas. For all activities and assignments, your work must be uploaded via Canvas for marking by the assessment deadline. Follow our directions in the announcement for each assessment and in the modules. Submission Requirements: ● All assignments need to be submitted via Canvas unless otherwise instructed ● Submissions must include a title page with subject name, student name and ID, date of submission and the title of the assessment.
● Please use the following file naming format for each submission: ○ For individual assignments: StudentName_AssignmentName_Date. ● Please use embedded objects instead of linked objects for content sourced externally ● Citation: Proper referencing is mandatory (Harvard or APA style preferred) for all externally sourced material. We recommend using a citation manager like Endnote, Mendeley, or Zotero. The library has guidance, and runs workshops, on this http://www.lib.uts.edu.au/help/referencing ● Length: Submissions exceeding task Length by more than 20% will be penalised (10% of the overall assessment mark). Tables, figures, references, and appendices are not included in word limits. You should aim to write as much as the word limit allows. IF you need to reduce: ○ Check you need each quote. You may be able to paraphrase more briefly and cite the source without directly quoting ○ For every sentence, check it's adding new information. You want a claim, an elaboration (examples, explanations, evidence, etc), and so what (why it matters to your argument), but don't worry about repeating lots of things, or adding extraneous info. ○ For each section, look at the criteria. They're evenly weighted which generally means they should be roughly weighted the same in text too. If you need to cut and you can't find obvious things to take out, be really brutal and just find the 10% you need to take out from each sub section, or sections that are overweighted. Extensions and Late Penalties: ● If unavoidable circumstances arise students can apply before the assignment due date for an extension of up to one week by sending an email to the subject coordinator. This needs to outline the reason they are unable to submit on time and include an outline of how far they have progressed with the assignment. ● Extension requests submitted after the due date will only be considered in exceptional circumstances; note that work, travel or hardware issues are not valid reasons.. ● Unless formal extension dates are agreed upon in writing each late submission will be penalised 10% per day after the due date (to a Pass grade). ● All assessments in a subject must be submitted at a Pass level. If a submitted assignment does not achieve a Pass grade on submission, students will be given an opportunity to resubmit for a maximum of a Pass grade at the subject coordinator’s discretion. Special Consideration: Extensions of more than 1 week require a formal application for Special Consideration in accordance with university policy. Feedback in DSI To help you complete your assignments, and develop your professional skills, we’ll give you feedback. Feedback is crucial to help people learn, but it’s only useful if people pay attention to it. In DSI, you’ll receive grades for each assignment, made up of criterion-level grades. We don’t tell you the scores because they’re not very important. 1. Exemplars, and formative tasks we set before you submit - generally we’ll discuss some examples that could be improved, and some that are pretty spot on. But, we’re always looking for new creative approaches, so don’t be constrained by the examples we show! 2. The criteria for each task, and your grade on each criterion - the criteria tell you what you need to include in each assignment, and your grade for the criterion is a piece of feedback that tells you how well you address it; you should pay attention to these! 3. Written comments - we spend time providing written feedback on each assignment. You may find it helpful to try and apply this feedback to your assignment, and to use it when reflecting on your future assignments. 4. Feedback you get from other people - I cannot recommend enough having other people read your work, even reading it aloud to yourself can help you take a different perspective. We’ll ask you to self assess most assignments, using REVIEW. This is because: 1. Judging the quality of work is an important professional practice, including judging the quality of your own work 2. Self-assessment helps you get better grades because you can see places you can improve; By building up your skill in self-assessment, you shouldn’t be surprised by results you get; 3. Self-assessment helps us give you better feedback, because we can target feedback to the criteria where there’s the biggest difference between how we thought you did, and how you thought you did. This feedback is always written to help you improve - after each assignment it’s a good idea to have a go at implementing the feedback, and using it to help you in your next assignment AT1: A discussion paper Assessment 1 general guidance We’re asking you to write a discussion paper as an authentic task – it’s something data scientists do. As we’ll discuss in class, even in this context it’s important to make use of existing knowledge to support your arguments and set out the background; that should include scholarly articles. We want the piece to be scholarly in style, making some use of peer-review literature, and following conventions for style/layout/formatting, etc. The assessment criteria provide your most important guidance. The best discussion papers make clear how particular opportunities for innovative use of data are addressing factors or challenges that drive the innovation, while highlighting issues that will be faced in implementing these innovations, and how the innovation will actually have an impact on the sector. For this task you should: ● Identify your selected organisation or sector ● Describe the opportunities and problems that the organisation or sector is facing, analysing their role in driving change – it’s good to think about specific problems that an organisation is facing ● Articulate opportunities for the use of data to drive change in your organisation or sector ● Evaluate the impact that the changes would make on the organisation or sector ● Evaluate the potential data issues inherent in the implementation of the highlighted opportunities To support your claims you will need to draw upon relevant scholarly peer reviewed literature and current industry sources. The best discussion papers make clear how particular opportunities for innovative use of data are addressing factors or challenges that drive the innovation, while highlighting issues that will be faced in implementing these innovations, and how the innovation will actually have an impact on the sector. Innovation generation: In addition to the resources we provide in week 3, you might find these innovation canvases helpful: ● Our own simple canvas (recommended): PDF here (editable on Canva, here) ● The Beyond Data Science AI Project Canvas https://towardsdatascience.com/introducing-the-ai-project-canvas-e88e29eb7024 ● A Data Innovation Board https://medium.com/d-lighted/the-data-innovation-board-44b9cc35e0ca You do not need to write an executive summary for this task, or present all of the opportunities for change in the organisation or sector. You only have 1800 words, so select key factors to focus on. Your discussion might cover a whole sector (e.g. telecoms) or particular technologies, trends, or opportunities within a sector (e.g. the potential of increasing data speeds, geolocation, etc.). I’ve put some sample discussion papers in this Zotero collection, with some stared (search the tags) to indicate interesting cases. We’d encourage you to use headings. Most professional writing, including the academic subset, involves strong signposting. Headings are a great way to do that signposting, but of course shouldn’t substitute for good flow and argument structure. The bullets above could, for example, be used to create key headings. If you’d like some quite introductory guidance on using headings, these two resources may be useful – and do please share other resources or writing tips if you find them useful – http://learninghub.une.edu.au/tlc/aso/aso-online/academic-writing/headings.php and http://www.monash.edu.au/lls/OffCampus/Improve/9.11.html To make a reliable and convincing case, you should consider your evidence base. You might find it useful to consult resources on evaluating information, e.g. https://prod.owl.english.purdue.edu/owl/resource/588/02/ Think about where you can make use of reliable industry sources or reports (e.g. Pew Internet reports) and literature, including peer-reviewed literature in your discussion paper. For example, peer-reviewed literature might address: ● Challenges in a particular sector (e.g. the need for increased capacity for public transport use); ● New technologies, methods, or opportunities in a sector (e.g., about general issues such as the use of open data, or more specific issues such as emerging analytic techniques, or research-prototype technologies); ● Challenges involved in obtaining, processing, and analysing data (e.g. on specific issues such as the reliability of quantified-self devices). For examples – but not an exhaustive list – of peer-reviewed literature, explore the examples in the Zotero collection. As part of your professional presentation, you should cite your sources and include a reference list (using a consistent citation format). See the subject outline guidance for further information regarding professional writing and presentation. Scaffold for the paper In addition to the exemplars we provide for the benchmarking task, you might also find it useful to use a scaffolding tool. These are just templates that help you structure the ideas: ● Our own simple canvas (recommended): PDF here (editable on Canva, here) ● The Beyond Data Science AI Project Canvas https://towardsdatascience.com/introducing-the-ai-project-canvas-e88e29eb7024 ● A Data Innovation Board https://medium.com/d-lighted/the-data-innovation-board-44b9cc35e0ca ● A DSI writing template (from us) https://docs.google.com/document/d/10FI22idmkhs8prx35OfYbhimrHR13Lca/edit?rtp of=true Timeline and Benchmarking task instructions 1. Week 1-2: Briefing on assessment 1 we considered: a. The kinds of writing that a data scientist might engage in and with b. Whether that writing was unique to ‘data science’ (if so, what makes it so?), or if other disciplines also do this kind of writing c. How ‘academic’ or scholarly the different writing types are, and why? 2. Week 1-2: Use the scaffold (above) to start your assignment; create a Medium account (see below) 3. Week 2-3: read example reports and complete the benchmarking task (in google form) for assessment 1 a. Access the google form here https://forms.gle/2nsT3gm5ab8Vv4CaA b. View the texts (accessible through the form itself and also downloadable here), and assess them on the online google form, giving comments. 4. Week 2-4: Critically investigate examples of discussion/white papers from industry (there are some samples here: https://www.zotero.org/groups/mdsi/items/collectionKey/HJ8RZ7V6 - do share any interesting examples you find!) 5. Week 2-4: Review feedback on previous discussion papers (and the benchmarking papers) 6. Week 6 – Assessment 1 due How to write and submit AT1 The MDSI aims to build your professional practice, support you in developing a portfolio of work, and expose you to a range of authentic tools and platforms for data science. For AT1 in DSI, we want you to submit your discussion paper in the form of a Medium blog. To do this you must: 1. Read the Medium terms, crucially, note you retain ownership of anything you submit https://medium.com/policy/medium-terms-of-service-9db0094a1e0f 2. Create an account (e.g. with your UTS email, https://help.medium.com/hc/en-us/articles/115004915268-Sign-in-or-sign-up-by-email ) You can choose your own username. 3. You should draft your piece in a word processing environment (like Word), using a reference manager. 4. As you write, ensure that you have rights to all the content you use - understanding basic IP any copyright is important. See e.g. https://www.lib.uts.edu.au/about-us/policies-guidelines/copyright-and-uts/copyright-st udents-and-researchers a. If you’re worried about breaching your own organisation’s IP please talk to me. b. Ensure you use standard practices of citing your sources c. If you use images, attribute their source and ensure you have permission to repost the image 5. When you are ready, create a new draft post in Medium. Tweak it there as you like, and add categories, etc. 6. To submit, by the deadline: a. Complete REVIEW self assessment https://uts.review-edu.com/uts b. In Canvas, you should post (1) a Word/PDF version, and (2) the link to your draft in the AT1 assignment comment box. You can find and share your draft URL in Medium using this menu: c. We want you to submit the draft post to the publication Trends in Data Science. Instructions on how to submit to a publication are here https://help.medium.com/hc/en-us/articles/213904978-Add-draft-or-post-to-pu blication This is a private submission to the publication. We will add you to the publication as a ‘writer’ to enable you to submit. 7. We’d love you to publish publicly afterwards, especially once you’ve taken on board feedback, and we’ll work with you to do that. AT2: Quantified Self Report Assessment 2 general guidance Assignment two has three parts: 1. AT2a is due week 5, and is a short online form (only available in the week before due date) 2. AT2b is due week 9, via Canvas, and consists of (a) a draft of your final submission, and (b) your feedback to your class colleagues via peer review 3. AT2c is your final submission, due in the UTS exam period This structure ensures you’re on track for the assignment, and provides an opportunity for you to resubmit your AT2 taking into account the feedback provided to make changes. For AT2 you will collect, record, share, and analyse several types of data about yourself and compare and contrast what you find in your analysis with an analysis of the same data from the group. The following requirements apply to your data collection: 1. Two sources of data negotiated with your group for sharing: a. Unstructured. One of which must be unstructured in nature (e.g. text, comments, images, audio, etc. You might obtain this from social media, email, slack, twitter, daily photos, etc.) - you may find the ‘what does facebook know about you’ materials useful for this. b. Your choice. The second source can be structured, drawing on one of the many examples provided. 2. You are expected to individually collect one other data source of personal interest to you. This data does not need to be shared across the groups, but should be analysed by you in your report. 3. One external cohort-level dataset (this might be summary data): The idea of this dataset is that you will have data from: (1) an individual, (2) a small group, and (3) a larger cohort. You might, for example, draw on published summary level data (for example, what is the average step count in Australia?...for who?), or publicly available stepcount data. For example, you may be able to obtain data from one of these sources: a. http://openhumansfoundation.org/ohjh-example-notebooks/ b. http://vhosts.eecs.umich.edu/vision//activity-dataset.html interesting image dataset c. http://quantifiedself.com/open-data/ d. http://www.cs.vu.nl/~mhoogen/ml4qs/crowdsignals.zip described http://crowdsignals.io/ e. http://www.cis.fordham.edu/wisdm/dataset.php f. http://www.cis.fordham.edu/wisdm/dataset.php#actitracker You will negotiate and agree a processes for recording, sharing and storing the data being collected as a group, in the on-campus briefing session for AT2. Your attendance at this session will be crucial in getting off to a strong start with a minimum of disruption for this major task. Examples of data that you and your group could collect include: daily step counts; pulse rates; time spent on activities each day (exercise, grooming, travelling, eating/cooking, shopping, sleeping studying, etc.); sleep patterns; daily spending; number & length of conversations each day; location tracking, and so on. Some of these can be easily tracked via smartphone apps, see examples at https://quantifiedself.com/ Guidance on the criteria Criterion 1: Strength of justification for the method to obtain data from multiple sources, for gaining insight into a chosen problem, including analysis of data quality issues in the individual and group data. Consider, do you tell the reader: ● What data you’re collecting ● Why that data is interesting to collect ● How you’re collecting the data, justifying that method (evaluate the benefits/issues with the app or method you choose, etc.) ● What is the quality of the data you have (missing data, issues in the data, etc.) and implications Criterion 2: Insightfulness in the analysis of the obtained data, including quality issues, to draw conclusions in a professional and engaging manner. Consider, do you: ● Provide analysis, presenting both a range of visualizations/data summaries and drawing conclusions from them? ● Contextualise your findings, noting your particular context (you know about this data!), and limitations in the analysis? ● Use appropriate analysis (we discuss this in class) Criterion 3 Insightfulness in identification, contextualisation and reflection on ethical, privacy, and legal issues relevant to the collection, analysis, and use of one's own and other's personal data: The AT2 is designed to give you experience in collecting and working with personal data. It can be confronting at times, and you should consider issues of privacy and ethics, with regard to both the specifics of what you did, and implications for data science, connecting to legal and ethical frameworks. You could imagine what would happen if a data science troll (malicious agent) gained access to your data. We want you to consider: ● What harm can be done? (i.e., what is the risk, and the likelihood of those risks occurring?). You could consider this both in relation to insights from your own data, and the implications of those insights over much larger datasets. Imagine that we conducted your QS project on many more people, and now have millions of datapoints on the variables you collected...what insights can be gained (by individuals and companies?), what is the balance of concerns? What are the legal/privacy implications (i.e., what laws or principles have been breached?) and ethical ethical considerations? (i.e., what insight do ethical frameworks provide us to navigate the issue) ● What strategies could be adopted to do differently? Again, consider both your own case (“We should have…”) and wider practice (“App policies should…”) Think about: 1. Is there relevant ethical or legal guidance from: Australian law, professional organisations (medical, sales/marketing), non-disclosure and consent agreements, terms terms and conditions that you sign when using various apps and websites, or ethical frameworks, and how they help us navigate the benefits and risks of actions 2. Tools like the ODI data ethics canvas https://theodi.org/article/data-ethics-canvas/ or The Omidyar EthicalOS Risk Mitigation checklist: https://ethicalos.org/wp-content/uploads/2018/08/EthicalOS_Check-List_080618.pdf Criterion 4 Strength of connection between the individual experience of this QS project to the practice of data science (and the preceding three criteria): When you’re writing, think about both the specifics of your own analysis and insights, and what your work tells us about the wider practice and implications of data science, drawing on sources to contextualise and support your claims. Criterion 5 Level of professionalism in the presentation appropriate to the discipline: You can see specific guidance on this criterion in the subject outline. Remember, your visualisations, and the way you develop your narrative are a part of professional presentation. You should draw on external sources to support and contextualise your work throughout. Be careful to emphasise interpretation and analysis over description and narrative. So, don’t tell us about discussions you had and who said what (description), tell us about the decisions you made, why, and their implications for the practice of data science (analysis). Scaffold for AT2 To support you in AT2, you should explore: 1. To initially think about your questions and data you might use this simple template https://drive.google.com/uc?export=download&id=1RgqHDSRZomIkFXWLoUTLtRrQ nmEkYfJ5 2. Old examples of this assignment, and all of our feedback given in a previous semester at https://drive.google.com/drive/u/1/folders/1bxq1cISULLcmU7kRdadCg-RszVrBKGbA 3. Download the template folder(s): Three versions, from which to choose: a. Very simple - outline, headings, marking criteria Recommended: New to R; will be creating charts graphs in another application and saving to images https://bit.ly/2LaKAqe b. Simple - outline, headings, marking criteria, some graphs Recommended: Some experience with to R; keen to try generating charts in R; https://bit.ly/2MD7cD2 c. Medium - more complex, with outline, headings, marking criteria, and code examples. (Often requires troubleshooting, particularly for the citation library) Recommended: Experience in R; keen to try generating charts in R; Want experience using a research template tinyurl.com/DSIAT2Template https://drive.google.com/drive/u/1/folders/1k0UfbMqf2bjvaoXYcdhmFLrEyHhE mVtx or/and view it online at http://rpubs.com/sjgknight/AT2-Template 4. You might find vignettes at https://sjgknight.github.io/DSI/ helpful if you want to use R to do analysis (but remember, you do not have to) 5. View examples of quantified self dashboards that have been written in R notebooks, for example ● https://rpubs.com/Arunash/300206 ● http://www.rpubs.com/snowan/Quantified_Self ● https://rpubs.com/louislouis/quantifiedself-anly512 (which uses a nice package using Flexdashboard) Timeline for AT2 To keep on track, here’s roughly where you should be at for each week: 1. Pre-work What Does Facebook Know About Me? 2. Choose group, establish communication and data sharing method, begin sharing data 3. Be able to justify your approach “for the method to obtain data from multiple sources, for gaining insight into a chosen problem, including analysis of data quality issues in the individual and group data” (Criterion 1) - draft this section in the template 4. Ensure you have a shared dataset in preparation for Mystery Box formative task; start to think about insights (criterion 2) 5. AT2a due, group status update, and your preliminary thoughts on analysis and external (ideally scholarly) resources you’re drawing on 6. Continue thinking about insights you might gain, visualisations you can use, issues (including ethical) with your data (criteria 2 and 3). Review sample assignments and the AT2 template. STUVAC HERE 7. Focus on issues with your data (including ethical) (criteria 1-3) and their implications for the practice of data science (criterion 4) 8. Continue from week 7, with a particular focus on how comparing across the levels of data (individual, group, cohort) provides insights. Ensure you have considered the privacy and ethical issues throughout your report, and the implications of the project for the practice of data science 9. Week 9, draft submission of AT2b. 10. Week 10, review colleague’s AT2b and continue work on your own final submission 11. Week 11, review colleague’s AT2b and continue work on your own final submission 12. Week 12 AT2b - feedback due. You should use that feedback to reflect on how to improve for your final submission STUVAC 13. Final assessment period, AT2c due How to write and submit AT2 Data scientists don’t just use Word and Powerpoint to write. They also write live reports, that draw on real-time data to show visualisations alongside narratives. These can draw on databases of data, to allow us to write text and do reproducible analysis of data for insights. One of the key tools to do this is the ‘notebook’ file. For AT2, we want you to use RStudio to write and submit your report. We know this will be unfamiliar for many of you, but that’s ok. We’re not asking you to learn to code. We’ve provided a template, and if you want, you can simply modify the example ‘markdown’ to format your own report, and load visualisations that you’ve created in other tools (like Excel, or Tableau). Some of you will want to go further, and that’s ok too! But remember to address the assessment criteria - this isn’t an assignment where you have to demonstrate technical coding skills. To submit, you will: 1. Complete self assessment on REVIEW https://uts.review-edu.com/uts 2. Use the RMarkdown template to ‘knit’ a pdf/html file 3. Upload to Canvas: 1. The HTML or PDF file - it may be necessary to zip this 2. The raw .Rmd - it may be necessary to zip this You may also wish to share these on github or rpubs.com - however, consider the privacy implications of doing so first.
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